In-cavity pressure measurements for failure diagnosis in injection moulding process and correlation with Numerical Simulation

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Therefore, this work aims to demonstrate the feasibility of the in-cavity pressure monitorization for failure diagnose and injection moulding process optimization. The methodology used to analyse the obtained pressure variation is presented. The results were correlated to the theoretical profile curve, which enables to obtain information about the process and the moulding tool. This way, it was possible to determine the origin of the defects present in the injected parts, focusing not only on the velocity to pressure switchover, but also in the initial part of the curve, related to the filling phase. Moreover, the obtained measured results and the studied processing conditions were correlated with the injection moulding process simulation. Injection moulding Pressure sensors Failure diagnosis Process simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The injection moulding process is a complex nonlinear, multivariate process and is widely used for the production of plastic components with complex geometries. There is a variety of variables that influence the final component quality, from the geometry and production tool, to the material and process conditions. Regarding the process variables, we should specifically address the processing temperatures, backpressure, injection velocity, velocity to pressure (V/P) switchover, packing pressure and time, and cooling time[ 1 ]. Its incorrect use leads to common defects, such as flow marks, flash, warpage, weight variation, among others. Additionally, affects the polymer chains orientation which may influence the mechanical behaviour of the final component[ 1 ][ 2 ]. Injection moulding machines are able to monitor different data, such as, temperature in the barrel, ram position or injection and packing pressures, however, they are not yet capable of measuring the polymer melt behaviour in the mould during filling and packing, as well as the material shrinkage during cooling[ 3 ]. Therefore, pressure and temperature sensors have been commonly integrated in the mould cavity to preform real-time measurements during the injection moulding process, since cavity pressure variations have high impact on the final part quality and the process control [ 4 ][ 5 ]. They enable to obtain information about the filling behaviour and the volumetric filling point, on contrary to the hydraulic pressure or nozzle pressure [ 6 ][ 7 ]. Simultaneously, injection moulding process simulation is broadly used to predict the polymer melt behaviour [ 8 ] in the mould, to support the manufacturing tool design[ 9 ][ 10 ], to diminishing the experimental trial-and-error techniques on the process optimization[ 9 ], thus reducing costs[ 11 ], and preventing or determine the origin of injection part defects and later eliminate them, such as, jetting[ 12 ], welding lines[ 13 ] or hesitation[ 14 ], venting problems[ 15 ], birefringence[ 16 ] etc. It is also an useful tool to predict the localization of hot spots and uneven cooling that leads to an inhomogeneous shrinkage throughout the part, resulting in residual stress [ 17 ], warping and deformation of the final part[ 18 ]. The literature reports[ 4 ][ 19 ] the conventional and non-conventional technologies that are often used to measure and monitor the pressure and temperature in mould cavity, and how indirect parameters such as viscosity, shrinkage, etc., may be estimated. It is possible to diagnose probable causes of process deviations by comparing the pressure sensors measurements with a profile curve, for instance, the V/P switchover with pressure peak and, therefore, proceed with corrective actions to optimize the process conditions[ 7 ]. The V/P switchover controls the process and its repeatability, when programmed too early may lead to short shots or contraction marks, or to an excessive stress in the part if programmed too late[ 1 ] [ 20 ]. Moreover, the cavity pressure peak is related to amount of material entering the cavity[ 21 ][ 22 ]. For a controlled process is advised to assemble the pressure sensor at 1/3 of the polymer flow and in the thicker zone of the part[ 23 ]. However, the location of the sensors depends on the information to be collected. The evaluation of the initial part of the curve is of great relevance when aesthetic[ 23 ] requirements are demanded and the pressure information may reveal inadequate behaviours, as non-adequate injection velocity, causing common surface defects[ 23 ]. Figure 1 depicts the typical curve of the cavity pressure variation over the injection cycle. From point 1 to 3 is given the filling phase: the molten polymer is dosed and injected, filling the mould cavity with a specific geometry. In point 1, the injection has started, but the melt has not yet achieved the sensor location. In point 2, the melt polymer touches the sensor. From point 2 to point 3 comprises the cavity filling, which leads to a gradual pressure increase until the volumetric filling of the cavity, without packing stage. Point 3 is the ideal time for the V/P switchover – changing from filling phase to packing. From points 3 to point 5 consist on the packing phase necessary to compensate the volumetric shrinkage, due to the cooling of the material. In point 4 the maximum pressure value in the cavity is reached, meaning the compression of the material takes place. From point 4 to 5 is represented the holding phase. Between points 5 and 6 the cooling phase occurs until the proper solidification of the part is assured. At point 5, the material freezes at the gate, and pressure is no longer applied. Finally, in point 6 pressure decreases to its minimum value, zero, meaning that the part is no longer in contact with the mould wall, that is the part is ejected. The state-of-art (SOA)[ 24 ][ 25 ] addresses the use of sensors in cavity for the injection moulding process, both to control the process and guarantee the part quality. It was observed that V/P switchover with screw position and cavity pressure near the gate assured a process repeatability[ 25 ]. Huang et al.[ 21 ] optimized the cavity pressure profile and the V/P switchover by modifying the injection velocity and the holding pressure to assure the stability in the part, focusing on warpage and width values[ 21 ]. In other study[ 22 ], Huang et al. considered the part weight as a reference to adjust holding phase in order to control the component quality[ 22 ]. Since an increase of the holding pressure results in an increase in the maximum cavity pressure, the cavity pressure profile was used to estimate the weight of the moulded parts[ 22 ]. On other hand, Jian-Yu et all.[ 26 ] developed an online quality monitoring and control system based on measured tie bar elongation characteristics[ 26 ]. It was analysed the injection speed, V/P switchover, holding pressure and barrel temperature, using the sensing characteristics, peak clamping force increment, as the state variable to monitor the quality variation of the process[ 26 ]. G. Gordon et all.