Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters

Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters Anis Izzati Md Yusoff, Faiz Mohd Turan, Nur Qurratul Ain Adanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4820100/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Injection moulding is a widely used method for manufacturing plastic components, with the quality of the final product depending on various process factors managed throughout the procedure. Integrating sustainable manufacturing practices is crucial for mitigating ecological impacts while maintaining product excellence. Manufacturers need to balance product quality, procedural effectiveness, and environmental impact by evaluating how each parameter affects the product's quality and ecological footprint. While many focus on optimising process parameters, fewer consider integrating sustainability competency, which also affects parameter performance. This study aims to advance understanding by conducting experiments and analyses on these factors' influence on product quality. The incorporation of sustainability competency aims to empower individuals and entities to make informed choices that align with environmental, societal, and economic factors for a more sustainable and accountable future. The optimised model, with an error of less than 1%, quantifies the competency value bridging mechanical properties and comprehensive competency by integrating attitudinal factors. Parameter selection through Design of Experiments (DOE) and expert elicitation method contribute to this integration. Evolution from the foundational to the proficient model includes operational team and sustainability competency descriptors, providing context for innovation and knowledge creation highly valued by employers and stakeholders in a productive and streamlined setting. Additionally, this research contributes to the advancement of smart grid and sustainable energy applications by promoting energy-efficient manufacturing processes. By integrating renewable energy sources and smart grid technologies, the injection moulding industry can achieve significant reductions in energy consumption and greenhouse gas emissions. This integration not only enhances the sustainability of manufacturing processes but also supports the broader transition to a more resilient and eco-friendly energy system. Sustainable Manufacturing Injection Moulding Energy Efficiency Sustainability Integration Process Parameter Optimisation Figures Figure 1 1.0 INTRODUCTION In today's manufacturing landscape, sustainability has become crucial, particularly in injection moulding, where sustainability competency integrates environmentally friendly practices, economic viability, and social responsibility. This involves understanding sustainable materials, processes, and design strategies to minimise environmental impact while maintaining efficiency and product quality (Czepiel et al., 2023 ; Gholami et al., 2021 ; Nguyen et al., 2024 ; Selamat et al., 2017 ). Key areas of sustainability competency in injection moulding include material selection, process optimisation, waste reduction, lifecycle assessment, design for sustainability, regulatory compliance, continuous improvement, and effective communication. This competency not only addresses environmental challenges but also serves as a proactive strategy for long-term success (Cao et al., 2023 ; Turan, Johan, & Nor, 2016 ; Vieira et al., 2021 ). Meanwhile, research trends in sustainability competency encompass interdisciplinary approaches, education and training methods, assessment tools development, behavioural psychology insights, technology integration, corporate social responsibility, circular economy principles, policy influence, social equity considerations, supply chain management, stakeholder engagement, and resilience against environmental changes (D. O. Aikhuele & Turan, 2016 ; Sahimi et al., 2017 ; Turan & Johan, 2016 ). It's important to note that the field is dynamic, and staying updated on recent research through academic sources is advisable. Several research studies have delved into improving the mechanical properties and production processes of plastic parts through the use of the Taguchi method (Adanan, Mohd Turan, Johan, Md Yusoff, & Yee, 2022; Zhu et al., 2021 ). For instance, investigations by various researchers focused on optimising parameters like melting temperature, injection pressure, and cooling time to enhance the strength and quality of plastic products (Mehat & Kamaruddin, 2011a ). By carefully adjusting these factors, researchers achieved significant improvements in tensile, compressive, and flexural strengths of plastic trays, containers, and other items made from recycled plastics, demonstrating the feasibility of substituting recycled materials for virgin plastics. Integration of the Taguchi method with simulation software like Mould-Flow further enhanced the process optimisation, enabling engineers to identify optimal parameters and improve energy efficiency and product quality (Moayyedian et al., 2021 ; Wen et al., 2014 ). Additionally, studies explored the Taguchi method's applicability in minimising defects like weld lines and shrinkage, leading to enhanced quality and dimensional accuracy of plastic parts, which is crucial in industries requiring tight tolerances (Adanan, Mohd Turan, Johan, Md Yusoff, & Xin, 2022). Furthermore, researchers investigated the Taguchi method's effectiveness in optimising the mechanical properties of new composites, including bio composites and recycled composites, highlighting the potential for sustainable practices by utilising recycled materials and improving composite performance through optimised processing conditions (Haniel et al., 2023). By systematically analysing the impact of various factors on material properties, researchers identified optimal process parameters to achieve desired outcomes, such as improved impact strength and dimensional stability (Chauhan et al., 2021 ; W.-C. Chen et al., 2016 ). Additionally, the Taguchi method facilitated the minimisation of part weight and reduction of defects like short shots, further enhancing the efficiency and reliability of injection moulding processes (Panneerselvam & Turan, 2020 ). Overall, the Taguchi method emerged as a valuable tool for optimising injection moulding processes, enabling improved mechanical properties, reduced defects, and enhanced quality of plastic parts, thereby contributing to sustainable practices and efficient production processes in the plastic industry (Panneerselvam & Turan, 2021 ). The excerpt emphasises the importance of sustainability practices and the benefits they bring. It highlights that sustainability practices can enhance brand value, attract customers, employees, and investors, reduce costs, improve business resilience, and create new opportunities for market expansion. The article also discusses the need for comprehensive approaches and frameworks, as well as the use of indicators to measure sustainable development. It mentions the importance of integrating knowledge and consciousness levels in the decision-making process to achieve sustainable development goals effectively. In the context of Malaysia, the article acknowledges the country's involvement in the development of the Sustainable Development Goals (SDGs) and the alignment of the SDGs with the national agenda. However, it also mentions the need for comprehensive approaches, frameworks, and indicators to address the challenges in interpreting sustainable development and setting indicators in Malaysia. Overall, the article emphasises the importance of integrating sustainable business practices, knowledge, consciousness, and comprehensive approaches to achieve sustainable development goals and ensure the success of businesses in Malaysia (Adanan et al., 2021 ; Turan, Johan, & Abu Sofian, 2018 ). Sustainable manufacturing involves employing technologies and practices that adhere to sustainability principles across economic, environmental, and social aspects. It encompasses various outcomes, including prioritizing eco-friendliness to reduce environmental impact, optimising resource use to lower costs and product prices, minimising energy consumption for cost savings and environmental preservation, reducing waste generation for efficient resource utilisation and pollution reduction, prioritising safety in operations to prevent accidents, and enhancing worker health by improving workplace conditions (Sahimi et al., 2018 ). Meeting the growing demand for consumer products while ensuring sustainability aligns with environmental responsibility and long-term economic competitiveness. Efforts to reduce energy consumption and increase renewable energy usage are crucial, considering that a significant portion of global energy demand and CO 2 emissions stem from manufacturing activities. Achieving sustainability involves comprehensive approaches at both the factory and process levels, including data-driven strategies, technical modifications to conserve energy and resources, and adopting energy-efficient technologies as alternatives to traditional methods (D.-C. Chen et al., 2024 ; Farbodi, 2017 ; Mehat & Kamaruddin, 2011b ; Turan, Johan, & Omar, 2018 ). This research study concentrates on exploring parameters related to knowledge, attitude, and psychological aspects within the context of sustainability. The work of Ramdas & Mohamed [26] provides a suitable reference, as it delves into the interconnectedness between knowledge, attitude, and behavioural intention, drawing insights from the theory of action. This theory suggests that individuals' decisions to engage in certain behaviours are influenced by their perceptions of sustainability practices rather than solely by extreme desires or unconscious motives. The research underscores the importance of quantitatively measuring knowledge while also recognizing the qualitative aspects of attitudes towards best practices. The theoretical framework presented by Ramdas & Mohamed integrates tangible elements like knowledge with intangible factors such as consciousness or attitudes, which serve as motivators for adopting sustainable business practices. Moreover, the theory posits a correlation between willingness to participate in sustainability efforts and factors like knowledge, awareness, attitude, or consciousness, emphasising the interdependence of these elements in fostering sustainable behaviours. Considering the focus on knowledge, attitude, and psychological aspects in this research, the findings from Ramdas & Mohamed (Ramdas & Mohamed, 2014 ) offer valuable insights and support for the study's objectives, ensuring relevance and accuracy in the analysis and findings. The literature review underscores various factors influencing the quality of plastic parts manufactured through injection moulding, including material selection, design, and processing parameters. Researchers have focused on minimising defects using the Taguchi method, particularly addressing issues like warpage and shrinkage. Processing parameters have been extensively studied for their impact on part quality, often integrated with simulation packages to optimise their influence. Some studies explore how the Taguchi method enhances mechanical properties, aiming to improve composite material performance. However, this research aims to advance beyond single characteristic optimisation by combining quality characteristics using a composite desirability function, allowing for a more comprehensive assessment of part quality. Expert elicitation proves useful in capturing implicit judgments, particularly in scenarios lacking empirical data, offering valuable insights for decision-making and modelling. Integrating sustainability into the injection moulding optimisation process faces challenges like technical complexity and data availability, requiring a collaborative effort to address these limitations effectively. This study focuses on the challenges in incorporating recycled materials in plastic manufacturing, particularly in injection moulding, due to the degradation of mechanical properties compared to virgin materials. While injection moulding parameters significantly impact product quality, the current approach often relies on trial and error rather than systematic optimisation, leading to material wastage. Integrating sustainability into this optimisation process faces challenges like technical complexity and data availability. This research aims to investigate how injection moulding parameters affect mechanical properties, optimise these parameters using the Taguchi method, and characterise them with sustainability competency. It emphasises thermoplastic polypropylene (PP) and excludes thermosets, focusing on tensile strength and flexural modulus for both virgin and recycled plastics. The study will utilise the Taguchi method and the desirability function to enhance part quality while considering sustainability principles, aiming to contribute to a more responsible manufacturing future. In this study, the attitudinal