Statistical optimization of a solvent-free laminating unit for enhanced adhesion strength

Statistical optimization of a solvent-free laminating unit for enhanced adhesion strength | 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 Statistical optimization of a solvent-free laminating unit for enhanced adhesion strength Ali Rugbani, Sandisile Mgobo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635968/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract The increasing demand for environmentally sustainable and high-performance flexible packaging has accelerated the adoption of solvent-free lamination processes, particularly for multi-layered films such as polyethylene and polyamide bonded with polyurethane adhesives. Achieving optimal adhesion strength (AS) in solvent-free lamination remains challenging due to the complex interplay of processing parameters. This study employs Taguchi’s design of experiments (DOE) methodology to statistically optimize eight key parameters influencing AS, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio. An L 18 orthogonal array was used to reduce experimental runs from 6,561 (full factorial design) to 18 while maintaining balanced parameter representation. Signal-to-noise (S/N) ratio analysis identified surface energy as the most influential factor, followed by machine speed and application temperature. ANOVA confirmed the statistical significance of surface energy (P = 0.047), accounting for 70.25% of the total variance in AS. Linear and quadratic regression models were developed to validate predictive accuracy, yielding R² values of 85.75% and 96.53%, respectively. A confirmation test under the optimized conditions predicted an AS of 646.94 N, closely matching the experimental value of 642 N with an error margin of 0.76%. The results demonstrate the effectiveness of Taguchi-based optimization and regression modeling in improving adhesion performance while minimizing experimental effort in SF lamination systems. Design of experiments Solvent-free lamination Statistical optimization Adhesion strength Surface Energy Figures Figure 1 Figure 2 Figure 3 1. Introduction The increasing demand for high-barrier polymer structures in the flexible packaging industry has driven organizations to optimize processing conditions, focusing on advanced materials and technologies to enhance barrier performance [ 1 – 3 ]. Modern packaging polymers are required to deliver effective barriers against oxygen and light, possess heat sealability, and maintain controlled water vapor permeability—functionalities that often necessitate multilayer or composite structures due to the limitations of single polymer [ 1 , 2 ]. These attributes are critical to prolong the shelf life of packaging materials and prevent product loss after lamination, as evidenced by recent advancements in active and biodegradable packaging solutions that enhance barrier properties and maintain product quality [ 4 – 7 ]. Lamination is therefore widely used to combine different polymer films and achieve the desired functionality. These material properties not only influence product integrity but also determine the choice of lamination method applied during packaging. Flexible packaging commonly employs three types of adhesive lamination: solvent-based, water-based, and solvent-free. Among these, solvent-free lamination has gained prominence in recent years due to its elimination of solvent emissions during processing, aligning with environmental and safety objectives [ 8 ]. Given the increasing demand for high-strength, high-performance laminated polymers, optimizing the solvent-free lamination process is essential to reduce process variation and enhance productivity. Recent advancements in adhesive chemistry, equipment design, and process control have significantly improved the efficiency and sustainability of solvent-free lamination, making it a preferred choice in flexible packaging applications [ 9 ]. Design of experiments (DOE) techniques, particularly the Taguchi method, offer a systematic approach to multivariate optimization. The Taguchi DOE approach is efficient in reducing time and cost, and it minimizes the sensitivity of output variables to uncontrolled noise factors [ 10 , 11 ]. The Taguchi orthogonal array (OA) has been widely applied to optimize production processes across various industries. For example, Ayyildiz et al. [ 10 ] used a Taguchi L 18 array to optimize surface roughness in drilling medium-density fiberboard. In that study, two parameters at three levels and one parameter at two levels were investigated (18 experiments total). Both linear and quadratic regression models were developed, and the feed rate was found to be the most significant factor (50.10% contribution) [ 10 ]. Akgün and Kara [ 11 ] applied a Taguchi OA to study the effect of tool coating parameters on the turning of AA 6061 alloy. They investigated three parameters at three levels and one parameter at two levels using a ‘smaller-is-better’ criterion. Their results demonstrated the effectiveness of the Taguchi method in optimizing the process [ 11 ]. Kara et al. [ 12 ] examined the effect of process parameters and their performance on grinding of cryogenically treated AISI 5140 steel process. They used a Taguchi based L 18 OA and developed linear and quadratic regression models, both achieving R2 above 95%. They found that optimal conditions were achieved for specimens cryogenically treated for 30 hours. Similarly, Ozbek et al. [ 13 ] investigated cutting parameters in the turning of AISI P20 die steel using a Taguchi L 18 design, and identified feed rate as the most statistically significant factor [ 13 ]. Recent studies have applied the Taguchi method to optimize surface roughness in polymer processing. For instance, Maidin et al. [ 14 ] utilized a Taguchi DOE approach to optimize surface roughness in Fused Deposition Modeling (FDM) of ABS polymers, identifying flow rate and layer height as significant factors [ 14 ]. Similarly, Barragán-Trinidad et al. [ 15 ] applied the Taguchi method to optimize the photo-Fenton process for wastewater treatment, achieving significant improvements in chemical oxygen demand (COD) removal [ 15 ]. Research showed the potential of predictive modeling in optimizing process outputs under varied parameter settings, as demonstrated in similar applications involving dry-jet wet spinning processes [ 16 ]. In summary, the Taguchi method has proven effective for optimizing a variety of manufacturing processes. However, its application to solvent-free film lamination processes has not been extensively reported. This study applies a Taguchi L 18 DOE combined with Analysis of variance (ANOVA) and regression modeling to optimize the solvent-free lamination process parameters to enhance the adhesion strength (AS) between polyamide (PA) and polyethylene (PE) films. 