[ 27 ] used multivariate sensors in the mould to analyse dimensional and mechanical properties[ 27 ]. Currently, the leading manufacturers of machinery and systems, are providing SOA manufacturing equipment with sensing systems data collection, data logging function monitoring and machine status as well as remote access to machine set up, however, it has yet to be integrated systems to collect data from the cavity tool. Although, in-mould sensors and data acquisition systems may monitor real-time data, the operator intervention is still required to configure and rectify the process when necessary. Systems to control the process, able to segregate good and bad parts or adjust de V/P switchover, by monitoring cavity data are already commercially available, such as, Kistler Group. The use of smart machines and process monitoring and control by cavity sensors integration, are aligned with Industry 4.0, where connected information is essential, including material data, injection machine, injection tools and component quality control and storage[ 28 ][ 29 ].The purpose of real-time monitoring, process control, automation, predictive models for process conditions definition, equipment and mould maintenance is to achieve production with zero defect and greater efficiency[ 30 ]. In this context, the literature reports the development of predictive systems using different approaches (artificial neural network (ANN), multiple linear regression (MLR) or multi-layer perceptron (MLP), etc., to achieve in situ (within runs) approaches to correct process variables instabilities and assure part quality. Chen et al.[ 31 ] developed predictive models, by MLR and ANN, to detect dimensional defects in moulded disks with collected data from cavity sensors[ 31 ]. Alternatively, Gim et al.[ 32 ] built a MLP neural network model to correlate state point (PSP), that represents moulding conditions to the part weight defined as the quality index, with the aim to monitor and optimize the process conditions[ 32 ]. Similarly, Ke and Huang[ 6 ] used MLP models with cavity pressure profiles, varying injection velocity and holding pressure, to inspect the quality of the produced parts, considering geometric width as the quality index[ 6 ]. On other hand, Finkeldey et al.[ 33 ] used both measured and simulated data to train two machine learning models, to minimize the experimental data necessary to predict part quality (thickness) of injected moulded parts[ 33 ]. However, injection moulding process being a complex nonlinear and multivariate process is difficult to settle the relationship between the real and the predicted output. The current SOA on predictive systems and correlation pressure profile recognition has proven to be more effective for monitoring and fault diagnosis. The importance of in-cavity pressure measurements for failure diagnosis in injection moulding process is underlined in this paper. This work presents the methodology behind the interpretation of the in-mould cavity pressure curve and the correlation with the theoretical one, focused on the identification of the process conditions that were incorrectly set and process optimization. Furthermore, the experimental data was compared with the results from process simulation. The case study was selected due to its non-consistent process and a recurrent defect. 2. Experimental 2.1. Equipment setup and materials To conduct this experimental study, a Ferromatik Elektra 110T injection moulding machine coupled with a robot Wemo of three axes (X, Y, Z) and three rotational axes, with a W-HP7/8 console was used. The mould temperature was controlled with a Thermovan temperature controller, by Piovan. Sensors (Cavity Pressure Sensor for direct measuring Ø 4mm Type 6157C and a Pressure and Temperature Sensor for direct measuring Ø 4mm Type 6190C) by Kistler, were integrated in the mould cavity. The data was monitored and acquired with the ComoNeo process monitoring system Type 5887A, by Kistler. A bicycle seat, represented in Fig. 2 , consisting of two parts, an insert based on glass fibre pre-impregnated with a Polyamide 6 (PA6), henceforward called as prepreg, was heated by infrared (IR) radiation system, by Krelus and then over-moulded with a Polyamide 6 reinforced with 30% glass fibber (PA6GF30) grade Domamid 6G30 300 BK from Domo. The material was pre-dry before the experimental work, 100°C for 12 hours. After the prepreg was heated by IR and transported to the mould by the Wemo robot, the insert was conformed by the closing movement of the mould and the injected material completed the conformation of the insert to the desired shape. For process monitoring, two pressure sensors were integrated near the gate, at 1/3 of the fill flow, and in the thickest part of the component (Fig. 3 ). For part quality analysis, two other pressure sensors were integrated at the end of the filling flow, coincident with the area of the part where burn marks often occur, although it is not a recommended practice to assemble sensors near the venting systems, as it requires more recurrent sensor maintenance, and may lead to damage. 2.2. Experimental methodology The previously process conditions, used without sensor monitoring have been reproduced, now using the pressure sensors instrumented mould for data record (Table 1 ) for analysing and comparative purpose. Following the optimum curve reported in the literature, a set of experiments were conducted for an optimization assessment of these process variables. The process conditions were adapted in order to approximate the initial in-cavity pressure curve to the theoretical one. The optimized cavity pressure variation may be used for process control. The process conditions held constant are also described in Table 1 . Table 1 – Process conditions before in-cavity pressure monitoring and process condition assessment. Injection Packing Temperature (°C) Velocity (mm/s) V/P switchover (mm) Time (s) Pressure (bar) Hot nozzle Zone 1 Zone 2 Zone 3 Zone 4 Mould Non-optimized cond. 100 30 5 400 250 240 235 230 230 90 Optimized conditions EXP1 60 30 15 800 280 270 250 240 230 90 EXP2 60 60 15 800 250 260 250 240 230 90 EXP3 60 60 5 800 250 260 250 240 230 90 Constant process conditions Dosage (mm) Decompression (mm) Screw velocity (rpm) Back Pressure (bar) Cooling time (s) Non-optimized cond. 100 10 150 50 15 Optimized conditions 150 2 300 50 30 2.3. Process simulation The assessed optimum process conditions were reproduced and correlated with a commercial process simulation software, Moldex3D 2021. The mould tool systems and the built model considered in the process simulation are represented in Fig. 4 . Also, pressure sensors were added in the built model in the same locations as the tool. In the process simulation, the sensors are defined as probes, this is, they are represented as a point in the mesh, unlike the real data monitoring where the data is measured by the sensor tip area. The Transient Cooling, Filling, Packing and Warpage analysis type was considered. The Ferromatik Elektra 110T injection machine was, also, considered, using the Machine Mode. 3. Results And Discussion 3.1. Process conditions optimization The pressure measured in the mould cavity is represented in Fig. 5 . A peak at the end of the volumetric filling was observed with a pressure value of 567,04 bar. This behaviour results from an early V/P switchover, as explained by Chen et al.