parameter serves as a mathematical representation of the emotional inclinations of design stakeholders or decision-makers, acknowledging that attitudes significantly shape behaviour and decision-making processes. (D. Aikhuele & Turan, 2018 ; Turan, Johan, Lanang, et al., 2016; Wan Lanang et al., 2017 ) demonstrate that knowledge levels influence awareness and attitudes, impacting both willingness to pay and environmental literacy. Supported by (Ayasrah & Mohd Turan, 2022 ), the theory of reasoned action suggests a link between beliefs, attitudes, intentions, and behaviours. Attitudes can be influenced by various factors such as environmental concerns, personal values, and motivations, as highlighted by (D. O. Aikhuele & Turan, 2018 ). (Ayasrah et al., 2024 ) propose an attitudinal parameter (𝜆) to capture decision makers' risk attitudes, enabling classification as risk-averse, risk-neutral, or risk-seeking. This parameter facilitates a deeper understanding of decision makers' emotional disposition and its influence on decision-making processes, ultimately impacting sustainability practices. Additionally, expert elicitation proves valuable in capturing implicit judgments, particularly in fields where expert knowledge shapes probability distributions for uncertain variables. This method allows for the incorporation of expert opinions into decision-making analyses, especially in scenarios where empirical data is lacking or traditional data collection methods are impractical. Following a well-defined protocol during the elicitation process ensures that judgments are based on evidence and personal experience, providing scientifically informed insights for decision-making and modelling purposes across various fields. Additionally, embedding sustainable manufacturing practices into the optimisation process will ensure the efficient utilisation of resources and minimise environmental impact, aligning with the broader goal of sustainability in the plastic industry. 2.0 METHODOLOGY 2.1 RESEARCH FRAMEWORK This research is divided into three main phases: selecting process parameters and quality characteristics, optimizing process parameters, and establishing quantitative relationships, and integrating sustainability competency into characterising process parameters. The first part explains how variables are chosen, including process parameters and quality characteristics. Then, it discusses how optimisation is done in two steps: first, through Taguchi experiments and mechanical testing, followed by analysis using the S/N ratio method; second, through the desirability function and verification studies. Next, it outlines how to establish a quantitative relationship between process parameters and quality characteristics. Finally, it explores how sustainability competency is incorporated into characterising injection moulding process parameters. Figure 2.1 shows the proposed framework visually, outlining the structure of the research. 2.2 PHASE 1: PROCESS PARAMETER AND QUALITY CHARACTERISTIC SELECTION In the first phase of this research, emphasis is placed on the selection of quality characteristics and process parameters essential for injection moulding. Quality characteristics, such as dimensional properties, surface appearance, and mechanical strength, play a critical role in evaluating part effectiveness. Based on an extensive literature review, the primary focus is on assessing the tensile strength and flexural modulus of recycled polypropylene parts. Through thorough research, eight key parameters have been identified: melt temperature, injection pressure, injection speed, injection time, holding pressure, holding time, cooling time, and mould temperature (held constant in this study). Three levels are selected for each factor to enable better parameter analysis and optimization. These parameters are chosen based on industrial standards and preliminary testing to ensure the production of parts with acceptable dimensional properties and mechanical strength. The values of these parameters are detailed in Table 2.1 , excluding the mould temperature parameter, which remains constant throughout the study. Table 2.1 SELECTED PROCESS PARAMETERS Factors Process Parameters Level 1 Level 2 Level 3 A Melt temperature, ℃ 180 220 260 B Injection pressure, MPa 45 50 55 C Injection speed, mm/s 20 25 30 D Injection time, Sec 6 7 8 E Holding pressure, MPa 20 35 50 F Holding time, Sec 1 2 3 G Cooling time, Sec 15 20 25 2.3 PHASE 2: OPTIMISING PROCESS PARAMETERS In the second phase of the research, the focus shifts to optimising the injection moulding process parameters using the Taguchi optimisation method. When multiple responses share similar optimal characteristics, an overall desirability function becomes crucial. This function transforms each response into a dimensionless value, indicating its desirability. After determining individual desirability functions for each response, they are combined into an overall desirability function using a relative weight scale. Regression analysis is then utilised to establish quantitative relationships between product quality and process parameters. This analysis explains the connection between dependent and independent variables, with linear regression being the preferred method. Coefficients in the regression model indicate the extent and direction of the impact of independent variables on the dependent variable. The R-squared value measures the proportion of variability in the dependent variable explained by independent variables, while the P-value indicates the significance of these correlations. Additionally, the standard error of the coefficient provides insight into the precision of coefficient estimates, with smaller values indicating higher precision. 2.4 PHASE 3: CHARACTERISING PROCESS PARAMETERS In the third phase, the focus is on characterising process parameters, specifically by incorporating sustainability competency into the model derived from the optimization phase. This collaborative equation, known as the basic model for forecasting sustainability competency value, integrates correlations between injection moulding parameters. Attitude is identified as a crucial factor in this integration, particularly within the demographic context of industry executives and non-executives, which are categorised into operational teams. The qualitative responses from survey questions were transformed into quantitative data using the attitudinal theory, as demonstrated in Table 2.2 , aligning with a common practice among previous researchers (Ahmed Shaikh, 2016; Jiménez & San-Martín, 2017; Turan et al., 2017). Table 2.2 THE INTERPRETATION FROM SCALE TO ATTITUDINAL Scale Value factor Attitudinal response Yes 1 Risk averse No -1 Risk seeking Neutral 0 Neutral The incorporation of job design and sustainability competency factors further enhances the model, emphasising knowledge and behaviour within the broader framework of sustainability competency. Functional attributes, termed sustainability competency descriptors, are outlined to encapsulate the dimensions of sustainability competency within different roles. These descriptors are comprehensively outlined in Table 2.3 , encapsulating the intricate dimensions of sustainability competency within the context of the staff's role. Additionally, survey distribution and data collection methods are discussed, including the distribution of surveys via postal methods and the subsequent organisation and analysis of collected data. The conversion of qualitative responses into quantitative data, expert elicitation, and regression analysis for sustainability competency value are also described in detail, emphasising the comprehensive approach employed in this phase. Table 2.3 JOB DESIGN BASED ON SUSTAINABILITY COMPETENCY DESCRIPTORS Job design Sustainability competency descriptors Day shift Night shift Executive W1 W4 Non-executive W2 W3 3.0 RESULTS 3.1 EXPERIMENTAL RESULTS The data obtained from both tensile testing and flexural testing of all the specimens were organised into a tabular format. The average values derived from these data sets were then utilised to analyse the mechanical properties exhibited by the recycled material based on the settings of the process parameters. The results for the tensile strength and flexural modulus of each specimen are available in Tables 3.1 and 3.2. Table 3.1 TENSILE STRENGTH RESULTS Trial Test 1 (kgf/cm 2 ) Test 2 (kgf/cm 2 ) Test 3 (kgf/cm 2 ) Average (kgf/cm 2 ) 1 194.86 195.38 191.90 194.05 2 192.93 188.49 191.87 191.10 3 193.89 196.38 195.47 195.25 4 193.00 193.38 193.13 193.17 5 197.71 198.09 195.99 197.26 6 195.90 198.14 195.30 196.45 7 196.23 196.64 195.75 196.21 8 195.88 196.43 196.98 196.43 9 194.77 194.19 195.44 194.80 10 188.69 190.27 186.74 188.57 11 189.20 191.29 186.28 188.92 12 188.46 189.06 190.80 189.44 13 187.70 185.94 184.39 186.01 14 188.09 187.55 189.59 188.41 15 183.21 180.04 183.81 182.35 16 191.39 191.56 189.97 190.97 17 188.86 190.54 189.85 189.75 18 189.92 188.07 187.46 188.48 19 170.36 171.97 171.71 171.35 20 165.45 163.13 166.47 165.01 21 174.70 178.74 173.74 175.72 22 172.59 172.94 172.02 172.52 23 169.45 167.34 168.67 156.48 24 170.90 171.40 169.56 170.62 25 166.57 166.43 168.24 167.08 26 156.48 154.60 154.90 155.33 27 164.69 166.06 166.50 165.75 Table 3.2 FLEXURAL MODULUS RESULTS Trial Test 1 (kgf/cm 2 ) Test 2 (kgf/cm 2 ) Test 3 (kgf/cm 2 ) Average (kgf/cm 2 ) 1 9770.61 9611.09 9339.32 9573.67 2 10028.71 9843.2 9740.2 9870.7 3 9815.81 9818.07 9515.52 9716.47 4 10052.5 9269.43 9617.57 9646.5 5 9891.04 9979.72 9756.89 9875.88 6 9573.03 9532.09 9535.96 9547.03 7 9554.02 9827.06 9768.56 9716.55 8 9922.6 10181.62 10166.62 10090.28 9 10076.53 10141.78 10031.23 10083.18 10 10200.89 10067.14 9715.51 9994.51 11 9653.35 9742.88 9366.99 9587.74 12 9281.37 9405.2 9026.06 9238.54 13 9802.49 9482.05 9859.92 9714.82 14 9813.99 9643.14 9453.32 9636.82 15 9762.54 9712.03 10054.24 9842.94 16 9627.91 9827.47 9602.13 9685.84 17 9608.54 9944.02 9794.04 9782.2 18 9776.11 9634.19 9580.49 9663.6 19 9174.61 9372.79 9353.5 9313.63 20 9349.34 9118.98 9352.78 9273.7 21 9267.48 9302.7 9049.11 9206.43 22 9802.11 9601.5 9753.76 9719.12 23 9137.07 9381.04 9064.62 9194.24 24 9901.27 9369.47 9398.59 9556.44 25 9377.89 9171.33 9337.98 9295.73 26 8968.19 9327.65 9203.26 9166.37 27 9449.53 9636.18 9490.64 9525.45 Tables 3.3 and 3.4 display rankings of process parameters' impact on tensile strength and flexural modulus, using signal-to-noise ratios. The delta value, calculated from highest and lowest averages for each factor, measures effect size. Melt temperature ranks highest for both qualities, followed by other factors like injection time and holding time. Injection pressure ranks lowest. For flexural modulus, the same trend is observed, with melt temperature being most influential and holding pressure least. Table 3.3 RESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF TENSILE STRENGTH Level A B C D E F G 1 45.80 45.31 45.31 45.11 45.30 45.15 45.32 2 45.49 45.28 45.20 45.34 45.19 45.32 45.28 3 44.50 45.21 45.28 45.34 45.30 45.33 45.19 Delta 1.30 0.10 0.11 0.24 0.11 0.19 0.13 Rank 1 7 5 2 6 3 4 Table 3.4 RESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF FLEXURAL MODULUS Level A B C D E F G 1 79.82 79.58 79.68 79.64 79.67 79.53 79.63 2 79.72 79.68 79.59 79.60 79.65 79.75 79.69 3 79.43 79.70 79.69 79.72 79.64 79.67 79.64 Delta 0.39 0.12 0.10 0.12 0.03 0.22 0.07 Rank 1 3 5 4 7 2 6 3.2 PARAMETERS OPTIMISATION After individually optimizing tensile strength and flexural modulus, both responses are optimised together using desirability functions. Each response is treated as an objective function, with specific process parameters identified for maximum tensile strength and flexural modulus. For example, a melt temperature of 180°C, a holding time of 3 seconds, and other parameters yield a tensile strength of 203.82 kgf/cm². Similarly, optimal parameters for maximum flexural modulus include a melt temperature of 180°C and others, resulting in a modulus of 10005 kgf/cm². However, optimising each response separately may lead to different optimal parameters. To address this, a composite desirability function method is used to find a single optimal value that improves all responses together. The outcomes show optimal parameter values where both tensile strength and flexural modulus have the same desirability value. These results are summarised in Table 3.5 . Table 3.5 RESULTS OF MULTI RESPONSE OPTIMISATION Responses Factors Predicted responses (kgf/cm 2 ) Desirability value (kgf/cm 2 ) A B C D E F G Tensile strength 180 55 30 8 20 3 25 199 0.9767 Flexural modulus 180 55 30 8 20 3 25 10005 0.9767 A confirmation test serves to validate the research outcomes by ensuring that the optimised process parameters indeed produce the expected quality characteristics. Despite challenges in achieving a perfect match due to repeatability factors, the confirmation experiment was conducted using the established optimal parameters to assess any differences between these parameters and the actual manufacturing process. Maintaining the same