2. Material and Methods 2.1. Materials The solvent-free lamination process was applied to PA and PE films. The PA film had a constant surface energy of 48 dyne/cm, while the PE film’s surface energy was varied at three different levels (38 and 42 dyne/cm) [ 17 ]. Lamination between the two films was facilitated by applying a solvent-free two-component adhesive system (polyurethane resin + hardener) using an application roller. This Polyurethane-based solvent-free adhesive is commonly used in flexible packaging due to their strong bonding characteristics and environmental compliance [ 18 ]. Bonding of the PA and PE films was performed using a duplex SL 450/600 HD laminating machine. The effect of process parameters on the AS between the bonded films was investigated. Figure 1 illustrates the configuration of the materials in the lamination process. The PE film was prepared in-house using a 50:50 blend of linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) resins (supplier: Sasol Pty Ltd). Prior to lamination, the PE surface was corona-treated to achieve a minimum surface energy of 40 dyne/cm. The PA film was supplied by Kolon (Kyungnam), and had a surface energy of 48 dyne/cm after treatment. To ensure optimal lamination conditions, both films were stored at a minimum temperature of 20°C and were wrapped in plastic film to prevent contamination and moisture uptake. The PA film was further stored in wooden boxes with silica gel to limit moisture absorption. Both PE and PA films were used within one week of corona treatment to prevent surface energy decay and additive migration. The polyurethane resin and hardener were procured from Morchem Ltd. 2.2. Experimental procedure Before running experiments, the effectiveness of the corona treatment was verified by assessing the surface energy of the treated films using the wetting tension method with ethyl cellosolve and formamide, following ASTM D2578 standard procedures [ 19 ]. The laminating system was then loaded with the PE and PA films (with PE on unwind 2 and PA on unwind 1 of the laminator). To ensure consistent adhesive application, the gap between the adhesive application rollers was set using a gauge dial indicator. The AS measurement tests were conducted using the Instron 5969 system in accordance with ISO 527-3 procedures for plastic films [ 20 ]. A laminated sample 10 x 10 mm of PA and PE material was produced and subjected to the curing process, with the PA side placed on the upper gripper and the PE side on the lower gripper, as illustrated in Fig. 2 . A laminated sample 10 x 10 mm of PA and PE material was produced and subjected to the curing process, with the PA side placed on the upper gripper and the PE side on the lower gripper, as illustrated in Fig. 2 . All AS measurements were taken after a 48-hour curing period to allow the polyurethane adhesive to fully react. All experiments were performed in three replications to ensure reliability. For each run, the measured AS values were averaged and converted to a signal-to-noise (S/N) ratio (using the ‘larger-is-better’ criterion). The response variables (AS, tensile strength, and S/N ratio) were calculated using Minitab 17 statistical software. The selection of parameter levels was grounded in both industrial practice and material behavior under solvent-free lamination conditions. Application temperature (AV) and Curing Temperature (CT) were chosen to span the range where polyurethane adhesives exhibit optimal viscosity and curing characteristics, ensuring uniform application and strong intermolecular bonding. Given PU’s non-Newtonian and moisture-sensitive nature, AV values between 35°C and 55°C and a CT above 15°C were selected to avoid premature curing or delamination. Machine Speed (MS) levels (150, 180 and 200 m/min) reflect practical operating conditions in commercial laminating units and help assess the influence of dynamic processing speed on bond uniformity. For Rewind Tension (RT) and Taper Tension (TT), ranges were selected to control substrate elasticity and compensate for the inherently low green strength of solvent-free adhesives. These tensions directly influence the integrity of the laminate during and after application. Coating Weight (CW) levels (1.5 to 2.5 gsm) were chosen based on standard adhesive distribution ranges to evaluate the trade-off between sufficient wetting and excessive build-up. Collectively, these parameters and their levels are representative of realistic manufacturing conditions and are expected to provide a meaningful understanding of their individual and combined effects on AS. Finally, Mix Ratio (MR) between the PU adhesive and its hardener (80/20) was chosen based on supplier specifications to ensure optimal chemical reactivity and bond strength. Deviations from this ratio cause degradation in adhesive properties and increased variation in AS. Three levels were chosen for most parameters to capture non-linear effects in the lamination process except for CT, which was assigned two levels. The eight process parameters and their levels are listed in Table 1 . The L 18 (3 7 ×2 1 ) OA require only 18 runs to cover all parameter combinations in a balanced manner. Table 1 Process parameters and their levels used in the L 18 Taguchi design. Parameter Levels Units 1 2 3 Curing temperature (CT) 28 32 - o C Rewind tension (RT) 80 100 120 N Taper tension (TT) 15 25 35 % Surface energy (SE) 40 42 44 dynes/cm Coating weight (CW) 1.5 2.0 2.5 gsm Machine speed (MS) 150 180 200 m/min Application temperature (AV) 35 45 55 o C Mix ratio (MR) 75 85 95 % 2.3 Statistical analysis ANOVA was used to quantify the effect and percentage contribution of each input variable on the output response (AS). Minitab 17 software was used to perform the ANOVA and to develop predictive regression equations. The total sum of squares (SST) of the response is given by: SST = \(\:\sum\:_{i-1}^{n}\sum\:_{j-1}^{r}{{Y}^{2}}_{ij}\) - nr \(\:\stackrel{-}{{Y}^{2}}\) (1) Where: n = number of experiments, r = experiment data, \(\:\stackrel{-}{Y}\) = grand mean of all AS values. Sum of the squares (SS) for each factor is demonstrated by the below equation: SS = \(\:\frac{nr}{L}\) \(\:\sum\:_{k=1}^{L}(\stackrel{-}{{Y}_{k}}\) - \(\:\stackrel{-}{Y}\) ) (2) Where: k = parameter level; \(\:{Y}_{k}\) = bond strength response values. Percentage contribution = \(\:\frac{\:\text{S}\text{S}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{S}\text{S}}\) (3) F-ratio is used to ascertain the significance of the variables, it defined as a ratio of variances. F-ratio = \(\:\frac{\text{V}\text{a}\text{r}\text{i}\text{a}\text{n}\text{c}\text{e}}{\text{V}\text{a}\text{r}\text{i}\text{a}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{e}\text{r}\text{r}\text{o}\text{r}\left(\text{V}\text{e}\right)}\) (4) Where the Variance = SS/DoF. Taguchi method developed three ways of categorising quality, namely: the lower the better, the nominal the better, and the higher the better [ 21 ]. For the purpose of this study, higher the better was used since a high bond strength is required between the two films. S/N = – 10 log 10 \(\:\:\left(\frac{1}{n}\sum\:_{i=1}^{n}\frac{1}{{y}_{i}^{2}}\right)\) (5) Where y i is the experimental response variable, and n is the number of tests in the trial. The ANOVA partitions this total variance into contributions from each parameter and from error. Regression models (linear and quadratic) were fitted to the experimental data to enable prediction of AS as a function of the process parameters. 3. Results and Discussion The effect of each parameter on AS was evaluated by analyzing the S/N ratios of the AS responses, as summarized in Fig. 3 . For each run, AS was measured using the Instron machine. The runs, and the corresponding AS values and S/N ratios are presented in the L 18 OA in Table 2 . Each experiment was replicated three times to ensure the reliability of the results. Table 2 L 18 OA, measured AS and S/N ratios. Run CT RT TT SE CW MS AV MR AS (N) S/N AS 1 1 1 1 1 1 1 1 1 350 50.88 2 1 1 2 2 2 2 2 2 490 53.80 3 1 1 3 3 3 3 3 3 355 51.00 4 1 2 1 1 2 2 3 3 370 51.36 5 1 2 2 2 3 3 1 1 460 53.26 6 1 2 3 3 1 1 2 2 640 56.12 7 1 3 1 2 1 3 2 3 450 53.06 8 1 3 2 3 2 1 3 1 550 54.81 9 1 3 3 1 3 2 1 2 300 49.54 10 2 1 1 3 3 2 2 1 565 55.04 11 2 1 2 1 1 3 3 2 300 49.54 12 2 1 3 2 2 1 1 3 445 52.97 13 2 2 1 2 3 1 3 1 440 52.87 14 2 2 2 3 1 2 1 2 590 55.42 15 2 2 3 1 2 3 2 3 300 49.54 16 2 3 1 3 2 3 1 1 560 54.96 17 2 3 2 1 3 1 2 2 380 51.60 18 2 3 3 2 1 2 3 3 450 53.06 The S/N ratio analysis indicated that SE has a directly proportional relationship with AS. The maximum AS observed was 640 N (in run 6, SE = 44 dyne/cm) corresponding to S/N ratio of 56.12. The minimum AS observed was 300 N with the lowest S/N ratio of 49.54, recorded in runs 9, 11, and 15, despite having the same SE = 44 dyne/cm, indicating the influence of interactions with other parameters. Figure 3 illustrates the main effects of each parameter on the S/N ratio. It clearly shows that SE has the most significant and positive influence on AS, followed by MS and AV. The S/N analysis results are summarized in Table 3 . Higher S/N values correspond to better performance. The “Delta” row (range of means) and “Rank” indicate the relative importance of each parameter. SE has the largest range (Delta = 4.15) and is ranked 1st, confirming its dominant influence on AS. MS and AV followed in second and third place, respectively. Table 3 Mean S/N ratios for AS at each parameter level (larger-is-better criterion). Level CT RT TT SE CW MS AV MR 1 52.65 52.21 53.03 50.41 53.02 53.21 52.84 52.77 2 52.78 53.10 53.07 53.17 52.91 53.04 53.20 52.81 3 52.84 52.04 54.56 52.22 51.90 52.11 52.57 Delta 0.13 0.89 1.03 4.15 0.80 1.31 1.09 0.24 Rank 8 5 4 1 6 2 3 7 Based on the S/N ratio analysis presented in Table 3 , the optimum operating conditions for achieving high AS are: based on S/N ratio analysis, were: CT = 32°C (level 2), RT = 100 N (level 2), TT = 25% (level 2), SE = 44 dyne/cm (level 3), CW = 1.5 gsm (level 1), MS = 150 m/min (level 1), AV = 45°C (level 2), and MR = 85% (level 2). A confirmation test was carried out since the optimal combination was not among the original experimental runs. The predicted AS value was calculated using the additive model (Eq. 6), which combines the effects of optimal levels for each parameter relative to the mean AS (AT AS ). \(\:{\text{A}\text{S}}_{\text{o}\text{p}\text{t}}=\:{\text{A}\text{T}}_{\text{A}\text{S}}+({\text{C}\text{T}}_{2}-\:{\text{A}\text{T}}_{\text{A}\text{S}}\) ) + ( \(\:{\text{R}\text{T}}_{2}-{\text{A}\text{T}}_{\text{A}\text{S}})\) + ( \(\:{\text{T}\text{T}}_{2}-\:{\text{A}\text{T}}_{\text{A}\text{S}})+({\text{S}\text{E}}_{3}-\:{\text{A}\text{T}}_{\text{A}\text{S}})+({\text{C}\text{W}}_{1}\) - \(\:{\text{A}\text{T}}_{\text{A}\text{S}}\) ) + ( \(\:{\text{M}\text{S}}_{1}{-\text{A}\text{T}}_{\text{A}\text{S}}\) ) + ( \(\:{\text{A}\text{V}}_{1}\) - \(\:{\text{A}\text{T}}_{\text{A}\text{S}}\) ) + ( \(\:{\text{M}\text{R}}_{2}\) - \(\:{\text{A}\text{T}}_{\text{A}\text{S}}\) ) (6) ANOVA was performed to quantify the contribution of each parameter to the variation in AS. Results are shown in Table 4 . Table 4 ANOVA for AS. Source Degree of Freedom Sum of squares Mean of squares F P Contribution (%) CT 1 235 234.7 0.07 0.814 0.12 RT 2 7408 3704.2 1.12 0.471 3.89 TT 2 7758 3879.2 1.18 0.459 4.08 SE 2 133525 66762.5 20.27 0.047 70.25 CW 2 7158 3579.2 1.09 0.479 3.76 MS 2 14533 7266.7 2.21 0.312 7.64 AV 2 11200 5600.0 1.70 0.370 5.89 MR 2 1658 829.2 0.25 0.799 0.87 Residual Error 2 6586 3293.1 3.46 Total 17 190062 100 As shown in Table 4 . SE was the most significant factor, accounting for 70.25% of the total variation (F = 20.27, P = 0.047). which is statistically significant at the 95% confidence level [ 22 ]. While SE accounted for the highest variance, the influence of other parameters should not be overlooked, particularly given their potential interaction effects and relevance in real-world manufacturing settings where process robustness is critical. MS and AV were the next most influential factors, contributing 7.64% and 5.89%, respectively. Although their P-values exceeded 0.05, their combined contribution of 13.53% highlights their practical importance in process fine-tuning. The residual error was low (3.46%), indicating that the model captured most of the variability in AS. These findings were consistent with the results of the S/N analysis. Regression analysis was also performed. The linear model (Eq. 7) explained 85.75% of the variance in AS (adjusted R² = 73.09%), while the quadratic model (Eq. 8), which included interaction and squared terms, yielded a higher R² of 96.53%. AS = − 1388 + 1.81CT + 0.771RT – 2.04TT + 52.50SE – 46.7CW – 1.184MS – 2.00AV –0.71MR (7) AS q = − 13763 + 1.81CT + 17.6RT + 11.1TT + 420SE + 153CW + 17MS + 34AV + 26.9MR – 0.084RT 2 – 0.26TT 2 – 4.37SE 2 – 50CW 2 – 0.052MS 2 – 0.4AV 2 – 0.16MR 2 (8) A confirmation test was carried out since the optimum parameters obtained were not part of the Taguchi OA. Remarkably, the optimal operating parameters were achieved with a significantly fewer number of experiments (18 experiments) than in a traditional full factorial design (FFD), where a high number of experiments (3 7 ×2 1 = 4374) would have been necessary to consider all possible levels and combinations of parameters. A confirmation test was carried out under the optimal conditions identified by the Taguchi analysis. The Taguchi-based predicted AS under these conditions was 646.94 N, very close to the experimental result of 642 N (0.76% error). Table 5 presents a comparison between experimental and predicted AS values for two key cases: run 6 and the optimum condition. Values from the Taguchi model, linear regression, and quadratic regression are shown. Table 5 Experimental and predicted AS under optimal conditions. Taguchi method Linear regression method Quadratic regression experimental pred. error % pred. error % pred. error % Run 6 640 613.05 4.21 580.38 9.32 644.86 0.76 Optimum 642 646.94 0.76 608.02 5.29 697.10 8.58 The results confirm that the Taguchi model provided the most accurate prediction at the optimum conditions, with an error of just 0.76%. The quadratic regression model also performed well for Run 6, though its prediction for the optimum condition was less precise, with an 8.58% error. The linear regression model showed larger discrepancies in both cases. These findings reinforce the robustness of the Taguchi method in validating and enhancing process optimization. The regression model results further support the predictive capability of the Taguchi-derived model, aligning with prior findings in engineered polymer applications [ 16 ]. Importantly, the Taguchi OA enabled determination of the optimal parameter set with only 18 experimental runs. In contrast, a full factorial design considering all parameters combinations would have been prohibitively expensive and time-consuming. This highlights the efficiency and cost-effectiveness of the Taguchi approach in optimizing complex lamination processes. 4. Conclusion This study successfully applied Taguchi's design of experiments and regression analysis modeling to statistically optimize key processing parameters in a solvent-free lamination system using polyurethane adhesives for flexible packaging applications. Through an L18 OA, eight critical parameters, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio were efficiently evaluated with only 18 experimental runs. SE was found to be the most influential factor affecting AS, accounting for more than 70% of the total variance as confirmed by ANOVA. Though, the roles of the other parameters, MS and AV, which contributed 7.64% and 5.89% respectively, should not be overlooked. Their combined influence of 13.53% underscores the importance of fine-tuning secondary parameters to ensure consistent and reliable bonding performance in industrial environments. Regression modeling further validated the process, with the quadratic model achieving an R² of 96.53%. The optimized parameter combination yielded a predicted AS of 646.94 N, closely matching the experimental value of 642 N, with a minimal error margin of 0.76%. These results provide valuable insights into the factors affecting adhesion in solvent-free lamination and demonstrate how process parameters can be tuned to improve performance. The optimized parameters can be directly used to set up laminating units for improved product quality. Furthermore, this optimization framework can be readily extended to other lamination and coating systems involving multi-material bonding, particularly where process efficiency, material performance, and sustainability are critical. Statements and Declarations Funding The financial support from Amcor Flexible South Africa (AFSA) and Cape Peninsula University of Technology (CPUT) is highly appreciated and acknowledged . Competing Interests The authors have no relevant financial or non-financial interests to disclose. References S. Yue, T. Zhang, S. Wang, D. Han, S. Huang, M. Xiao, and Y. Meng, “Recent progress of biodegradable polymer package materials: Nanotechnology improving both oxygen and water vapor barrier performance,” Nanomaterials , vol. 14, no. 4, p. 338, 2024, doi: 10.3390/nano14040338. R. K. Deshmukh, S. Tripathi, S. Bisht, P. Kumar, T. Patil, and K. K. Gaikwad, “Mucilage-based composite films and coatings for food packaging application: A review,” Int. J. Biol. 