[ 1 ], indicating that there is an excess of material entering in the cavity during the filling phase, which may lead to an over-packing component and residual stress[ 7 ]. Moreover, the specific injection pressure of 2533 bar is near the maximum limit of the injection machine. A visual inspection of the moulded part, data analysis correction and theoretical profile curve approximation enabled the identification of the parameter that had a significant effect on the variable response and on the final moulded part quality. Thus, for defects correction, a set of experiments were performed and correlated to the pressure profile. The first step to reduce the peak in measured pressure was to verify the melt temperature. Being below the material supplier recommendation, the melt temperature was increased from 250°C to 280°C, also adjusting the entire temperature profile. To compensate the excess material entering in the cavity, observed in the initial pressure curve, the injection velocity was also decreased, from 100 mm/s to 60 mm/s. These adjustments in the process variables resulted in the cavity pressure represented in Fig. 6 EXP1, as the resultant part. In the packing phase, it was obtained a more adequate pressure variation. However, at the volumetric filling, remained a pressure peak related to a late switching. This non suitable V/P switchover and the melt temperature adjustments resulted in the major flash visible in EXP1. Consequently, the V/P switchover from filling to packing phases was adjusted from 30mm to 60mm, resulting on the cavity pressure shown in EXP2. As shown, the peak at the end of the volumetric filling was eliminated and the pressure has a good variation during the cycle time. As the pressure drop after the packing phase was not abrupt, this is, there was no significant shrinkage of the material, this also indicates that the packing time may be reduced. As such, this time was changed from 15s to 5s, resulting in the cavity pressure curve in EXP3, which maintained an adequate pressure variation leading to a cycle time optimization. Here, is important to mention that in this production process, during the cooling phase, the time is given by the heating of the prepreg and not the cooling of the part. Therefore, the reduction of the cooling time is not important to the reduction of the total cycle time. Comparing the graphics from Fig. 5 and Fig. 6 EXP3 it may be observed that the process conditions were optimized. When comparing the pressure variation curve, before and after optimization, a reduction in the cavity pressure peak is observed, from 563,16 bar to 427, 96bar for P1 and 567,04 to 419,18 bar for P2, near the gate. Additionally, the achieved specific injection pressure was reduced to 1255 bar. Comparing the cavity pressure variation of the different sensors in the three graphics (Fig. 6 ), it may be observed that the curve with the higher pressure, P4 represented in yellow, corresponds to the sensor located at the end of the filling. Generally, the sensors near the gate detect more pressure, however, this different behaviour may indicate an insufficient venting system. As the polymer flow advances in the mould cavity and the air is not extracted, increased pressure is needed from the front flow to be able to go forward and fill the cavity. Moreover, focusing on the initial part of the pressure variation, corresponding to the filling phase, in general, the sensors located at the left side of the cavity (P1 and P3) has detected a more significant pressure variation, which may be related to the process, as to the position of the insert. In this sense, it was also conducted experiments with the injection of the part without the insert (Fig. 7 ). Is observed that the same tendency was achieved, this is, it is given more information on the left side of the cavity, confirming that this indicates that this result was not related to the positioning of the prepreg. On other hand, this behaviour may be related to the mould, namely, tool wear in the cold gate or even warping in the mould plates. 3.2. Analysis of the optimized curve This section focus on the analyses of the initial part of the EXP3 (Fig. 6 ). The volumetric filling is given by the cavity pressure when there is a difference between the change in the pressure variation (slope) and the initial measurement of a pressure change (the front of the polymer reaches the sensor location), represented with green circles. For the filling phase, it is recommendable to analyse the data from the sensors assemble near the gate, P1 and P2, which allows the collection of more data throughout the injection moulding. Specifically, in this mould cavity, the measured pressure had a low significant increase and it started to increase more significantly at the end of the filling. This is explained by the fact that the sensors are located at the top of the ribs, therefore, the base of the part is being fully filled before the top of the ribs. Additionally, the pressure has different variation during the filling indicating that the melt flow is not symmetric. In the short shot injection (Fig. 10 ) this behaviour is visible, where the polymer front is more advanced in the base while some parts of the top of the ribs are still unfilled. Moreover, is observed, and identified with red circles(Fig. 6 Exp3), that the P1 was already filled while the P2 was not yet totally replicated. Focusing on the packing phase in the pressure curve (Fig. 6 EXP3), it is noticeable the contraction of the material. 3.3. Correlation of the process simulation with experimental results As observed in the real-time pressure measurements, there was an insufficient venting system. Therefore, the consequence of the existing venting system was studied, by process simulation, comparing the results from a suitable and a non-suitable venting system. In Fig. 8 is possible to observe that the process simulation predicted that in the absence of a suitable venting system in the mould, a non-complete filling of the part was obtained. In the areas of the part where the pressure air contained in the cavity was higher than the front flow, it was not able to advance in the cavity, resulting in an incomplete part, not only in the areas at the end of filling, but also in the centre of the part (zoomed in the Fig. 8 ). By considering a venting system, at the end of filling, at the gaps in the extractors and at the gaps in the pins it was possible to obtain a filled cavity. In Fig. 9 is presented the filling profile at V/P switchover and at the volumetric filling of the part with the venting system. Although resulted on a complete filling of the cavity is possible to see that the software predicted a difficulty to fill the areas at the ends of the part. Moreover, it is observed in the injected part flow hesitation marks in the same area are identified in the simulation affirming the flow