material properties as in the initial experiment helped mitigate variations. Three specimens were manufactured using the optimised parameters, followed by tensile and flexural tests. The results were compared with the anticipated values, showing a close match as shown in Table 3.6 . For instance, the anticipated and observed tensile strengths were 199 kgf/cm² and 197 kgf/cm² respectively, with a percentage error of 1%. Similarly, the flexural modulus values showed a minimal error of 0.03%. This confirmation experiment underscores the validity of the optimisation approach, affirming the reliability and accuracy of the optimized process parameters in achieving the desired quality characteristics. Table 3.6 CONFIRMATION TEST OF OPTIMISED PARAMETERS Responses Predicted results (kgf/cm 2 ) Confirmation results (kgf/cm 2 ) Error (%) Tensile strength 199 197 1 Flexural modulus 10005 10002 0.03 3.2 PARAMETERS OPTIMISATION Regression analysis is a statistical method used to explore the relationship between variables. In multiple linear regression, the R-square value, typically between 0.8 and 1, measures how well the model predicts future outcomes based on existing data. It indicates the accuracy of the model in forecasting results. In this study, regression was used to model the relationship between process parameters and quality attributes. Tables 3.7 and 3.8 show the coefficients for each injection moulding parameter, detailing their impact on tensile strength and flexural modulus respectively. Table 3.7 COEFFICIENT OF PREDICTION MODEL FOR TENSILE STRENGTH Term Coefficient SE Coefficient T-Value P-Value VIF Constant 253.3 17.0 14.89 0.000 Melt temperature -0.3373 0.0280 -12.04 0.000 1.00 Injection pressure -0.173 0.224 -0.77 0.449 1.00 Injection speed -0.055 0.224 -0.24 0.810 1.00 Injection time 2.31 1.12 2.07 0.053 1.00 Holding pressure -0.0039 0.0747 -0.05 0.959 1.00 Holding time 1.83 1.12 1.64 0.118 1.00 Cooling time -0.253 0.224 -1.13 0.274 1.00 Table 3.8 COEFFICIENT OF PREDICTION MODEL FOR FLEXURAL MODULUS Term Coefficient SE Coefficient T-Value P-Value VIF Constant 9603 735 13.07 0.000 Melt temperature -5.37 1.21 -4.44 0.000 1.00 Injection pressure 13.71 9.69 1.42 0.173 1.00 Injection speed 1.29 9.69 0.13 0.895 1.00 Injection time 46.5 48.4 0.96 0.349 1.00 Holding pressure -1.04 3.23 -0.32 0.751 1.00 Holding time 75.9 48.4 1.57 0.134 1.00 Cooling time 1.60 9.69 0.17 0.870 1.00 A mathematical model derived from regression analysis needs validation to ensure its accuracy in real manufacturing processes. To confirm its reliability, an actual experiment mirroring previous setups was conducted, and mechanical tests were carried out accordingly. Table 3.9 shows the results of this verification test. The model predicted tensile strength and flexural modulus as 197.19 kgf/cm² and 9827.05 kgf/cm² respectively, while the confirmed values were 197.26 kgf/cm² and 9875.88 kgf/cm². The error percentages for tensile strength and flexural modulus were calculated at 0.03% and 0.4% respectively, both below 2%. These validation outcomes underscore the practicality and effectiveness of the regression model for both responses. Table 3.9 CONFIRMATION TEST OF REGRESSION MODEL Responses Factors Predicted results (kgf/cm 2 ) Confirmation results (kgf/cm 2 ) Error (%) A B C D E F G Tensile strength 180 50 25 7 35 2 20 197.19 197.26 0.03 Flexural modulus 180 50 25 7 35 2 20 9827.05 9875.88 0.4 3.3 INCORPORATING SUSTAINABILITY COMPETENCY The model developed in Phase 2 lacks the comprehensive competency needed to foster innovation due to its oversight of staff roles and behavioural elements like trust and honesty crucial for effective knowledge creation. To address this, a sustainability competency definition is introduced to incorporate staff roles and attitudinal factors. This enhancement aims to predict competency value more accurately by integrating critical elements like "Knowledge" and "Behaviour" into the equation using feedback from questionnaires. These elements are assigned significant weightages, as shown in Table 3.10 , to better capture their impact on the knowledge creation process, thus improving the equation's predictive capacity. Table 3.10 JOB DESIGN WEIGHTAGE FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR Job design Weightage Knowledge Behavioural Executive 0.49 0.52 Non-executive 0.47 0.67 The job design has been characterised by integrating staff functions with the sustainability competency definition, including knowledge and behaviour. This integration is represented by predictive equations in Tables 3.11 and 3.12, forming a comprehensive model that captures the relationship between staff roles, attitudes, and job design. These appendices offer a thorough examination of regression results and their connection to knowledge and behaviour factors. Table 3.11 CHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (TENSILE STRENGTH) Phase Operational team Characteristic (predictive) model Before characterised - TS = 253.3–0.3373 A − 0.173 B- 0.055 C + 2.31 D − 0.0039 E + 1.83 F − 0.253 G After characterised W1 Wk1 = 116.72–0.1567 A − 0.0516 B + 0.0058 C + 0.9288 D + 0.0074 E + 0.8097 F − 0.0990 G Wb1 = 129.14–0.1733 A - 0.0570 B + 0.0060 C + 1.0280 D + 0.0081 E + 0.8960 F - 0.1090 G W2 Wk2 = 125.15–0.1660 A - 0.0749 B - 0.0457 C + 1.0276 D - 0.0090 E + 0.6307 F - 0.0915 G Wb2 = 166.39–0.2234 A - 0.0736 B + 0.0083 C + 1.3240 D + 0.0105 E + 1.1543 F – 0.1411 G W3 Wk3 = 119.91–0.1596 A - 0.1054 B + 0.0390 C + 1.2713 D - 0.0042 E + 1.0912 F - 0.1537 G Wb3 = 170.94–0.2275 A - 0.1503 B - 0.0556 C + 1.8123 D - 0.0060 E + 1.5555 F – 0.2191 G Wk = knowledge, Wb = behaviour, TS = tensile strength Table 3.12 CHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (FLEXURAL MODULUS) Phase Operational team Characteristic (predictive) model Before characterised - FM = 9603 − 5.37 A + 13.71 B + 1.29 C + 46.5 D − 1.04 E + 75.9 F + 1.60 G After characterised W1 Wk1 = 4339 − 2.56 A + 7.36 B − 1.30 C + 38.46 D + 0.56 E + 43.89 F + 1.83 G Wb1 = 6186–3.65 A -3.65 B − 1.85 C + 54.80 D + 0.80 E + 62.60 F + 2.61 G W2 Wk2 = 4025 − 2.26 A + 13.14 B + 6.79 C + 7.77 D − 1.16 E + 29.65 F + 3.15 G Wb2 = 5738–3.23 A + 18.73 B + 9.68 C + 11.10 D − 1.65 E + 42.30 F + 4.49 G W3 Wk3 = 5395 − 2.90 A − 0.99 B – 4.05 C + 20.13 D − 0.83 E + 33.87 F − 2.83 G Wb3 = 5726 − 3.08 A − 1.04 B − 4.30 C + 21.4 D − 0.88 E + 35.9 F − 3.00 G Wk = knowledge, Wb = behaviour, FM = flexural modulus The R-squared (R²) coefficient measures how well a regression model fits the data. It indicates how accurately the predictors within the model explain the response. Table 3.13 and 3.14 show the R² values for the tensile strength and flexural modulus models respectively. Across all models, whether before or after characterisation, those incorporating sustainability competency definitions and attitudinal aspects demonstrate high regression coefficients. This suggests they explain a significant portion of the variability in competency dimensions. The values from the characterized model highlight the strength of competency attributed to integration, where staff attitudes toward knowledge creation interact with mechanical properties based on optimized parameters established in Phase 2. Table 3.13 R-SQUARED (R²) ASSESSMENT RESULT FOR ALL TENSILE STRENGTH Job design Operational team Before characterisation After characterisation Knowledge Behaviour R² R² (adj) R² R² (adj) R² R² (adj) Executive W1 89.00% 85.00% 89.03% 84.99% 89.03% 84.99% Non-executive W2 86.58% 82.56% 88.25% 84.72% W3 85.59% 81.27% 86.49% 82.43% Table 3.14 R-SQUARED (R²) ASSESSMENT RESULT FOR ALL FLEXURAL MODULUS Job design Operational team Before characterisation After characterisation Knowledge Behaviour R² R² (adj) R² R² (adj) R² R² (adj) Executive W1 57.00% 57.00% 56.30% 40.19% 56.30% 40.19% Non-executive W2 52.69% 35.26% 52.69% 35.26% W3 45.70% 25.69% 45.70% 25.69% 3.4 SUMMARY The experiment followed the Taguchi design, evaluating mechanical properties like tensile strength and flexural modulus, documented in Tables 3.1 and 3.2. Signal-to-noise ratio analysis identified key injection moulding factors affecting recycled polypropylene. Melt temperature was most influential for tensile strength, followed by injection and holding time, while for flexural modulus, it was melting temperature, holding time, and injection pressure. Optimizing both properties simultaneously resulted in 199kgf/cm² tensile strength and 10005 kgf/cm² flexural modulus with parameters like 180°C melt temperature and 8s injection time. A confirmation test validated these parameters with error (%) values of 1% for tensile strength and 0.03% for flexural modulus. Regression analysis developed a mathematical model showing 0.03% and 0.4% error (%) for tensile strength and flexural modulus, affirming model reliability. The characterisation of operational teams for knowledge and behaviour found no significant differences between models before and after, validating the sustainability competency model for self-assessment of knowledge creation. 4.0 CONCLUSIONS This research explores the relationship between job design and attitudes using predictive models with integrated algorithms. By analysing data from experiments and questionnaires on process parameters and competency, the study investigates how job design, operational teams, and sustainability competency descriptors interact in injection moulding investigations. These correlations help predict overall competency performance, symbolised as competency value distributions. The characterised model generates metrics for competency performance, termed sustainability competency, capturing the interaction between job design, operational teams, sustainability competency descriptors, and other factors. Additionally, a mathematical model is developed through regression analysis to understand the relationship between injection moulding processes and mechanical properties. The regression analysis shows strong correlations between process parameters and attitudinal elements, enhancing the competency model. Finally, the study's main contributions include identifying optimal injection moulding parameters, simultaneously optimising quality characteristics, and refining the competency model by incorporating attitudinal elements, all essential for achieving competitiveness and innovation in the plastic industrial sector. This research focuses on optimising the production of recycled materials to improve their strength using Taguchi experimental design and the desirability function approach. Future studies should explore additional aspects of material strength, such as impact and compression, and test different batches of recycled materials for consistency. Advanced techniques like scanning electron microscopy can provide more precise insights into material properties. Understanding how various factors like temperature and machine settings influence the final product is essential, along with investigating the effects of mixing recycled materials with new ones and additives. The newly developed model helps predict how well the manufacturing process integrates sustainability, which is crucial for industry growth and ensuring quality standards. Declarations Ethical Approval Not applicable. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors' contributions The authors, Anis Izzati Md Yusoff, Faiz Mohd Turan, and Nur Qurratul Ain Adanan carried out the conceptualisation, drafting of the research work, and editing of the final article. All authors approved the submitted version. Funding This research was funded by a grant from Universiti Malaysia Pahang Al-Sultan Abdullah (RDU233016). Availability of data and materials Not applicable References Adanan, N. Q. A., Mohd Turan, F., Johan, K., Md Yusoff, A. I., & Xin, W. H. (2022). Optimising Casting Film Parameters for LPDE Material Assessment (pp. 67–74). https://doi.org/10.1007/978-981-19-2890-1_7 Adanan, N. Q. A., Mohd Turan, F., Johan, K., Md Yusoff, A. I., & Yee, Y. W. (2022). Performance of Assessment Model for Injection Moulding Parameters (pp. 59–65). https://doi.org/10.1007/978-981-19-2890-1_6 Adanan, N. Q. A., Turan, F. M., & Johan, K. (2021). Industrial Sustainability Policy and Standards-Related on Management Discipline of SMEs Industry in Malaysia: A Conceptual Framework. In Lecture Notes in Mechanical Engineering (Vol. 46). https://doi.org/10.1007/978-981-15-9505-9_3 Aikhuele, D. O., & Turan, F. M. (2016). A Hybrid Fuzzy Model for Lean Product Development Performance Measurement. 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An Integrated TOPSIS Model with Exponential Intuitionistic Entropy Measure for Multi-Attribute Decision-Making (MADM) (pp. 59–69). https://doi.org/10.1007/978-981-99-9848-7_6 Cao, Y., Fan, X., Guo, Y., Ding, W., Liu, X., & Li, C. (2023). Multi-objective optimization of injection molding process parameters based on BO-RFR and NSGAⅡ methods. International Polymer Processing, 38(1), 8–18. https://doi.org/10.1515/ipp-2020-4063 Chauhan, V., Kärki, T., & Varis, J. (2021). Optimization of Compression Molding Process Parameters for NFPC Manufacturing Using Taguchi Design of Experiment and Moldflow Analysis. Processes, 9(10), 1853. https://doi.org/10.3390/pr9101853 Chen, D.