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Hoboken, NJ, USA: Wiley-Interscience, 2005, ISBN: 978-0471413349. Scribbr, “Understanding p-values: Definition and examples,” Scribbr, 2023. [Online]. Available: https://www.scribbr.com/statistics/p-value/ Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 22 Dec, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 18 May, 2025 First submitted to journal 13 May, 2025 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. 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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-6635968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458714226,"identity":"73ea7293-0fca-4f52-983b-3fd10e6183d9","order_by":0,"name":"Ali Rugbani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYHACNiCWYOAnXYtkA5A6QIIWBgaDA8RqMZ+Rfu3Bzx0W8sbHzx6T/sBgJ8/Af/gBXi0yN3LKDXvPSBhuO5OXJnGAIdmwgeGYAV4tEhI5aRK8bRKM227wmAG1MCcwMDYQ1iL5t03CfvMMsJb6BAZm9g8EtKQfkwbakrhBAqzlcAIDGw8BW3jesBvLtkkkzziTl2xxxuC4YRsPTwF+Lezpzx6+bauz7W8/e/BGRUW1PD//8Q14tTAwwJ3BA8QGsGjCC9gfIGkZBaNgFIyCUYAFAAAQWT222PNi0wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9899-639X","institution":"Cape Peninsula University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Rugbani","suffix":""},{"id":458714227,"identity":"46ca5423-4e74-498a-a5a8-c7f91aa7e884","order_by":1,"name":"Sandisile Mgobo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sandisile","middleName":"","lastName":"Mgobo","suffix":""}],"badges":[],"createdAt":"2025-05-10 16:46:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6635968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6635968/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-026-17647-z","type":"published","date":"2026-02-11T15:58:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83194164,"identity":"d0b90b91-cfb3-44d1-9f28-a9fe33bc182e","added_by":"auto","created_at":"2025-05-21 04:40:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16599,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the material configuration for lamination.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6635968/v1/84b06037ed8702617c52c33a.png"},{"id":83194163,"identity":"9de7a1e7-dd1d-4a9b-a00c-db87c4948542","added_by":"auto","created_at":"2025-05-21 04:40:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":387172,"visible":true,"origin":"","legend":"\u003cp\u003eShows PA and PE material placed on the Instron Grippers\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6635968/v1/06151038691ca6b3f27a4d07.png"},{"id":83194233,"identity":"6e3a0bdb-8527-4d1f-8653-9960abc04a3e","added_by":"auto","created_at":"2025-05-21 04:48:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40677,"visible":true,"origin":"","legend":"\u003cp\u003eMain effects of process parameters on S/N ratio for AS.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6635968/v1/27c01b566c57c0d5c2b51d8e.png"},{"id":102786514,"identity":"74134c2e-1157-47d9-9491-36a24714218f","added_by":"auto","created_at":"2026-02-16 16:13:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287603,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6635968/v1/cdeb9e3b-492f-4a0b-b240-30ecb68f2dfb.pdf"}],"financialInterests":"","formattedTitle":"Statistical optimization of a solvent-free laminating unit for enhanced adhesion strength","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increasing demand for high-barrier polymer structures in the flexible packaging industry has driven organizations to optimize processing conditions, focusing on advanced materials and technologies to enhance barrier performance [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Modern packaging polymers are required to deliver effective barriers against oxygen and light, possess heat sealability, and maintain controlled water vapor permeability\u0026mdash;functionalities that often necessitate multilayer or composite structures due to the limitations of single polymer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These attributes are critical to prolong the shelf life of packaging materials and prevent product loss after lamination, as evidenced by recent advancements in active and biodegradable packaging solutions that enhance barrier properties and maintain product quality [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Lamination is therefore widely used to combine different polymer films and achieve the desired functionality.\u003c/p\u003e \u003cp\u003eThese material properties not only influence product integrity but also determine the choice of lamination method applied during packaging. Flexible packaging commonly employs three types of adhesive lamination: solvent-based, water-based, and solvent-free. Among these, solvent-free lamination has gained prominence in recent years due to its elimination of solvent emissions during processing, aligning with environmental and safety objectives [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given the increasing demand for high-strength, high-performance laminated polymers, optimizing the solvent-free lamination process is essential to reduce process variation and enhance productivity. Recent advancements in adhesive chemistry, equipment design, and process control have significantly improved the efficiency and sustainability of solvent-free lamination, making it a preferred choice in flexible packaging applications [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDesign of experiments (DOE) techniques, particularly the Taguchi method, offer a systematic approach to multivariate optimization. The Taguchi DOE approach is efficient in reducing time and cost, and it minimizes the sensitivity of output variables to uncontrolled noise factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Taguchi orthogonal array (OA) has been widely applied to optimize production processes across various industries. For example, Ayyildiz et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] used a Taguchi L\u003csub\u003e18\u003c/sub\u003e array to optimize surface roughness in drilling medium-density fiberboard. In that study, two parameters at three levels and one parameter at two levels were investigated (18 experiments total). Both linear and quadratic regression models were developed, and the feed rate was found to be the most significant factor (50.10% contribution) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAkg\u0026uuml;n and Kara [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] applied a Taguchi OA to study the effect of tool coating parameters on the turning of AA 6061 alloy. They investigated three parameters at three levels and one parameter at two levels using a \u0026lsquo;smaller-is-better\u0026rsquo; criterion. Their results demonstrated the effectiveness of the Taguchi method in optimizing the process [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Kara et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] examined the effect of process parameters and their performance on grinding of cryogenically treated AISI 5140 steel process. They used a Taguchi based L\u003csub\u003e18\u003c/sub\u003e OA and developed linear and quadratic regression models, both achieving R2 above 95%. They found that optimal conditions were achieved for specimens cryogenically treated for 30 hours. Similarly, Ozbek et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] investigated cutting parameters in the turning of AISI P20 die steel using a Taguchi L\u003csub\u003e18\u003c/sub\u003e design, and identified feed rate as the most statistically significant factor [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have applied the Taguchi method to optimize surface roughness in polymer processing. For instance, Maidin et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] utilized a Taguchi DOE approach to optimize surface roughness in Fused Deposition Modeling (FDM) of ABS polymers, identifying flow rate and layer height as significant factors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, Barrag\u0026aacute;n-Trinidad et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] applied the Taguchi method to optimize the photo-Fenton process for wastewater treatment, achieving significant improvements in chemical oxygen demand (COD) removal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research showed the potential of predictive modeling in optimizing process outputs under varied parameter settings, as demonstrated in similar applications involving dry-jet wet spinning processes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, the Taguchi method has proven effective for optimizing a variety of manufacturing processes. However, its application to solvent-free film lamination processes has not been extensively reported. This study applies a Taguchi L\u003csub\u003e18\u003c/sub\u003e DOE combined with Analysis of variance (ANOVA) and regression modeling to optimize the solvent-free lamination process parameters to enhance the adhesion strength (AS) between polyamide (PA) and polyethylene (PE) films.