difficulty in advancing because of the trapped air. Figure 10 shows the comparison between the filling profile of the process simulation and the progressive filling in the experimental work. On a direct observation it may be confirmed that the process simulation has given a similar profile to the one obtained in the experimental work. However, the process simulation did not foresee that the ribs were not fully filled at the same time as the base of the part. On the other hand, the parts obtained from the progressive filling presents the front flow more advanced than shown in the simulation results; this behaviour is associated to the consequent force in the mould given by the dosage during the progressive filling. As mentioned above is visible the non-symmetric filling, that may be related to the mould wear in the gate that is not considered in the process simulation. The same behaviour can justify the difference in the injection pressure, that was lower for the process simulation (814,5 bar). Figure 11 shows the cavity pressure at the probes sensors given by the process simulation. Associated with the previous analyses, the measured pressure both by sensors P1 and P2 and by P3 and P4 predicted a symmetric filling, with an identical pressure variation in the two sensors near the gate and the two at the end of the filling. Moreover, during the filling phase, the pressure was felt more significantly when compared with the experimental measured cavity pressure, once more meaning that the process simulation did not predict the filled of the base of the part more advanced than the top of the ribs. Although the process simulation predicted problems with the venting system, and the cavity peak pressure in the process software for each sensor (707,1 bar for P1, 702,2 bar for P2, 691,8bar for P3 and 691,7 bar for P4) was higher than the values measured in the experimental data collection, this behaviour did not result in a pressure rise in the P3 and P4 sensors. At the packing phase of the pressure variation, is also noticeable the material contraction, as observed in the measured data from the experimental experiences. The time difference between the volumetric filling and the start of pressure rising, resulting in the filling time of the cavity, as the difference between the contraction point and the volumetric feeling in the sensors, for both process simulation and experimental injections are presented in Table 2 . It was observed a non-significant difference between the time reported on process simulation and the experimental component in terms of flow front reaching the sensors, meaning that the process simulation gives an accurate prediction on the pressure variation tendency. Sensors P3 and P4 weren’t considered on the filling time calculus since they are located at the end of the filling. Table 2 – Difference between the volumetric filling and the start of the pressure measurement, in seconds, to each sensor, for the process simulation and the experimental work. Volumetric filling VS Pressure measurements (s) Contraction point VS Volumetric feeling (s) Process simulation Experimental Process simulation Experimental P1 1,4 1,5 4,1 3,9 P2 1,4 1,8 4,1 4,0 P3 0,22 4,1 3,9 P4 0,22 4,1 4,1 4. Conclusions In this paper, the feasibility of the use of cavity pressure sensors for process monitoring and diagnose was demonstrated. The in-cavity pressure was monitored and acquired by a data acquisition system and then the influence of the process variables in the cavity pressure and part quality was analysed. The methodology for measurements and analysis of the cavity pressure and its correlation with the behaviour of the polymer flow during the injection process of the final moulded component was demonstrated. In addition to the pressure variation analysis during V/P switchover, focus was given to an analysis on the initial part of the curve to obtain more information about the filling phase of the process and its influence in the final part quality. Apart from the process related defects, the pressure cavity may also indicate tool mould problems that affects the final component. The control of the process is a prompt and precise solution for the identification and elimination of problems, particularly in this case study related to the mould, enabling a direct corrective action to eliminate potential defects. On other hand, in the process simulation, although the sensor probes did not predict the exact behaviour of the material when compared with the experimental process, this divergence may be explained by the fact that the mould in the computational model did not replicate the tool wear. Furthermore, the general results of the process simulation, namely the influence of the venting system did prove that the process simulation is a useful tool to predict the melt behaviour and to assist in the mould tool design. Declarations Funding This work was supported by Interface Programme, through the FITEC – Fund for Innovation, Technology Transfer and Circular Economy. Competing Interests The authors have no relevant financial or non-financial interests to declare. Availability of data and material Not applicable Code availability Not applicable Ethics approval Not applicable Consent to participate Not applicable Consent to publication Not applicable Author’s contribution Not applicable References Chen JY, Liu CY, Huang MS (2019) Tie-bar elongation based filling-to-packing switchover control and prediction of injection molding quality. Polym (Basel) 11. https://doi.org/10.3390/polym11071168 Pantani R, Coccorullo I, Speranza V, Titomanlio G (2007) Morphology evolution during injection molding: Effect of packing pressure. Polym (Guildf) 48:2778–2790. https://doi.org/10.1016/j.polymer.2007.03.007 Chen JY, Yang KJ, Huang MS (2018) Online quality monitoring of molten resin in injection molding. 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Int J Prod Res 59:4773–4778. https://doi.org/10.1080/00207543.2021.1956675 Farahani S, Brown N, Loftis J et al (2019) Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0. Int J Adv Manuf Technol 105:1371–1389. https://doi.org/10.1007/s00170-019-04323-8 Chen JC, Guo G, Wang WN (2020) Artificial neural network-based online defect detection system with in-mold temperature and pressure sensors for high precision injection molding. Int J Adv Manuf Technol 110:2023–2033. https://doi.org/10.1007/s00170-020-06011-4 Gim J, Rhee B (2021) Novel analysis methodology of cavity pressure profiles in injection-molding processes using interpretation of machine learning model. Polym (Basel) 13. https://doi.org/10.3390/polym13193297 Finkeldey F, Volke J, Zarges JC et al (2020) Learning quality characteristics for plastic injection molding processes using a combination of simulated and measured data. J Manuf Process 60:134–143. https://doi.org/10.1016/j.jmapro.2020.10.028 Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2023 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 03 Aug, 2022 Reviewers agreed at journal 09 Jun, 2022 Reviewers invited by journal 08 Jun, 2022 Editor assigned by journal 27 May, 2022 First submitted to journal 26 