-C., Chen, D.-F., & Huang, S.-M. (2024). Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality. Polymers, 16(8), 1113. https://doi.org/10.3390/polym16081113 Chen, W.-C., Nguyen, M.-H., Chiu, W.-H., Chen, T.-N., & Tai, P.-H. (2016). 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Optimization of Characteristics Polymer Composite Reinforced Kenaf and Jute Fiber Using Taguchi-Response Surface Methodology Approach. Journal of Natural Fibers, 20(2). https://doi.org/10.1080/15440478.2023.2204453 Mehat, N. M., & Kamaruddin, S. (2011a). Investigating the Effects of Injection Molding Parameters on the Mechanical Properties of Recycled Plastic Parts Using the Taguchi Method. Materials and Manufacturing Processes, 26(2), 202–209. https://doi.org/10.1080/10426914.2010.529587 Mehat, N. M., & Kamaruddin, S. (2011b). Optimization of mechanical properties of recycled plastic products via optimal processing parameters using the Taguchi method. Journal of Materials Processing Technology, 211(12), 1989–1994. https://doi.org/10.1016/j.jmatprotec.2011.06.014 Moayyedian, M., Dinc, A., & Mamedov, A. (2021). Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques. Polymers, 13(23), 4158. https://doi.org/10.3390/polym13234158 Nguyen, D. T., Yu, E., Barry, C., & Chen, W.-T. (2024). Energy consumption variability in life cycle assessments of injection molding processes: A critical review and future outlooks. Journal of Cleaner Production, 452, 142229. https://doi.org/10.1016/j.jclepro.2024.142229 Panneerselvam, V., & Turan, F. M. (2020). Multi Response Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function (pp. 252–264). https://doi.org/10.1007/978-981-13-9539-0_26 Panneerselvam, V., & Turan, F. M. (2021). Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function (pp. 247–260). https://doi.org/10.1007/978-981-15-7309-5_24 Ramdas, M., & Mohamed, B. (2014). Impacts of Tourism on Environmental Attributes, Environmental Literacy and Willingness to Pay: A Conceptual and Theoretical Review. Procedia - Social and Behavioral Sciences, 144, 378–391. https://doi.org/10.1016/j.sbspro.2014.07.307 Sahimi, N. S., Turan, F. M., & Johan, K. (2017). Development of Sustainability Assessment Framework in Hydropower sector. IOP Conference Series: Materials Science and Engineering, 226, 012048. https://doi.org/10.1088/1757-899X/226/1/012048 Sahimi, N. S., Turan, F. M., & Johan, K. (2018). Framework of Sustainability Assessment (FSA) method for manufacturing industry in Malaysia. IOP Conference Series: Materials Science and Engineering, 342(1). https://doi.org/10.1088/1757-899X/342/1/012079 Selamat, S. N., Nor, N. H. M., Rashid, M. H. A., Ahmad, M. F., Mohamad, F., Ismail, A. E., Hassan, M. F., Turan, F. M., Mohd Zain, M. Z., Bakar, E. A., & Seiji, Y. (2017). Review of CO 2 Reduction Technologies using Mineral Carbonation of Iron and Steel Making Slag in Malaysia. Journal of Physics: Conference Series, 914, 012012. https://doi.org/10.1088/1742-6596/914/1/012012 Turan, F. M., & Johan, K. (2016). Assessing sustainability framework of automotiverelated industry in the malaysiacontext based on GPM P5 standard. ARPN Journal of Engineering and Applied Sciences, 11(12). Turan, F. M., Johan, K., & Abu Sofian, M. I. (2018). Development of Sustainability Assessment Tool for Malaysian hydropower industry: A case study. IOP Conference Series: Materials Science and Engineering, 342, 012009. https://doi.org/10.1088/1757-899X/342/1/012009 Turan, F. M., Johan, K., Lanang, W. N. S. W., & Nor, N. H. M. (2016). Development of Systematic Sustainability Assessment (SSA) for the Malaysian Industry. IOP Conference Series: Materials Science and Engineering, 160(1). https://doi.org/10.1088/1757-899X/160/1/012047 Turan, F. M., Johan, K., & Nor, N. H. M. (2016). Criteria Assessment Model for Sustainable Product Development. IOP Conference Series: Materials Science and Engineering, 160(1). https://doi.org/10.1088/1757-899X/160/1/012004 Turan, F. M., Johan, K., & Omar, N. A. (2018). Development of hydropower sustainability assessment method in Malaysia context. IOP Conference Series: Materials Science and Engineering, 319, 012006. https://doi.org/10.1088/1757-899X/319/1/012006 Vieira, A. L. N., Campilho, R. D. S. G., Silva, F. J. G., & Ferreira, L. P. (2021). Increasing the Environmental Sustainability of an Over-Injection Line for the Automotive Component Industry. Sustainability, 13(22), 12692. https://doi.org/10.3390/su132212692 Wan Lanang, W. N. S., Turan, F. M., & Johan, K. (2017). Systematic Assessment Through Mathematical Model for Sustainability Reporting in Malaysia Context. IOP Conference Series: Materials Science and Engineering, 226(1). https://doi.org/10.1088/1757-899X/226/1/012049 Wen, T., Chen, X., Yang, C., Liu, L., & Hao, L. (2014). Optimization of processing parameters for minimizing warpage of large thin-walled parts in whole stages of injection molding. Chinese Journal of Polymer Science, 32(11), 1535–1543. https://doi.org/10.1007/s10118-014-1541-7 Zhu, J., Qiu, Z., Huang, Y., & Huang, W. (2021). Overview of injection molding process optimization technology. Journal of Physics: Conference Series, 1798(1), 012042. https://doi.org/10.1088/1742-6596/1798/1/012042 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4820100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344688937,"identity":"742c811b-d00b-4d64-bbdb-99d0cf7ca65e","order_by":0,"name":"Anis Izzati Md Yusoff","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah","correspondingAuthor":false,"prefix":"","firstName":"Anis","middleName":"Izzati Md","lastName":"Yus","suffix":"Md"},{"id":344688938,"identity":"26e4dc20-5428-4e50-a424-551296af0a57","order_by":1,"name":"Faiz Mohd Turan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBAC9gbGBiBlA+MzE9bCcwCkJSGNJC0gMuEwKVrYDzd/uvnjfOLa2e0PPzBUWCc2iJ0xwK+FJ7HBOCfhduK2O2eMJRjOpCc2SOfg12LPkNiQDNZyI4dBgrHtMGEtPPwPGw7nJJwDakl//IPxHzFaJBIbm3MSDgC1JJhJMDYQpeVhM3NOWrIx0C9mFgnH0o3bpNMKCDgs/fHnHBs72W232x/f+FBjLdsvnbwBrxYEkADiBCBmY+DA7zBULRDA/oBILaNgFIyCUTBCAADFBktVmOEQuwAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah","correspondingAuthor":true,"prefix":"","firstName":"Faiz","middleName":"Mohd","lastName":"Turan","suffix":""},{"id":344688939,"identity":"3726c886-90e8-4397-87ca-1b0c11330ac5","order_by":2,"name":"Nur Qurratul Ain Adanan","email":"","orcid":"","institution":"Universiti Malaysia Pahang Al-Sultan Abdullah","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"Qurratul Ain","lastName":"Adanan","suffix":""}],"badges":[],"createdAt":"2024-07-29 07:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4820100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4820100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63290027,"identity":"7cf033a1-26f1-4cac-81ed-1aecbae06483","added_by":"auto","created_at":"2024-08-26 14:08:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRESEARCH FRAMEWORK\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4820100/v1/ea95543a58dbe14425ec8147.png"},{"id":68476848,"identity":"6af98f93-a070-491e-8302-ff1e4f75bebb","added_by":"auto","created_at":"2024-11-07 16:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102109,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4820100/v1/ee21f36b-0605-4db0-9175-a7b526a683c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eIn today's manufacturing landscape, sustainability has become crucial, particularly in injection moulding, where sustainability competency integrates environmentally friendly practices, economic viability, and social responsibility. This involves understanding sustainable materials, processes, and design strategies to minimise environmental impact while maintaining efficiency and product quality (Czepiel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gholami et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Selamat et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Key areas of sustainability competency in injection moulding include material selection, process optimisation, waste reduction, lifecycle assessment, design for sustainability, regulatory compliance, continuous improvement, and effective communication. This competency not only addresses environmental challenges but also serves as a proactive strategy for long-term success (Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Turan, Johan, \u0026amp; Nor, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vieira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, research trends in sustainability competency encompass interdisciplinary approaches, education and training methods, assessment tools development, behavioural psychology insights, technology integration, corporate social responsibility, circular economy principles, policy influence, social equity considerations, supply chain management, stakeholder engagement, and resilience against environmental changes (D. O. Aikhuele \u0026amp; Turan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sahimi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Turan \u0026amp; Johan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It's important to note that the field is dynamic, and staying updated on recent research through academic sources is advisable.\u003c/p\u003e \u003cp\u003eSeveral research studies have delved into improving the mechanical properties and production processes of plastic parts through the use of the Taguchi method (Adanan, Mohd Turan, Johan, Md Yusoff, \u0026amp; Yee, 2022; Zhu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, investigations by various researchers focused on optimising parameters like melting temperature, injection pressure, and cooling time to enhance the strength and quality of plastic products (Mehat \u0026amp; Kamaruddin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e). By carefully adjusting these factors, researchers achieved significant improvements in tensile, compressive, and flexural strengths of plastic trays, containers, and other items made from recycled plastics, demonstrating the feasibility of substituting recycled materials for virgin plastics. Integration of the Taguchi method with simulation software like Mould-Flow further enhanced the process optimisation, enabling engineers to identify optimal parameters and improve energy efficiency and product quality (Moayyedian et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, studies explored the Taguchi method's applicability in minimising defects like weld lines and shrinkage, leading to enhanced quality and dimensional accuracy of plastic parts, which is crucial in industries requiring tight tolerances (Adanan, Mohd Turan, Johan, Md Yusoff, \u0026amp; Xin, 2022).\u003c/p\u003e \u003cp\u003eFurthermore, researchers investigated the Taguchi method's effectiveness in optimising the mechanical properties of new composites, including bio composites and recycled composites, highlighting the potential for sustainable practices by utilising recycled materials and improving composite performance through optimised processing conditions (Haniel et al., 2023). By systematically analysing the impact of various factors on material properties, researchers identified optimal process parameters to achieve desired outcomes, such as improved impact strength and dimensional stability (Chauhan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; W.-C. Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, the Taguchi method facilitated the minimisation of part weight and reduction of defects like short shots, further enhancing the efficiency and reliability of injection moulding processes (Panneerselvam \u0026amp; Turan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Overall, the Taguchi method emerged as a valuable tool for optimising injection moulding processes, enabling improved mechanical properties, reduced defects, and enhanced quality of plastic parts, thereby contributing to sustainable practices and efficient production processes in the plastic industry (Panneerselvam \u0026amp; Turan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe excerpt emphasises the importance of sustainability practices and the benefits they bring. It highlights that sustainability practices can enhance brand value, attract customers, employees, and investors, reduce costs, improve business resilience, and create new opportunities for market expansion. The article also discusses the need for comprehensive approaches and frameworks, as well as the use of indicators to measure sustainable development. It mentions the importance of integrating knowledge and consciousness levels in the decision-making process to achieve sustainable development goals effectively. In the context of Malaysia, the article acknowledges the country's involvement in the development of the Sustainable Development Goals (SDGs) and the alignment of the SDGs with the national agenda. However, it also mentions the need for comprehensive approaches, frameworks, and indicators to address the challenges in interpreting sustainable development and setting indicators in Malaysia. Overall, the article emphasises the importance of integrating sustainable business practices, knowledge, consciousness, and comprehensive approaches to achieve sustainable development goals and ensure the success of businesses in Malaysia (Adanan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Turan, Johan, \u0026amp; Abu Sofian, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSustainable manufacturing involves employing technologies and practices that adhere to sustainability principles across economic, environmental, and social aspects. It encompasses various outcomes, including prioritizing eco-friendliness to reduce environmental impact, optimising resource use to lower costs and product prices, minimising energy consumption for cost savings and environmental preservation, reducing waste generation for efficient resource utilisation and pollution reduction, prioritising safety in operations to prevent accidents, and enhancing worker health by improving workplace conditions (Sahimi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Meeting the growing demand for consumer products while ensuring sustainability aligns with environmental responsibility and long-term economic competitiveness. Efforts to reduce energy consumption and increase renewable energy usage are crucial, considering that a significant portion of global energy demand and CO\u003csub\u003e2\u003c/sub\u003e emissions stem from manufacturing activities. Achieving sustainability involves comprehensive approaches at both the factory and process levels, including data-driven strategies, technical modifications to conserve energy and resources, and adopting energy-efficient technologies as alternatives to traditional methods (D.-C. Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Farbodi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mehat \u0026amp; Kamaruddin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e; Turan, Johan, \u0026amp; Omar, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research study concentrates on exploring parameters related to knowledge, attitude, and psychological aspects within the context of sustainability. The work of Ramdas \u0026amp; Mohamed [26] provides a suitable reference, as it delves into the interconnectedness between knowledge, attitude, and behavioural intention, drawing insights from the theory of action. This theory suggests that individuals' decisions to engage in certain behaviours are influenced by their perceptions of sustainability practices rather than solely by extreme desires or unconscious motives. The research underscores the importance of quantitatively measuring knowledge while also recognizing the qualitative aspects of attitudes towards best practices. The theoretical framework presented by Ramdas \u0026amp; Mohamed integrates tangible elements like knowledge with intangible factors such as consciousness or attitudes, which serve as motivators for adopting sustainable business practices. Moreover, the theory posits a correlation between willingness to participate in sustainability efforts and factors like knowledge, awareness, attitude, or consciousness, emphasising the interdependence of these elements in fostering sustainable behaviours. Considering the focus on knowledge, attitude, and psychological aspects in this research, the findings from Ramdas \u0026amp; Mohamed (Ramdas \u0026amp; Mohamed, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) offer valuable insights and support for the study's objectives, ensuring relevance and accuracy in the analysis and findings.\u003c/p\u003e \u003cp\u003eThe literature review underscores various factors influencing the quality of plastic parts manufactured through injection moulding, including material selection, design, and processing parameters. Researchers have focused on minimising defects using the Taguchi method, particularly addressing issues like warpage and shrinkage. Processing parameters have been extensively studied for their impact on part quality, often integrated with simulation packages to optimise their influence. Some studies explore how the Taguchi method enhances mechanical properties, aiming to improve composite material performance. However, this research aims to advance beyond single characteristic optimisation by combining quality characteristics using a composite desirability function, allowing for a more comprehensive assessment of part quality. Expert elicitation proves useful in capturing implicit judgments, particularly in scenarios lacking empirical data, offering valuable insights for decision-making and modelling. Integrating sustainability into the injection moulding optimisation process faces challenges like technical complexity and data availability, requiring a collaborative effort to address these limitations effectively.\u003c/p\u003e \u003cp\u003eThis study focuses on the challenges in incorporating recycled materials in plastic manufacturing, particularly in injection moulding, due to the degradation of mechanical properties compared to virgin materials. While injection moulding parameters significantly impact product quality, the current approach often relies on trial and error rather than systematic optimisation, leading to material wastage. Integrating sustainability into this optimisation process faces challenges like technical complexity and data availability. This research aims to investigate how injection moulding parameters affect mechanical properties, optimise these parameters using the Taguchi method, and characterise them with sustainability competency. It emphasises thermoplastic polypropylene (PP) and excludes thermosets, focusing on tensile strength and flexural modulus for both virgin and recycled plastics. The study will utilise the Taguchi method and the desirability function to enhance part quality while considering sustainability principles, aiming to contribute to a more responsible manufacturing future.\u003c/p\u003e \u003cp\u003eIn this study, the attitudinal parameter serves as a mathematical representation of the emotional inclinations of design stakeholders or decision-makers, acknowledging that attitudes significantly shape behaviour and decision-making processes. (D. Aikhuele \u0026amp; Turan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Turan, Johan, Lanang, et al., 2016; Wan Lanang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrate that knowledge levels influence awareness and attitudes, impacting both willingness to pay and environmental literacy. Supported by (Ayasrah \u0026amp; Mohd Turan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the theory of reasoned action suggests a link between beliefs, attitudes, intentions, and behaviours. Attitudes can be influenced by various factors such as environmental concerns, personal values, and motivations, as highlighted by (D. O. Aikhuele \u0026amp; Turan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). (Ayasrah et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) propose an attitudinal parameter (\u0026#120582;) to capture decision makers' risk attitudes, enabling classification as risk-averse, risk-neutral, or risk-seeking. This parameter facilitates a deeper understanding of decision makers' emotional disposition and its influence on decision-making processes, ultimately impacting sustainability practices. Additionally, expert elicitation proves valuable in capturing implicit judgments, particularly in fields where expert knowledge shapes probability distributions for uncertain variables. This method allows for the incorporation of expert opinions into decision-making analyses, especially in scenarios where empirical data is lacking or traditional data collection methods are impractical. Following a well-defined protocol during the elicitation process ensures that judgments are based on evidence and personal experience, providing scientifically informed insights for decision-making and modelling purposes across various fields. Additionally, embedding sustainable manufacturing practices into the optimisation process will ensure the efficient utilisation of resources and minimise environmental impact, aligning with the broader goal of sustainability in the plastic industry.\u003c/p\u003e"},{"header":"2.0 METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 RESEARCH FRAMEWORK\u003c/h2\u003e \u003cp\u003eThis research is divided into three main phases: selecting process parameters and quality characteristics, optimizing process parameters, and establishing quantitative relationships, and integrating sustainability competency into characterising process parameters. The first part explains how variables are chosen, including process parameters and quality characteristics. Then, it discusses how optimisation is done in two steps: first, through Taguchi experiments and mechanical testing, followed by analysis using the S/N ratio method; second, through the desirability function and verification studies. Next, it outlines how to establish a quantitative relationship between process parameters and quality characteristics. Finally, it explores how sustainability competency is incorporated into characterising injection moulding process parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e shows the proposed framework visually, outlining the structure of the research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 PHASE 1: PROCESS PARAMETER AND QUALITY CHARACTERISTIC SELECTION\u003c/h2\u003e \u003cp\u003eIn the first phase of this research, emphasis is placed on the selection of quality characteristics and process parameters essential for injection moulding. Quality characteristics, such as dimensional properties, surface appearance, and mechanical strength, play a critical role in evaluating part effectiveness. Based on an extensive literature review, the primary focus is on assessing the tensile strength and flexural modulus of recycled polypropylene parts. Through thorough research, eight key parameters have been identified: melt temperature, injection pressure, injection speed, injection time, holding pressure, holding time, cooling time, and mould temperature (held constant in this study). Three levels are selected for each factor to enable better parameter analysis and optimization. These parameters are chosen based on industrial standards and preliminary testing to ensure the production of parts with acceptable dimensional properties and mechanical strength. The values of these parameters are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e, excluding the mould temperature parameter, which remains constant throughout the study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSELECTED PROCESS PARAMETERS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcess Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelt temperature, ℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjection pressure, MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjection speed, mm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInjection time, Sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHolding pressure, MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHolding time, Sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCooling time, Sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PHASE 2: OPTIMISING PROCESS PARAMETERS\u003c/h2\u003e \u003cp\u003eIn the second phase of the research, the focus shifts to optimising the injection moulding process parameters using the Taguchi optimisation method. When multiple responses share similar optimal characteristics, an overall desirability function becomes crucial. This function transforms each response into a dimensionless value, indicating its desirability. After determining individual desirability functions for each response, they are combined into an overall desirability function using a relative weight scale. Regression analysis is then utilised to establish quantitative relationships between product quality and process parameters. This analysis explains the connection between dependent and independent variables, with linear regression being the preferred method. Coefficients in the regression model indicate the extent and direction of the impact of independent variables on the dependent variable. The R-squared value measures the proportion of variability in the dependent variable explained by independent variables, while the P-value indicates the significance of these correlations. Additionally, the standard error of the coefficient provides insight into the precision of coefficient estimates, with smaller values indicating higher precision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 PHASE 3: CHARACTERISING PROCESS PARAMETERS\u003c/h2\u003e \u003cp\u003eIn the third phase, the focus is on characterising process parameters, specifically by incorporating sustainability competency into the model derived from the optimization phase. This collaborative equation, known as the basic model for forecasting sustainability competency value, integrates correlations between injection moulding parameters. Attitude is identified as a crucial factor in this integration, particularly within the demographic context of industry executives and non-executives, which are categorised into operational teams. The qualitative responses from survey questions were transformed into quantitative data using the attitudinal theory, as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e, aligning with a common practice among previous researchers (Ahmed Shaikh, 2016; Jim\u0026eacute;nez \u0026amp; San-Mart\u0026iacute;n, 2017; Turan et al., 2017).