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003eThe solvent-free lamination process was applied to PA and PE films. The PA film had a constant surface energy of 48 dyne/cm, while the PE film\u0026rsquo;s surface energy was varied at three different levels (38 and 42 dyne/cm) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Lamination between the two films was facilitated by applying a solvent-free two-component adhesive system (polyurethane resin\u0026thinsp;+\u0026thinsp;hardener) using an application roller. This Polyurethane-based solvent-free adhesive is commonly used in flexible packaging due to their strong bonding characteristics and environmental compliance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBonding of the PA and PE films was performed using a duplex SL 450/600 HD laminating machine. The effect of process parameters on the AS between the bonded films was investigated. Figure\u0026nbsp;1 illustrates the configuration of the materials in the lamination process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PE film was prepared in-house using a 50:50 blend of linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) resins (supplier: Sasol Pty Ltd). Prior to lamination, the PE surface was corona-treated to achieve a minimum surface energy of 40 dyne/cm. The PA film was supplied by Kolon (Kyungnam), and had a surface energy of 48 dyne/cm after treatment.\u003c/p\u003e \u003cp\u003eTo ensure optimal lamination conditions, both films were stored at a minimum temperature of 20\u0026deg;C and were wrapped in plastic film to prevent contamination and moisture uptake. The PA film was further stored in wooden boxes with silica gel to limit moisture absorption. Both PE and PA films were used within one week of corona treatment to prevent surface energy decay and additive migration. The polyurethane resin and hardener were procured from Morchem Ltd.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental procedure\u003c/h2\u003e \u003cp\u003eBefore running experiments, the effectiveness of the corona treatment was verified by assessing the surface energy of the treated films using the wetting tension method with ethyl cellosolve and formamide, following ASTM D2578 standard procedures [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The laminating system was then loaded with the PE and PA films (with PE on unwind 2 and PA on unwind 1 of the laminator). To ensure consistent adhesive application, the gap between the adhesive application rollers was set using a gauge dial indicator.\u003c/p\u003e \u003cp\u003eThe AS measurement tests were conducted using the Instron 5969 system in accordance with ISO 527-3 procedures for plastic films [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A laminated sample 10 x 10 mm of PA and PE material was produced and subjected to the curing process, with the PA side placed on the upper gripper and the PE side on the lower gripper, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA laminated sample 10 x 10 mm of PA and PE material was produced and subjected to the curing process, with the PA side placed on the upper gripper and the PE side on the lower gripper, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll AS measurements were taken after a 48-hour curing period to allow the polyurethane adhesive to fully react. All experiments were performed in three replications to ensure reliability. For each run, the measured AS values were averaged and converted to a signal-to-noise (S/N) ratio (using the \u0026lsquo;larger-is-better\u0026rsquo; criterion). The response variables (AS, tensile strength, and S/N ratio) were calculated using Minitab 17 statistical software.\u003c/p\u003e \u003cp\u003eThe selection of parameter levels was grounded in both industrial practice and material behavior under solvent-free lamination conditions. Application temperature (AV) and Curing Temperature (CT) were chosen to span the range where polyurethane adhesives exhibit optimal viscosity and curing characteristics, ensuring uniform application and strong intermolecular bonding. Given PU\u0026rsquo;s non-Newtonian and moisture-sensitive nature, AV values between 35\u0026deg;C and 55\u0026deg;C and a CT above 15\u0026deg;C were selected to avoid premature curing or delamination.\u003c/p\u003e \u003cp\u003eMachine Speed (MS) levels (150, 180 and 200 m/min) reflect practical operating conditions in commercial laminating units and help assess the influence of dynamic processing speed on bond uniformity. For Rewind Tension (RT) and Taper Tension (TT), ranges were selected to control substrate elasticity and compensate for the inherently low green strength of solvent-free adhesives. These tensions directly influence the integrity of the laminate during and after application.\u003c/p\u003e \u003cp\u003eCoating Weight (CW) levels (1.5 to 2.5 gsm) were chosen based on standard adhesive distribution ranges to evaluate the trade-off between sufficient wetting and excessive build-up. Collectively, these parameters and their levels are representative of realistic manufacturing conditions and are expected to provide a meaningful understanding of their individual and combined effects on AS.\u003c/p\u003e \u003cp\u003eFinally, Mix Ratio (MR) between the PU adhesive and its hardener (80/20) was chosen based on supplier specifications to ensure optimal chemical reactivity and bond strength. Deviations from this ratio cause degradation in adhesive properties and increased variation in AS.\u003c/p\u003e \u003cp\u003eThree levels were chosen for most parameters to capture non-linear effects in the lamination process except for CT, which was assigned two levels. The eight process parameters and their levels are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The L\u003csub\u003e18\u003c/sub\u003e (3\u003csup\u003e7\u003c/sup\u003e\u0026times;2\u003csup\u003e1\u003c/sup\u003e) OA require only 18 runs to cover all parameter combinations in a balanced manner.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProcess parameters and their levels used in the L\u003csub\u003e18\u003c/sub\u003e Taguchi design.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuring temperature (CT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRewind tension (RT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaper tension (TT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface energy (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edynes/cm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoating weight (CW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egsm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachine speed (MS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003em/min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplication temperature (AV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMix ratio (MR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\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 Statistical analysis\u003c/h2\u003e \u003cp\u003eANOVA was used to quantify the effect and percentage contribution of each input variable on the output response (AS). Minitab 17 software was used to perform the ANOVA and to develop predictive regression equations.