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1697245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":112173325,"identity":"49350806-fb7e-4ad9-9564-168ff42fcce5","order_by":0,"name":"Cátia Araújo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJCCA1A6geEDgwQxGpgRWhhnQLQwNhDSgmDyQGj8Wvjbzx888IPBLp9/RsLDx7Y7LOTMGXiPP8CnReJMMsPBHoZkyxk3EpKNc89IGFs28CXitcWAIZnhAA8DswHDjYQ06dw2icQNB3gM8Wvhf8xw8A9DvYE8SItlm0Q9YS0SyQyHeRgOGxiAtDC2SSQYENIiceOxwWEZg+MGhmceJBv2npEw3NnMlzgDnxb+/sTHH99UVBvIHc9JfPBzR528OXvvgQ/4tECdByJ4EsARYgCLHSIA+wGIFgbitYyCUTAKRsHIAAAyJEiqNAZVagAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9946-3448","institution":"PIEP - Center for Innovation in Polymer Engineering","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Cátia","middleName":"","lastName":"Araújo","suffix":""},{"id":112173326,"identity":"ff8fe79f-0ca4-4c08-b891-bc88321c974f","order_by":1,"name":"Diogo Pereira","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diogo","middleName":"","lastName":"Pereira","suffix":""},{"id":112173327,"identity":"dbdc2edc-fac5-4d4f-a313-09ac480fd687","order_by":2,"name":"Diana Dias","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Dias","suffix":""},{"id":112173328,"identity":"8c7ba541-620c-47b6-be67-6546aef72a2d","order_by":3,"name":"Rita Marques","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rita","middleName":"","lastName":"Marques","suffix":""},{"id":112173329,"identity":"93a9c997-518b-420f-9095-4ec2d78eeed0","order_by":4,"name":"Sílvia Cruz","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sílvia","middleName":"","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2022-05-26 17:15:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1697245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1697245/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-023-11100-1","type":"published","date":"2023-02-24T18:59:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":22730201,"identity":"48c74eb3-b88b-4be2-b0fa-c55442f81d65","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68067,"visible":true,"origin":"","legend":"\u003cp\u003eCavity pressure profile\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/8ba8da13e4d90d6c36855603.png"},{"id":22730200,"identity":"32665b2e-8ad7-471f-8af6-4ade24789d06","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1805059,"visible":true,"origin":"","legend":"\u003cp\u003eBicycle seat: prepreg insert (in purple) and the PA6GF30 injected part (in grey).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/3687d57011a081a8b4921c20.png"},{"id":22730931,"identity":"dac5dbcb-ac2d-4ba8-b416-7f3f0af5133b","added_by":"auto","created_at":"2022-06-16 15:22:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1241793,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the experimental setup for in-cavity pressure measurements and sensors location.\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/10d85f7ff69c6c158d5dbf04.png"},{"id":22731429,"identity":"dd0657dd-a762-4d6e-8d2e-b6bdc2b5719e","added_by":"auto","created_at":"2022-06-16 15:27:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3861832,"visible":true,"origin":"","legend":"\u003cp\u003eMould tool systems (left) and built model (right) considered in the process simulation.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/a968b154aca3643e001928ed.png"},{"id":22730203,"identity":"f3b447fe-a2ff-489b-9cec-7d55a916ef87","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":794112,"visible":true,"origin":"","legend":"\u003cp\u003ePressure measured in the mould cavity using the non-optimized processing conditions, a) Adapted from[1].\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/04d35ffca0f44329480de5f7.png"},{"id":22730202,"identity":"e74e99f4-30c6-4a29-8842-7c9fc46ab759","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3068687,"visible":true,"origin":"","legend":"\u003cp\u003eCavity pressure and the resultant part from EXP1, EXP2 and EXP3\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/dc0a8b2a5ad7b16cd3d4487a.png"},{"id":22731431,"identity":"c9b54dae-7293-4762-80e4-e88f39b5359d","added_by":"auto","created_at":"2022-06-16 15:27:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":161821,"visible":true,"origin":"","legend":"\u003cp\u003eInitial part of the pressure cavity from a part injected without insert\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/cc9d309a5e9c69c23dc78152.png"},{"id":22730204,"identity":"325b9a6c-609a-40ee-996c-97661b5b4bb5","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1298878,"visible":true,"origin":"","legend":"\u003cp\u003eEnd of filling in a mould without a venting system\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/221a1d4c605840644c847c3e.png"},{"id":22732328,"identity":"33c9b436-601f-4e41-a7b8-1ad4523ad833","added_by":"auto","created_at":"2022-06-16 15:32:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3262220,"visible":true,"origin":"","legend":"\u003cp\u003eFilling profile considering the venting system.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/2eb60a9b4a11d7d979d8e4b4.png"},{"id":22730209,"identity":"b4c62cf7-408c-4f68-bfc2-be430c6cefaa","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3132796,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the filling profile given by the process simulation and the progressive filling obtained in experimental work.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/f0b3475c6db4c13a58f3041a.png"},{"id":22730210,"identity":"9734f5a2-70e9-4676-8208-95ee75f1b092","added_by":"auto","created_at":"2022-06-16 15:17:06","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":249266,"visible":true,"origin":"","legend":"\u003cp\u003eIn-cavity pressure measured by sensor probes in process simulation.\u0026nbsp;\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/c68127e4efaa157fb1df9340.png"},{"id":44720206,"identity":"e2df22d8-da96-421d-ae81-cf5a1cfbd5ae","added_by":"auto","created_at":"2023-10-16 19:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5449980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1697245/v1/31e2e549-fa37-46a6-8d22-13390d1468b2.pdf"}],"financialInterests":"","formattedTitle":"In-cavity pressure measurements for failure diagnosis in injection moulding process and correlation with Numerical Simulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe injection moulding process is a complex nonlinear, multivariate process and is widely used for the production of plastic components with complex geometries. There is a variety of variables that influence the final component quality, from the geometry and production tool, to the material and process conditions. Regarding the process variables, we should specifically address the processing temperatures, backpressure, injection velocity, velocity to pressure (V/P) switchover, packing pressure and time, and cooling time[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its incorrect use leads to common defects, such as flow marks, flash, warpage, weight variation, among others. Additionally, affects the polymer chains orientation which may influence the mechanical behaviour of the final