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTHE INTERPRETATION FROM SCALE TO ATTITUDINAL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttitudinal response\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk averse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk seeking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe incorporation of job design and sustainability competency factors further enhances the model, emphasising knowledge and behaviour within the broader framework of sustainability competency. Functional attributes, termed sustainability competency descriptors, are outlined to encapsulate the dimensions of sustainability competency within different roles. These descriptors are comprehensively outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e, encapsulating the intricate dimensions of sustainability competency within the context of the staff's role. Additionally, survey distribution and data collection methods are discussed, including the distribution of surveys via postal methods and the subsequent organisation and analysis of collected data. The conversion of qualitative responses into quantitative data, expert elicitation, and regression analysis for sustainability competency value are also described in detail, emphasising the comprehensive approach employed in this phase.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJOB DESIGN BASED ON SUSTAINABILITY COMPETENCY DESCRIPTORS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJob design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSustainability competency descriptors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay shift\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNight shift\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-executive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 EXPERIMENTAL RESULTS\u003c/h2\u003e \u003cp\u003eThe data obtained from both tensile testing and flexural testing of all the specimens were organised into a tabular format. The average values derived from these data sets were then utilised to analyse the mechanical properties exhibited by the recycled material based on the settings of the process parameters. The results for the tensile strength and flexural modulus of each specimen are available in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e and 3.2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTENSILE STRENGTH RESULTS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest 1 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest 2 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest 3 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e191.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e191.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e195.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e197.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e189.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e189.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e171.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e165.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e175.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e170.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e165.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFLEXURAL MODULUS RESULTS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest 1 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest 2 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest 3 (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9770.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9611.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9339.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9573.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10028.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9843.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9740.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9870.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9815.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9818.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9515.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9716.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10052.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9269.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9617.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9646.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9891.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9979.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9756.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9875.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9573.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9532.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9535.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9547.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9554.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9827.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9768.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9716.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9922.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10181.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10166.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10090.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10076.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10141.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10031.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10083.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10200.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10067.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9715.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9994.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9653.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9742.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9366.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9587.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9281.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9405.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9026.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9238.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9802.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9482.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9859.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9714.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9813.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9643.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9453.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9636.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9762.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9712.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10054.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9842.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9627.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9827.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9602.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9685.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9608.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9944.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9794.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9782.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9776.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9634.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9580.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9663.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9174.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9372.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9353.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9313.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9349.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9118.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9352.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9273.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9267.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9302.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9049.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9206.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9802.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9601.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9753.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9719.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9137.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9381.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9064.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9194.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9901.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9369.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9398.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9556.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9377.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9171.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9337.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9295.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8968.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9327.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9203.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9166.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9449.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9636.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9490.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9525.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e and 3.4 display rankings of process parameters' impact on tensile strength and flexural modulus, using signal-to-noise ratios. The delta value, calculated from highest and lowest averages for each factor, measures effect size. Melt temperature ranks highest for both qualities, followed by other factors like injection time and holding time. Injection pressure ranks lowest. For flexural modulus, the same trend is observed, with melt temperature being most influential and holding pressure least.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF TENSILE STRENGTH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRESPONSE TABLE FOR THE SIGNAL TO NOISE RATIO OF FLEXURAL MODULUS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 PARAMETERS OPTIMISATION\u003c/h2\u003e \u003cp\u003eAfter individually optimizing tensile strength and flexural modulus, both responses are optimised together using desirability functions. Each response is treated as an objective function, with specific process parameters identified for maximum tensile strength and flexural modulus. For example, a melt temperature of 180\u0026deg;C, a holding time of 3 seconds, and other parameters yield a tensile strength of 203.82 kgf/cm\u0026sup2;. Similarly, optimal parameters for maximum flexural modulus include a melt temperature of 180\u0026deg;C and others, resulting in a modulus of 10005 kgf/cm\u0026sup2;. However, optimising each response separately may lead to different optimal parameters. To address this, a composite desirability function method is used to find a single optimal value that improves all responses together. The outcomes show optimal parameter values where both tensile strength and flexural modulus have the same desirability value. These results are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRESULTS OF MULTI RESPONSE OPTIMISATION\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted responses\u003c/p\u003e \u003cp\u003e(kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDesirability value\u003c/p\u003e \u003cp\u003e(kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTensile strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexural modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA confirmation test serves to validate the research outcomes by ensuring that the optimised process parameters indeed produce the expected quality characteristics. Despite challenges in achieving a perfect match due to repeatability factors, the confirmation experiment was conducted using the established optimal parameters to assess any differences between these parameters and the actual manufacturing process. Maintaining the same material properties as in the initial experiment helped mitigate variations. Three