\u003c/p\u003e \u003cp\u003eThe total sum of squares (SST) of the response is given by:\u003c/p\u003e \u003cp\u003eSST = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i-1}^{n}\\sum\\:_{j-1}^{r}{{Y}^{2}}_{ij}\\)\u003c/span\u003e\u003c/span\u003e - \u003cem\u003enr\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{Y}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of experiments, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;experiment data, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{Y}\\)\u003c/span\u003e\u003c/span\u003e = grand mean of all AS values.\u003c/p\u003e \u003cp\u003eSum of the squares (SS) for each factor is demonstrated by the below equation:\u003c/p\u003e \u003cp\u003eSS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{nr}{L}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{k=1}^{L}(\\stackrel{-}{{Y}_{k}}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{Y}\\)\u003c/span\u003e\u003c/span\u003e) (2)\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;parameter level; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{k}\\)\u003c/span\u003e\u003c/span\u003e = bond strength response values.\u003c/p\u003e \u003cp\u003ePercentage contribution = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\:\\text{S}\\text{S}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{S}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e(3)\u003c/p\u003e \u003cp\u003eF-ratio is used to ascertain the significance of the variables, it defined as a ratio of variances.\u003c/p\u003e \u003cp\u003eF-ratio = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{V}\\text{a}\\text{r}\\text{i}\\text{a}\\text{n}\\text{c}\\text{e}}{\\text{V}\\text{a}\\text{r}\\text{i}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\left(\\text{V}\\text{e}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003eWhere the Variance\u0026thinsp;=\u0026thinsp;SS/DoF.\u003c/p\u003e \u003cp\u003eTaguchi method developed three ways of categorising quality, namely: the lower the better, the nominal the better, and the higher the better [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For the purpose of this study, higher the better was used since a high bond strength is required between the two films.\u003c/p\u003e \u003cp\u003eS/N = \u0026ndash; 10 log\u003csub\u003e10\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\left(\\frac{1}{n}\\sum\\:_{i=1}^{n}\\frac{1}{{y}_{i}^{2}}\\right)\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the experimental response variable, and \u003cem\u003en\u003c/em\u003e is the number of tests in the trial.\u003c/p\u003e \u003cp\u003eThe ANOVA partitions this total variance into contributions from each parameter and from error. Regression models (linear and quadratic) were fitted to the experimental data to enable prediction of AS as a function of the process parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe effect of each parameter on AS was evaluated by analyzing the S/N ratios of the AS responses, as summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For each run, AS was measured using the Instron machine. The runs, and the corresponding AS values and S/N ratios are presented in the L\u003csub\u003e18\u003c/sub\u003e OA in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each experiment was replicated three times to ensure the reliability of the results.\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\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eL\u003csub\u003e18\u003c/sub\u003e OA, measured AS and S/N ratios.\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\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAS (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003cp\u003eAS\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\u003e1\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e50.88\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\u003e1\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53.80\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\u003e1\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e51.00\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e51.36\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53.26\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e56.12\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53.06\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e54.81\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49.54\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\u003e2\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e55.04\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\u003e2\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49.54\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\u003e2\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e52.97\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e52.87\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e55.42\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e49.54\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e54.96\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e51.60\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53.06\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 S/N ratio analysis indicated that SE has a directly proportional relationship with AS. The maximum AS observed was 640 N (in run 6, SE\u0026thinsp;=\u0026thinsp;44 dyne/cm) corresponding to S/N ratio of 56.12. The minimum AS observed was 300 N with the lowest S/N ratio of 49.54, recorded in runs 9, 11, and 15, despite having the same SE\u0026thinsp;=\u0026thinsp;44 dyne/cm, indicating the influence of interactions with other parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the main effects of each parameter on the S/N ratio. It clearly shows that SE has the most significant and positive influence on AS, followed by MS and AV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe S/N analysis results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Higher S/N values correspond to better performance. The \u0026ldquo;Delta\u0026rdquo; row (range of means) and \u0026ldquo;Rank\u0026rdquo; indicate the relative importance of each parameter. SE has the largest range (Delta\u0026thinsp;=\u0026thinsp;4.15) and is ranked 1st, confirming its dominant influence on AS. MS and AV followed in second and third place, respectively.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean S/N ratios for AS at each parameter level (larger-is-better criterion).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMR\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\u003e52.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e53.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e53.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.77\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\u003e\u003cb\u003e52.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e53.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e53.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e53.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e52.81\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e54.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.57\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\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\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\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\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\u003eBased on the S/N ratio analysis presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the optimum operating conditions for achieving high AS are: based on S/N ratio analysis, were: CT\u0026thinsp;=\u0026thinsp;32\u0026deg;C (level 2), RT\u0026thinsp;=\u0026thinsp;100 N (level 2), TT\u0026thinsp;=\u0026thinsp;25% (level 2), SE\u0026thinsp;=\u0026thinsp;44 dyne/cm (level 3), CW\u0026thinsp;=\u0026thinsp;1.5 gsm (level 1), MS\u0026thinsp;=\u0026thinsp;150 m/min (level 1), AV\u0026thinsp;=\u0026thinsp;45\u0026deg;C (level 2), and MR\u0026thinsp;=\u0026thinsp;85% (level 2).