component[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Injection moulding machines are able to monitor different data, such as, temperature in the barrel, ram position or injection and packing pressures, however, they are not yet capable of measuring the polymer melt behaviour in the mould during filling and packing, as well as the material shrinkage during cooling[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, pressure and temperature sensors have been commonly integrated in the mould cavity to preform real-time measurements during the injection moulding process, since cavity pressure variations have high impact on the final part quality and the process control [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They enable to obtain information about the filling behaviour and the volumetric filling point, on contrary to the hydraulic pressure or nozzle pressure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Simultaneously, injection moulding process simulation is broadly used to predict the polymer melt behaviour [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] in the mould, to support the manufacturing tool design[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], to diminishing the experimental trial-and-error techniques on the process optimization[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], thus reducing costs[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and preventing or determine the origin of injection part defects and later eliminate them, such as, jetting[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], welding lines[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] or hesitation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], venting problems[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], birefringence[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] etc. It is also an useful tool to predict the localization of hot spots and uneven cooling that leads to an inhomogeneous shrinkage throughout the part, resulting in residual stress [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], warping and deformation of the final part[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature reports[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] the conventional and non-conventional technologies that are often used to measure and monitor the pressure and temperature in mould cavity, and how indirect parameters such as viscosity, shrinkage, etc., may be estimated. It is possible to diagnose probable causes of process deviations by comparing the pressure sensors measurements with a profile curve, for instance, the V/P switchover with pressure peak and, therefore, proceed with corrective actions to optimize the process conditions[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The V/P switchover controls the process and its repeatability, when programmed too early may lead to short shots or contraction marks, or to an excessive stress in the part if programmed too late[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, the cavity pressure peak is related to amount of material entering the cavity[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For a controlled process is advised to assemble the pressure sensor at 1/3 of the polymer flow and in the thicker zone of the part[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the location of the sensors depends on the information to be collected. The evaluation of the initial part of the curve is of great relevance when aesthetic[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] requirements are demanded and the pressure information may reveal inadequate behaviours, as non-adequate injection velocity, causing common surface defects[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the typical curve of the cavity pressure variation over the injection cycle. From point 1 to 3 is given the filling phase: the molten polymer is dosed and injected, filling the mould cavity with a specific geometry. In point 1, the injection has started, but the melt has not yet achieved the sensor location. In point 2, the melt polymer touches the sensor. From point 2 to point 3 comprises the cavity filling, which leads to a gradual pressure increase until the volumetric filling of the cavity, without packing stage. Point 3 is the ideal time for the V/P switchover \u0026ndash; changing from filling phase to packing. From points 3 to point 5 consist on the packing phase necessary to compensate the volumetric shrinkage, due to the cooling of the material. In point 4 the maximum pressure value in the cavity is reached, meaning the compression of the material takes place. From point 4 to 5 is represented the holding phase. Between points 5 and 6 the cooling phase occurs until the proper solidification of the part is assured. At point 5, the material freezes at the gate, and pressure is no longer applied. Finally, in point 6 pressure decreases to its minimum value, zero, meaning that the part is no longer in contact with the mould wall, that is the part is ejected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe state-of-art (SOA)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] addresses the use of sensors in cavity for the injection moulding process, both to control the process and guarantee the part quality. It was observed that V/P switchover with screw position and cavity pressure near the gate assured a process repeatability[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Huang et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] optimized the cavity pressure profile and the V/P switchover by modifying the injection velocity and the holding pressure to assure the stability in the part, focusing on warpage and width values[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In other study[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Huang et al. considered the part weight as a reference to adjust holding phase in order to control the component quality[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since an increase of the holding pressure results in an increase in the maximum cavity pressure, the cavity pressure profile was used to estimate the weight of the moulded parts[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On other hand, Jian-Yu et all.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] developed an online quality monitoring and control system based on measured tie bar elongation characteristics[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It was analysed the injection speed, V/P switchover, holding pressure and barrel temperature, using the sensing characteristics, peak clamping force increment, as the state variable to monitor the quality variation of the process[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. G. Gordon et all.