specimens were manufactured using the optimised parameters, followed by tensile and flexural tests. The results were compared with the anticipated values, showing a close match as shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e. For instance, the anticipated and observed tensile strengths were 199 kgf/cm\u0026sup2; and 197 kgf/cm\u0026sup2; respectively, with a percentage error of 1%. Similarly, the flexural modulus values showed a minimal error of 0.03%. This confirmation experiment underscores the validity of the optimisation approach, affirming the reliability and accuracy of the optimized process parameters in achieving the desired quality characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCONFIRMATION TEST OF OPTIMISED PARAMETERS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted results (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirmation results (kgf/cm\u003csup\u003e2\u003c/sup\u003e )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eError (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTensile strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexural modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 PARAMETERS OPTIMISATION\u003c/h2\u003e \u003cp\u003eRegression analysis is a statistical method used to explore the relationship between variables. In multiple linear regression, the R-square value, typically between 0.8 and 1, measures how well the model predicts future outcomes based on existing data. It indicates the accuracy of the model in forecasting results. In this study, regression was used to model the relationship between process parameters and quality attributes. Tables\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e and 3.8 show the coefficients for each injection moulding parameter, detailing their impact on tensile strength and flexural modulus respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCOEFFICIENT OF PREDICTION MODEL FOR TENSILE STRENGTH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelt temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooling time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCOEFFICIENT OF PREDICTION MODEL FOR FLEXURAL MODULUS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelt temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjection time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooling time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA mathematical model derived from regression analysis needs validation to ensure its accuracy in real manufacturing processes. To confirm its reliability, an actual experiment mirroring previous setups was conducted, and mechanical tests were carried out accordingly. Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e3.9\u003c/span\u003e shows the results of this verification test. The model predicted tensile strength and flexural modulus as 197.19 kgf/cm\u0026sup2; and 9827.05 kgf/cm\u0026sup2; respectively, while the confirmed values were 197.26 kgf/cm\u0026sup2; and 9875.88 kgf/cm\u0026sup2;. The error percentages for tensile strength and flexural modulus were calculated at 0.03% and 0.4% respectively, both below 2%. These validation outcomes underscore the practicality and effectiveness of the regression model for both responses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCONFIRMATION TEST OF REGRESSION MODEL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredicted results (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConfirmation results (kgf/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eError (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eE\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTensile strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e197.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e197.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlexural modulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9827.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9875.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 INCORPORATING SUSTAINABILITY COMPETENCY\u003c/h2\u003e \u003cp\u003eThe model developed in Phase 2 lacks the comprehensive competency needed to foster innovation due to its oversight of staff roles and behavioural elements like trust and honesty crucial for effective knowledge creation. To address this, a sustainability competency definition is introduced to incorporate staff roles and attitudinal factors. This enhancement aims to predict competency value more accurately by integrating critical elements like \"Knowledge\" and \"Behaviour\" into the equation using feedback from questionnaires. These elements are assigned significant weightages, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e3.10\u003c/span\u003e, to better capture their impact on the knowledge creation process, thus improving the equation's predictive capacity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJOB DESIGN WEIGHTAGE FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJob design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWeightage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBehavioural\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-executive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe job design has been characterised by integrating staff functions with the sustainability competency definition, including knowledge and behaviour. This integration is represented by predictive equations in Tables\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e3.11\u003c/span\u003e and 3.12, forming a comprehensive model that captures the relationship between staff roles, attitudes, and job design. These appendices offer a thorough examination of regression results and their connection to knowledge and behaviour factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (TENSILE STRENGTH)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational team\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacteristic (predictive) model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBefore characterised\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTS\u0026thinsp;=\u0026thinsp;253.3\u0026ndash;0.3373 A \u0026minus;\u0026thinsp;0.173 B- 0.055 C\u0026thinsp;+\u0026thinsp;2.31 D \u0026minus;\u0026thinsp;0.0039 E\u0026thinsp;+\u0026thinsp;1.83 F \u0026minus;\u0026thinsp;0.253 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eAfter characterised\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk1\u0026thinsp;=\u0026thinsp;116.72\u0026ndash;0.1567 A \u0026minus;\u0026thinsp;0.0516 B\u0026thinsp;+\u0026thinsp;0.0058 C\u0026thinsp;+\u0026thinsp;0.9288 D\u0026thinsp;+\u0026thinsp;0.0074 E\u0026thinsp;+\u0026thinsp;0.8097 F \u0026minus;\u0026thinsp;0.0990 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb1\u0026thinsp;=\u0026thinsp;129.14\u0026ndash;0.1733\u0026nbsp;A -\u0026nbsp;0.0570\u0026nbsp;B +\u0026nbsp;0.0060\u0026nbsp;C +\u0026nbsp;1.0280\u0026nbsp;D +\u0026nbsp;0.0081\u0026nbsp;E +\u0026nbsp;0.8960\u0026nbsp;F -\u0026nbsp;0.1090\u0026nbsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk2\u0026thinsp;=\u0026thinsp;125.15\u0026ndash;0.1660\u0026nbsp;A -\u0026nbsp;0.0749\u0026nbsp;B -\u0026nbsp;0.0457\u0026nbsp;C +\u0026nbsp;1.0276\u0026nbsp;D -\u0026nbsp;0.0090\u0026nbsp;E +\u0026nbsp;0.6307\u0026nbsp;F -\u0026nbsp;0.0915\u0026nbsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb2\u0026thinsp;=\u0026thinsp;166.39\u0026ndash;0.2234\u0026nbsp;A -\u0026nbsp;0.0736\u0026nbsp;B +\u0026nbsp;0.0083\u0026nbsp;C +\u0026nbsp;1.3240\u0026nbsp;D +\u0026nbsp;0.0105\u0026nbsp;E +\u0026nbsp;1.1543\u0026nbsp;F \u0026ndash; 0.1411 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk3\u0026thinsp;=\u0026thinsp;119.91\u0026ndash;0.1596\u0026nbsp;A -\u0026nbsp;0.1054\u0026nbsp;B +\u0026nbsp;0.0390\u0026nbsp;C +\u0026nbsp;1.2713\u0026nbsp;D -\u0026nbsp;0.0042\u0026nbsp;E +\u0026nbsp;1.0912\u0026nbsp;F -\u0026nbsp;0.1537\u0026nbsp;G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb3\u0026thinsp;=\u0026thinsp;170.94\u0026ndash;0.2275\u0026nbsp;A -\u0026nbsp;0.1503\u0026nbsp;B -\u0026nbsp;0.0556\u0026nbsp;C +\u0026nbsp;1.8123\u0026nbsp;D -\u0026nbsp;0.0060\u0026nbsp;E +\u0026nbsp;1.5555\u0026nbsp;F \u0026ndash; 0.2191 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eWk\u0026thinsp;=\u0026thinsp;knowledge, Wb\u0026thinsp;=\u0026thinsp;behaviour, TS\u0026thinsp;=\u0026thinsp;tensile strength\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCHARACTERISATION OF OPERATIONAL TEAM FOR KNOWLEDGE AND ATTITUDINAL BEHAVIOUR (FLEXURAL MODULUS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational team\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacteristic (predictive) model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBefore characterised\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFM\u0026thinsp;=\u0026thinsp;9603\u0026thinsp;\u0026minus;\u0026thinsp;5.37 A\u0026thinsp;+\u0026thinsp;13.71 B\u0026thinsp;+\u0026thinsp;1.29 C\u0026thinsp;+\u0026thinsp;46.5 D \u0026minus;\u0026thinsp;1.04 E\u0026thinsp;+\u0026thinsp;75.9 F\u0026thinsp;+\u0026thinsp;1.60 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eAfter characterised\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk1\u0026thinsp;=\u0026thinsp;4339\u0026thinsp;\u0026minus;\u0026thinsp;2.56 A\u0026thinsp;+\u0026thinsp;7.36 B \u0026minus;\u0026thinsp;1.30 C\u0026thinsp;+\u0026thinsp;38.46 D\u0026thinsp;+\u0026thinsp;0.56 E\u0026thinsp;+\u0026thinsp;43.89 F\u0026thinsp;+\u0026thinsp;1.83 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb1\u0026thinsp;=\u0026thinsp;6186\u0026ndash;3.65 A -3.65 B \u0026minus;\u0026thinsp;1.85 C\u0026thinsp;+\u0026thinsp;54.80 D\u0026thinsp;+\u0026thinsp;0.80 E\u0026thinsp;+\u0026thinsp;62.60 F\u0026thinsp;+\u0026thinsp;2.61 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk2\u0026thinsp;=\u0026thinsp;4025\u0026thinsp;\u0026minus;\u0026thinsp;2.26 A\u0026thinsp;+\u0026thinsp;13.14 B\u0026thinsp;+\u0026thinsp;6.79 C\u0026thinsp;+\u0026thinsp;7.77 D \u0026minus;\u0026thinsp;1.16 E\u0026thinsp;+\u0026thinsp;29.65 F\u0026thinsp;+\u0026thinsp;3.15 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb2\u0026thinsp;=\u0026thinsp;5738\u0026ndash;3.23 A\u0026thinsp;+\u0026thinsp;18.73 B\u0026thinsp;+\u0026thinsp;9.68 C\u0026thinsp;+\u0026thinsp;11.10 D \u0026minus;\u0026thinsp;1.65 E\u0026thinsp;+\u0026thinsp;42.30 F\u0026thinsp;+\u0026thinsp;4.49 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWk3\u0026thinsp;=\u0026thinsp;5395\u0026thinsp;\u0026minus;\u0026thinsp;2.90 A \u0026minus;\u0026thinsp;0.99 B \u0026ndash; 4.05 C\u0026thinsp;+\u0026thinsp;20.13 D \u0026minus;\u0026thinsp;0.83 E\u0026thinsp;+\u0026thinsp;33.87 F \u0026minus;\u0026thinsp;2.83 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWb3\u0026thinsp;=\u0026thinsp;5726\u0026thinsp;\u0026minus;\u0026thinsp;3.08 A \u0026minus;\u0026thinsp;1.04 B \u0026minus;\u0026thinsp;4.30 C\u0026thinsp;+\u0026thinsp;21.4 D \u0026minus;\u0026thinsp;0.88 E\u0026thinsp;+\u0026thinsp;35.9 F \u0026minus;\u0026thinsp;3.00 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eWk\u0026thinsp;=\u0026thinsp;knowledge, Wb\u0026thinsp;=\u0026thinsp;behaviour, FM\u0026thinsp;=\u0026thinsp;flexural modulus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe R-squared (R\u0026sup2;) coefficient measures how well a regression model fits the data. It indicates how accurately the predictors within the model explain the response. Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e3.13\u003c/span\u003e and 3.14 show the R\u0026sup2; values for the tensile strength and flexural modulus models respectively. Across all models, whether before or after characterisation, those incorporating sustainability competency definitions and attitudinal aspects demonstrate high regression coefficients. This suggests they explain a significant portion of the variability in competency dimensions. The values from the characterized model highlight the strength of competency attributed to integration, where staff attitudes toward knowledge creation interact with mechanical properties based on optimized parameters established in Phase 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR-SQUARED (R\u0026sup2;) ASSESSMENT RESULT FOR ALL TENSILE STRENGTH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJob design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOperational team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c4\" namest=\"c3\" rowspan=\"2\"\u003e \u003cp\u003eBefore characterisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eAfter characterisation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eBehaviour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e89.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e85.