\u003c/p\u003e \u003cp\u003eA confirmation test was carried out since the optimal combination was not among the original experimental runs. The predicted AS value was calculated using the additive model (Eq.\u0026nbsp;6), which combines the effects of optimal levels for each parameter relative to the mean AS (AT\u003csub\u003eAS\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{S}}_{\\text{o}\\text{p}\\text{t}}=\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}}+({\\text{C}\\text{T}}_{2}-\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}}\\)\u003c/span\u003e \u003c/span\u003e) + (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}\\text{T}}_{2}-{\\text{A}\\text{T}}_{\\text{A}\\text{S}})\\)\u003c/span\u003e\u003c/span\u003e + (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{T}\\text{T}}_{2}-\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}})+({\\text{S}\\text{E}}_{3}-\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}})+({\\text{C}\\text{W}}_{1}\\)\u003c/span\u003e\u003c/span\u003e- \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e) + (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}\\text{S}}_{1}{-\\text{A}\\text{T}}_{\\text{A}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e) + (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{V}}_{1}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e) + (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}\\text{R}}_{2}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{T}}_{\\text{A}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e ) (6)\u003c/p\u003e \u003cp\u003eANOVA was performed to quantify the contribution of each parameter to the variation in AS. Results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for AS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree of Freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eContribution\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e234.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3704.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3879.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66762.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3579.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7266.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5600.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e829.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3293.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. SE was the most significant factor, accounting for 70.25% of the total variation (F\u0026thinsp;=\u0026thinsp;20.27, P\u0026thinsp;=\u0026thinsp;0.047). which is statistically significant at the 95% confidence level [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While SE accounted for the highest variance, the influence of other parameters should not be overlooked, particularly given their potential interaction effects and relevance in real-world manufacturing settings where process robustness is critical.\u003c/p\u003e \u003cp\u003eMS and AV were the next most influential factors, contributing 7.64% and 5.89%, respectively. Although their P-values exceeded 0.05, their combined contribution of 13.53% highlights their practical importance in process fine-tuning.\u003c/p\u003e \u003cp\u003eThe residual error was low (3.46%), indicating that the model captured most of the variability in AS. These findings were consistent with the results of the S/N analysis.\u003c/p\u003e \u003cp\u003eRegression analysis was also performed. The linear model (Eq.\u0026nbsp;7) explained 85.75% of the variance in AS (adjusted R\u0026sup2; = 73.09%), while the quadratic model (Eq.\u0026nbsp;8), which included interaction and squared terms, yielded a higher R\u0026sup2; of 96.53%.\u003c/p\u003e \u003cp\u003eAS = \u0026minus;\u0026thinsp;1388\u0026thinsp;+\u0026thinsp;1.81CT\u0026thinsp;+\u0026thinsp;0.771RT \u0026ndash; 2.04TT\u0026thinsp;+\u0026thinsp;52.50SE \u0026ndash; 46.7CW \u0026ndash; 1.184MS \u0026ndash; 2.00AV\u003c/p\u003e \u003cp\u003e\u0026ndash;0.71MR (7)\u003c/p\u003e \u003cp\u003eAS\u003csub\u003eq\u003c/sub\u003e = \u0026minus;\u0026thinsp;13763\u0026thinsp;+\u0026thinsp;1.81CT\u0026thinsp;+\u0026thinsp;17.6RT\u0026thinsp;+\u0026thinsp;11.1TT\u0026thinsp;+\u0026thinsp;420SE\u0026thinsp;+\u0026thinsp;153CW\u0026thinsp;+\u0026thinsp;17MS\u0026thinsp;+\u0026thinsp;34AV\u0026thinsp;+\u0026thinsp;26.9MR\u003c/p\u003e \u003cp\u003e\u0026ndash; 0.084RT\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.26TT\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 4.37SE\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 50CW\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.052MS\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.4AV\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.16MR\u003csup\u003e2\u003c/sup\u003e (8)\u003c/p\u003e \u003cp\u003eA confirmation test was carried out since the optimum parameters obtained were not part of the Taguchi OA. Remarkably, the optimal operating parameters were achieved with a significantly fewer number of experiments (18 experiments) than in a traditional full factorial design (FFD), where a high number of experiments (3\u003csup\u003e7\u003c/sup\u003e\u0026times;2\u003csup\u003e1\u003c/sup\u003e = 4374) would have been necessary to consider all possible levels and combinations of parameters.\u003c/p\u003e \u003cp\u003eA confirmation test was carried out under the optimal conditions identified by the Taguchi analysis. The Taguchi-based predicted AS under these conditions was 646.94 N, very close to the experimental result of 642 N (0.76% error).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a comparison between experimental and predicted AS values for two key cases: run 6 and the optimum condition. Values from the Taguchi model, linear regression, and quadratic regression are shown.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental and predicted AS under optimal conditions.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTaguchi method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLinear regression method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eQuadratic regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epred.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eerror %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epred.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eerror %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003epred.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eerror %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e613.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e580.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e644.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e608.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e697.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.58\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 results confirm that the Taguchi model provided the most accurate prediction at the optimum conditions, with an error of just 0.76%. The quadratic regression model also performed well for Run 6, though its prediction for the optimum condition was less precise, with an 8.58% error. The linear regression model showed larger discrepancies in both cases. These findings reinforce the robustness of the Taguchi method in validating and enhancing process optimization. The regression model results further support the predictive capability of the Taguchi-derived model, aligning with prior findings in engineered polymer applications [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, the Taguchi OA enabled determination of the optimal parameter set with only 18 experimental runs. In contrast, a full factorial design considering all parameters combinations would have been prohibitively expensive and time-consuming. This highlights the efficiency and cost-effectiveness of the Taguchi approach in optimizing complex lamination processes.