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] used multivariate sensors in the mould to analyse dimensional and mechanical properties[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Currently, the leading manufacturers of machinery and systems, are providing SOA manufacturing equipment with sensing systems data collection, data logging function monitoring and machine status as well as remote access to machine set up, however, it has yet to be integrated systems to collect data from the cavity tool. Although, in-mould sensors and data acquisition systems may monitor real-time data, the operator intervention is still required to configure and rectify the process when necessary. Systems to control the process, able to segregate good and bad parts or adjust de V/P switchover, by monitoring cavity data are already commercially available, such as, Kistler Group.\u003c/p\u003e \u003cp\u003eThe use of smart machines and process monitoring and control by cavity sensors integration, are aligned with Industry 4.0, where connected information is essential, including material data, injection machine, injection tools and component quality control and storage[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].The purpose of real-time monitoring, process control, automation, predictive models for process conditions definition, equipment and mould maintenance is to achieve production with zero defect and greater efficiency[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this context, the literature reports the development of predictive systems using different approaches (artificial neural network (ANN), multiple linear regression (MLR) or multi-layer perceptron (MLP), etc., to achieve in situ (within runs) approaches to correct process variables instabilities and assure part quality. Chen et al.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] developed predictive models, by MLR and ANN, to detect dimensional defects in moulded disks with collected data from cavity sensors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Alternatively, Gim et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] built a MLP neural network model to correlate state point (PSP), that represents moulding conditions to the part weight defined as the quality index, with the aim to monitor and optimize the process conditions[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, Ke and Huang[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] used MLP models with cavity pressure profiles, varying injection velocity and holding pressure, to inspect the quality of the produced parts, considering geometric width as the quality index[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On other hand, Finkeldey et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] used both measured and simulated data to train two machine learning models, to minimize the experimental data necessary to predict part quality (thickness) of injected moulded parts[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, injection moulding process being a complex nonlinear and multivariate process is difficult to settle the relationship between the real and the predicted output. The current SOA on predictive systems and correlation pressure profile recognition has proven to be more effective for monitoring and fault diagnosis.\u003c/p\u003e \u003cp\u003eThe importance of in-cavity pressure measurements for failure diagnosis in injection moulding process is underlined in this paper. This work presents the methodology behind the interpretation of the in-mould cavity pressure curve and the correlation with the theoretical one, focused on the identification of the process conditions that were incorrectly set and process optimization. Furthermore, the experimental data was compared with the results from process simulation. The case study was selected due to its non-consistent process and a recurrent defect.\u003c/p\u003e"},{"header":"2. Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Equipment setup and materials\u003c/h2\u003e \u003cp\u003eTo conduct this experimental study, a Ferromatik Elektra 110T injection moulding machine coupled with a robot Wemo of three axes (X, Y, Z) and three rotational axes, with a W-HP7/8 console was used. The mould temperature was controlled with a Thermovan temperature controller, by Piovan. Sensors (Cavity Pressure Sensor for direct measuring \u0026Oslash; 4mm Type 6157C and a Pressure and Temperature Sensor for direct measuring \u0026Oslash; 4mm Type 6190C) by Kistler, were integrated in the mould cavity. The data was monitored and acquired with the ComoNeo process monitoring system Type 5887A, by Kistler.\u003c/p\u003e \u003cp\u003eA bicycle seat, represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, consisting of two parts, an insert based on glass fibre pre-impregnated with a Polyamide 6 (PA6), henceforward called as prepreg, was heated by infrared (IR) radiation system, by Krelus and then over-moulded with a Polyamide 6 reinforced with 30% glass fibber (PA6GF30) grade Domamid 6G30 300 BK from Domo. The material was pre-dry before the experimental work, 100\u0026deg;C for 12 hours. After the prepreg was heated by IR and transported to the mould by the Wemo robot, the insert was conformed by the closing movement of the mould and the injected material completed the conformation of the insert to the desired shape. For process monitoring, two pressure sensors were integrated near the gate, at 1/3 of the fill flow, and in the thickest part of the component (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For part quality analysis, two other pressure sensors were integrated at the end of the filling flow, coincident with the area of the part where burn marks often occur, although it is not a recommended practice to assemble sensors near the venting systems, as it requires more recurrent sensor maintenance, and may lead to damage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental methodology\u003c/h2\u003e \u003cp\u003eThe previously process conditions, used without sensor monitoring have been reproduced, now using the pressure sensors instrumented mould for data record (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for analysing and comparative purpose. Following the optimum curve reported in the literature, a set of experiments were conducted for an optimization assessment of these process variables. The process conditions were adapted in order to approximate the initial in-cavity pressure curve to the theoretical one. The optimized cavity pressure variation may be used for process control. The process conditions held constant are also described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e\u0026ndash; Process conditions before in-cavity pressure monitoring and process condition assessment.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInjection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePacking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\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\u003eVelocity (mm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV/P switchover (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePressure (bar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHot nozzle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZone 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZone 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZone 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZone 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMould\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eNon-optimized cond.