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-executive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab17\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3.14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR-SQUARED (R\u0026sup2;) ASSESSMENT RESULT FOR ALL FLEXURAL MODULUS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eJob design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOperational team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c4\" namest=\"c3\" rowspan=\"2\"\u003e \u003cp\u003eBefore characterisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eAfter characterisation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eBehaviour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u0026sup2; (adj)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e57.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e57.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-executive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 SUMMARY\u003c/h2\u003e \u003cp\u003eThe experiment followed the Taguchi design, evaluating mechanical properties like tensile strength and flexural modulus, documented in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e and 3.2. Signal-to-noise ratio analysis identified key injection moulding factors affecting recycled polypropylene. Melt temperature was most influential for tensile strength, followed by injection and holding time, while for flexural modulus, it was melting temperature, holding time, and injection pressure. Optimizing both properties simultaneously resulted in 199kgf/cm\u0026sup2; tensile strength and 10005 kgf/cm\u0026sup2; flexural modulus with parameters like 180\u0026deg;C melt temperature and 8s injection time. A confirmation test validated these parameters with error (%) values of 1% for tensile strength and 0.03% for flexural modulus. Regression analysis developed a mathematical model showing 0.03% and 0.4% error (%) for tensile strength and flexural modulus, affirming model reliability. The characterisation of operational teams for knowledge and behaviour found no significant differences between models before and after, validating the sustainability competency model for self-assessment of knowledge creation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 CONCLUSIONS","content":"\u003cp\u003eThis research explores the relationship between job design and attitudes using predictive models with integrated algorithms. By analysing data from experiments and questionnaires on process parameters and competency, the study investigates how job design, operational teams, and sustainability competency descriptors interact in injection moulding investigations. These correlations help predict overall competency performance, symbolised as competency value distributions. The characterised model generates metrics for competency performance, termed sustainability competency, capturing the interaction between job design, operational teams, sustainability competency descriptors, and other factors. Additionally, a mathematical model is developed through regression analysis to understand the relationship between injection moulding processes and mechanical properties. The regression analysis shows strong correlations between process parameters and attitudinal elements, enhancing the competency model. Finally, the study's main contributions include identifying optimal injection moulding parameters, simultaneously optimising quality characteristics, and refining the competency model by incorporating attitudinal elements, all essential for achieving competitiveness and innovation in the plastic industrial sector.\u003c/p\u003e \u003cp\u003eThis research focuses on optimising the production of recycled materials to improve their strength using Taguchi experimental design and the desirability function approach. Future studies should explore additional aspects of material strength, such as impact and compression, and test different batches of recycled materials for consistency. Advanced techniques like scanning electron microscopy can provide more precise insights into material properties. Understanding how various factors like temperature and machine settings influence the final product is essential, along with investigating the effects of mixing recycled materials with new ones and additives. The newly developed model helps predict how well the manufacturing process integrates sustainability, which is crucial for industry growth and ensuring quality standards.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors, Anis Izzati Md Yusoff, Faiz Mohd Turan, and Nur Qurratul Ain Adanan carried out the conceptualisation, drafting of the research work, and editing of the final article. All authors approved the submitted version.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by a grant from Universiti Malaysia Pahang Al-Sultan Abdullah (RDU233016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdanan, N. Q. A., Mohd Turan, F., Johan, K., Md Yusoff, A. I., \u0026amp; Xin, W. H. (2022). Optimising Casting Film Parameters for LPDE Material Assessment (pp. 67\u0026ndash;74). https://doi.org/10.1007/978-981-19-2890-1_7\u003c/li\u003e\n\u003cli\u003eAdanan, N. Q. A., Mohd Turan, F., Johan, K., Md Yusoff, A. I., \u0026amp; Yee, Y. W. (2022). Performance of Assessment Model for Injection Moulding Parameters (pp. 59\u0026ndash;65). https://doi.org/10.1007/978-981-19-2890-1_6\u003c/li\u003e\n\u003cli\u003eAdanan, N. Q. A., Turan, F. M., \u0026amp; Johan, K. (2021). Industrial Sustainability Policy and Standards-Related on Management Discipline of SMEs Industry in Malaysia: A Conceptual Framework. In Lecture Notes in Mechanical Engineering (Vol. 46). https://doi.org/10.1007/978-981-15-9505-9_3\u003c/li\u003e\n\u003cli\u003eAikhuele, D. O., \u0026amp; Turan, F. M. (2016). A Hybrid Fuzzy Model for Lean Product Development Performance Measurement. IOP Conference Series: Materials Science and Engineering, 114(1). https://doi.org/10.1088/1757-899X/114/1/012048\u003c/li\u003e\n\u003cli\u003eAikhuele, D. O., \u0026amp; Turan, F. M. (2018). A modified exponential score function for troubleshooting an improved locally made Offshore Patrol Boat engine. Journal of Marine Engineering and Technology, 17(1). https://doi.org/10.1080/20464177.2017.1286841\u003c/li\u003e\n\u003cli\u003eAikhuele, D., \u0026amp; Turan, F. (2018). A conceptual model for the implementation of lean product development. International Journal of Service Science, Management, Engineering, and Technology, 9(1). https://doi.org/10.4018/IJSSMET.2018010101\u003c/li\u003e\n\u003cli\u003eAyasrah, O., \u0026amp; Mohd Turan, F. (2022). Assessing Integrated TOPSIS Model with Exponential Intuitionistic Entropy Measure: A Case Study. In Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-981-19-2890-1_5\u003c/li\u003e\n\u003cli\u003eAyasrah, O., Mohd Turan, F., \u0026amp; Fahami, S. M. H. (2024). An Integrated TOPSIS Model with Exponential Intuitionistic Entropy Measure for Multi-Attribute Decision-Making (MADM) (pp. 59\u0026ndash;69). https://doi.org/10.1007/978-981-99-9848-7_6\u003c/li\u003e\n\u003cli\u003eCao, Y., Fan, X., Guo, Y., Ding, W., Liu, X., \u0026amp; Li, C. (2023). Multi-objective optimization of injection molding process parameters based on BO-RFR and NSGAⅡ methods. International Polymer Processing, 38(1), 8\u0026ndash;18. https://doi.org/10.1515/ipp-2020-4063\u003c/li\u003e\n\u003cli\u003eChauhan, V., K\u0026auml;rki, T., \u0026amp; Varis, J. (2021). Optimization of Compression Molding Process Parameters for NFPC Manufacturing Using Taguchi Design of Experiment and Moldflow Analysis. Processes, 9(10), 1853. https://doi.org/10.3390/pr9101853\u003c/li\u003e\n\u003cli\u003eChen, D.-C., Chen, D.-F., \u0026amp; Huang, S.-M. (2024). Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality. Polymers, 16(8), 1113. https://doi.org/10.3390/polym16081113\u003c/li\u003e\n\u003cli\u003eChen, W.-C., Nguyen, M.-H., Chiu, W.-H., Chen, T.-N., \u0026amp; Tai, P.-H. (2016). Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. The International Journal of Advanced Manufacturing Technology, 83(9\u0026ndash;12), 1873\u0026ndash;1886. https://doi.org/10.1007/s00170-015-7683-0\u003c/li\u003e\n\u003cli\u003eCzepiel, M., Bańkosz, M., \u0026amp; Sobczak-Kupiec, A. (2023). Advanced Injection Molding Methods: Review. Materials, 16(17), 5802. https://doi.org/10.3390/ma16175802\u003c/li\u003e\n\u003cli\u003eFarbodi, M. (2017). Application of Taguchi Method for Optimizing of Mechanical Properties of Polystyrene-Carbon Nanotube Nanocomposite. Polymers and Polymer Composites, 25(2), 177\u0026ndash;184. https://doi.org/10.1177/096739111702500208\u003c/li\u003e\n\u003cli\u003eGholami, M. D., Salamat, M., \u0026amp; Hashemi, R. (2021). 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Optimization of mechanical properties of recycled plastic products via optimal processing parameters using the Taguchi method. Journal of Materials Processing Technology, 211(12), 1989\u0026ndash;1994. https://doi.org/10.1016/j.jmatprotec.2011.06.014\u003c/li\u003e\n\u003cli\u003eMoayyedian, M., Dinc, A., \u0026amp; Mamedov, A. (2021). Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques. Polymers, 13(23), 4158. https://doi.org/10.3390/polym13234158\u003c/li\u003e\n\u003cli\u003eNguyen, D. T., Yu, E., Barry, C., \u0026amp; Chen, W.-T. (2024). Energy consumption variability in life cycle assessments of injection molding processes: A critical review and future outlooks. Journal of Cleaner Production, 452, 142229. https://doi.org/10.1016/j.jclepro.2024.142229\u003c/li\u003e\n\u003cli\u003ePanneerselvam, V., \u0026amp; Turan, F. M. (2020). 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IOP Conference Series: Materials Science and Engineering, 226(1). https://doi.org/10.1088/1757-899X/226/1/012049\u003c/li\u003e\n\u003cli\u003eWen, T., Chen, X., Yang, C., Liu, L., \u0026amp; Hao, L. (2014). Optimization of processing parameters for minimizing warpage of large thin-walled parts in whole stages of injection molding. Chinese Journal of Polymer Science, 32(11), 1535\u0026ndash;1543. https://doi.org/10.1007/s10118-014-1541-7\u003c/li\u003e\n\u003cli\u003eZhu, J., Qiu, Z., Huang, Y., \u0026amp; Huang, W. (2021). Overview of injection molding process optimization technology. Journal of Physics: Conference Series, 1798(1), 012042. https://doi.org/10.1088/1742-6596/1798/1/012042\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable Manufacturing, Injection Moulding, Energy Efficiency, Sustainability Integration, Process Parameter Optimisation","lastPublishedDoi":"10.21203/rs.3.rs-4820100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4820100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInjection moulding is a widely used method for manufacturing plastic components, with the quality of the final product depending on various process factors managed throughout the procedure. Integrating sustainable manufacturing practices is crucial for mitigating ecological impacts while maintaining product excellence. Manufacturers need to balance product quality, procedural effectiveness, and environmental impact by evaluating how each parameter affects the product's quality and ecological footprint. While many focus on optimising process parameters, fewer consider integrating sustainability competency, which also affects parameter performance. This study aims to advance understanding by conducting experiments and analyses on these factors' influence on product quality. The incorporation of sustainability competency aims to empower individuals and entities to make informed choices that align with environmental, societal, and economic factors for a more sustainable and accountable future. The optimised model, with an error of less than 1%, quantifies the competency value bridging mechanical properties and comprehensive competency by integrating attitudinal factors. Parameter selection through Design of Experiments (DOE) and expert elicitation method contribute to this integration. Evolution from the foundational to the proficient model includes operational team and sustainability competency descriptors, providing context for innovation and knowledge creation highly valued by employers and stakeholders in a productive and streamlined setting. Additionally, this research contributes to the advancement of smart grid and sustainable energy applications by promoting energy-efficient manufacturing processes. By integrating renewable energy sources and smart grid technologies, the injection moulding industry can achieve significant reductions in energy consumption and greenhouse gas emissions. This integration not only enhances the sustainability of manufacturing processes but also supports the broader transition to a more resilient and eco-friendly energy system.\u003c/p\u003e","manuscriptTitle":"Optimising Plastic Injection Moulding: Integrating Sustainability and Process Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 14:08:22","doi":"10.21203/rs.3.rs-4820100/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2610f43b-85de-4ff0-b166-51d78b778d7a","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-07T16:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 14:08:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4820100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4820100","identity":"rs-4820100","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}