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study successfully applied Taguchi's design of experiments and regression analysis modeling to statistically optimize key processing parameters in a solvent-free lamination system using polyurethane adhesives for flexible packaging applications. Through an L18 OA, eight critical parameters, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio were efficiently evaluated with only 18 experimental runs.\u003c/p\u003e \u003cp\u003eSE was found to be the most influential factor affecting AS, accounting for more than 70% of the total variance as confirmed by ANOVA. Though, the roles of the other parameters, MS and AV, which contributed 7.64% and 5.89% respectively, should not be overlooked. Their combined influence of 13.53% underscores the importance of fine-tuning secondary parameters to ensure consistent and reliable bonding performance in industrial environments.\u003c/p\u003e \u003cp\u003eRegression modeling further validated the process, with the quadratic model achieving an R\u0026sup2; of 96.53%. The optimized parameter combination yielded a predicted AS of 646.94 N, closely matching the experimental value of 642 N, with a minimal error margin of 0.76%.\u003c/p\u003e \u003cp\u003eThese results provide valuable insights into the factors affecting adhesion in solvent-free lamination and demonstrate how process parameters can be tuned to improve performance. The optimized parameters can be directly used to set up laminating units for improved product quality. Furthermore, this optimization framework can be readily extended to other lamination and coating systems involving multi-material bonding, particularly where process efficiency, material performance, and sustainability are critical.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial support from Amcor Flexible South Africa (AFSA) and Cape Peninsula University of Technology (CPUT) is highly appreciated and acknowledged\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. Yue, T. Zhang, S. Wang, D. Han, S. Huang, M. Xiao, and Y. 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Vi\u0026ntilde;a, \u0026ldquo;Shelf life of fresh sliced sea bream packed in PET nanocomposite trays,\u0026rdquo; \u003cem\u003eFoods\u003c/em\u003e, vol. 10, no. 12, p. 2974, 2021, doi: 10.3390/foods10122974.\u003c/li\u003e\n\u003cli\u003eRecyClass Technical Committee, \u0026ldquo;Technical review: Laminating adhesives,\u0026rdquo; \u003cem\u003eRecyClass\u003c/em\u003e, Jan. 2023. [Online]. Available: https://recyclass.eu/wp-content/uploads/2023/01/ Technical-Review-Laminating-Adhesives.pdf\u003c/li\u003e\n\u003cli\u003eM. Cook, \u0026ldquo;Reduction of solvents in adhesives,\u0026rdquo; \u003cem\u003ePigment \u0026amp; Resin Technol\u003c/em\u003e., vol. 5, no. 1, pp. 12\u0026ndash;16, 1996.\u003c/li\u003e\n\u003cli\u003eE. A. Ayyildiz, M. Ayyildiz, and F. Kara, \u0026ldquo;Optimization of surface roughness in drilling medium-density fibreboard with a parallel robot,\u0026rdquo; \u003cem\u003eAdv. Mater. Sci. 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Rugbani, \u0026ldquo;Predictive model for diameter control of polysulfone hollow fibers produced by dry-jet wet spinning,\u0026rdquo; \u003cem\u003eJ. Comput. Appl. Res. Mech. Eng\u003c/em\u003e., vol. 13, no. 1, pp. 27\u0026ndash;38, 2023, doi: 10.22061/jcarme.2023.9255.2239.\u003c/li\u003e\n\u003cli\u003eF. Hild, \u0026ldquo;Surface energy of plastics,\u0026rdquo; Tstar, 2021. [Online]. Available: https://www.tstar.com/blog/bid/33845/surface-energy-of-plastics\u003c/li\u003e\n\u003cli\u003eHenkel Adhesives, \u0026ldquo;Laminating adhesives,\u0026rdquo; Henkel, 2023. [Online]. Available: https://www.henkel-adhesives.com/ni/en/products/industrial-adhesives/laminating-adhesives.html\u003c/li\u003e\n\u003cli\u003eASTM D2578-17, \u003cem\u003eStandard Test Method for Wetting Tension of Polyethylene and Polypropylene Films\u003c/em\u003e, ASTM International, West Conshohocken, PA, USA, 2017. [Online]. Available: https://www.astm.org/d2578-17.html\u003c/li\u003e\n\u003cli\u003eInstron, \u0026ldquo;ISO 527-3 tensile properties of films and sheets,\u0026rdquo; Instron, 2023. [Online]. Available: https://www.instron.com/en/testing-solutions/iso-standards/iso-527-3\u003c/li\u003e\n\u003cli\u003eG. Taguchi, S. Chowdhury, and Y. Wu, \u003cem\u003eTaguchi Quality Engineering Handbook\u003c/em\u003e. Hoboken, NJ, USA: Wiley-Interscience, 2005, ISBN: 978-0471413349.\u003c/li\u003e\n\u003cli\u003eScribbr, \u0026ldquo;Understanding p-values: Definition and examples,\u0026rdquo; Scribbr, 2023. [Online]. Available: https://www.scribbr.com/statistics/p-value/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Design of experiments, Solvent-free lamination, Statistical optimization, Adhesion strength, Surface Energy","lastPublishedDoi":"10.21203/rs.3.rs-6635968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6635968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing demand for environmentally sustainable and high-performance flexible packaging has accelerated the adoption of solvent-free lamination processes, particularly for multi-layered films such as polyethylene and polyamide bonded with polyurethane adhesives. Achieving optimal adhesion strength (AS) in solvent-free lamination remains challenging due to the complex interplay of processing parameters. This study employs Taguchi’s design of experiments (DOE) methodology to statistically optimize eight key parameters influencing AS, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio. An L\u003csub\u003e18 \u003c/sub\u003eorthogonal array was used to reduce experimental runs from 6,561 (full factorial design) to 18 while maintaining balanced parameter representation. Signal-to-noise (S/N) ratio analysis identified surface energy as the most influential factor, followed by machine speed and application temperature. ANOVA confirmed the statistical significance of surface energy (P = 0.047), accounting for 70.25% of the total variance in AS. Linear and quadratic regression models were developed to validate predictive accuracy, yielding R² values of 85.75% and 96.53%, respectively. A confirmation test under the optimized conditions predicted an AS of 646.94 N, closely matching the experimental value of 642 N with an error margin of 0.76%. The results demonstrate the effectiveness of Taguchi-based optimization and regression modeling in improving adhesion performance while minimizing experimental effort in SF lamination systems.\u003c/p\u003e","manuscriptTitle":"Statistical optimization of a solvent-free laminating unit for enhanced adhesion strength","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 04:40:38","doi":"10.21203/rs.3.rs-6635968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-12-22T08:46:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-23T05:51:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-19T12:14:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-18T22:25:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-05-14T03:34:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"630971ac-1d17-4cee-9792-079fe1567df5","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:12:55+00:00","versionOfRecord":{"articleIdentity":"rs-6635968","link":"https://doi.org/10.1007/s00170-026-17647-z","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-02-11 15:58:42","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-05-21 04:40:38","video":"","vorDoi":"10.1007/s00170-026-17647-z","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17647-z","workflowStages":[]},"version":"v1","identity":"rs-6635968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6635968","identity":"rs-6635968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}