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eOptimized conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eConstant process conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDosage (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDecompression (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eScrew velocity (rpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eBack Pressure (bar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCooling time (s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eNon-optimized cond.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eOptimized conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e30\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. Process simulation\u003c/h2\u003e \u003cp\u003eThe assessed optimum process conditions were reproduced and correlated with a commercial process simulation software, Moldex3D 2021. The mould tool systems and the built model considered in the process simulation are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Also, pressure sensors were added in the built model in the same locations as the tool. In the process simulation, the sensors are defined as probes, this is, they are represented as a point in the mesh, unlike the real data monitoring where the data is measured by the sensor tip area. The Transient Cooling, Filling, Packing and Warpage analysis type was considered. The Ferromatik Elektra 110T injection machine was, also, considered, using the Machine Mode.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Process conditions optimization\u003c/h2\u003e \u003cp\u003eThe pressure measured in the mould cavity is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. A peak at the end of the volumetric filling was observed with a pressure value of 567,04 bar. This behaviour results from an early V/P switchover, as explained by Chen et al.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], indicating that there is an excess of material entering in the cavity during the filling phase, which may lead to an over-packing component and residual stress[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the specific injection pressure of 2533 bar is near the maximum limit of the injection machine.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA visual inspection of the moulded part, data analysis correction and theoretical profile curve approximation enabled the identification of the parameter that had a significant effect on the variable response and on the final moulded part quality. Thus, for defects correction, a set of experiments were performed and correlated to the pressure profile. The first step to reduce the peak in measured pressure was to verify the melt temperature. Being below the material supplier recommendation, the melt temperature was increased from 250\u0026deg;C to 280\u0026deg;C, also adjusting the entire temperature profile. To compensate the excess material entering in the cavity, observed in the initial pressure curve, the injection velocity was also decreased, from 100 mm/s to 60 mm/s. These adjustments in the process variables resulted in the cavity pressure represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e EXP1, as the resultant part. In the packing phase, it was obtained a more adequate pressure variation. However, at the volumetric filling, remained a pressure peak related to a late switching. This non suitable V/P switchover and the melt temperature adjustments resulted in the major flash visible in EXP1. Consequently, the V/P switchover from filling to packing phases was adjusted from 30mm to 60mm, resulting on the cavity pressure shown in EXP2. As shown, the peak at the end of the volumetric filling was eliminated and the pressure has a good variation during the cycle time. As the pressure drop after the packing phase was not abrupt, this is, there was no significant shrinkage of the material, this also indicates that the packing time may be reduced. As such, this time was changed from 15s to 5s, resulting in the cavity pressure curve in EXP3, which maintained an adequate pressure variation leading to a cycle time optimization. Here, is important to mention that in this production process, during the cooling phase, the time is given by the heating of the prepreg and not the cooling of the part. Therefore, the reduction of the cooling time is not important to the reduction of the total cycle time. Comparing the graphics from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e EXP3 it may be observed that the process conditions were optimized. When comparing the pressure variation curve, before and after optimization, a reduction in the cavity pressure peak is observed, from 563,16 bar to 427, 96bar for P1 and 567,04 to 419,18 bar for P2, near the gate. Additionally, the achieved specific injection pressure was reduced to 1255 bar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the cavity pressure variation of the different sensors in the three graphics (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), it may be observed that the curve with the higher pressure, P4 represented in yellow, corresponds to the sensor located at the end of the filling. Generally, the sensors near the gate detect more pressure, however, this different behaviour may indicate an insufficient venting system. As the polymer flow advances in the mould cavity and the air is not extracted, increased pressure is needed from the front flow to be able to go forward and fill the cavity. Moreover, focusing on the initial part of the pressure variation, corresponding to the filling phase, in general, the sensors located at the left side of the cavity (P1 and P3) has detected a more significant pressure variation, which may be related to the process, as to the position of the insert. In this sense, it was also conducted experiments with the injection of the part without the insert (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Is observed that the same tendency was achieved, this is, it is given more information on the left side of the cavity, confirming that this indicates that this result was not related to the positioning of the prepreg. On other hand, this behaviour may be related to the mould, namely, tool wear in the cold gate or even warping in the mould plates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of the optimized curve\u003c/h2\u003e \u003cp\u003eThis section focus on the analyses of the initial part of the EXP3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The volumetric filling is given by the cavity pressure when there is a difference between the change in the pressure variation (slope) and the initial measurement of a pressure change (the front of the polymer reaches the sensor location), represented with green circles. For the filling phase, it is recommendable to analyse the data from the sensors assemble near the gate, P1 and P2, which allows the collection of more data throughout the injection moulding. Specifically, in this mould cavity, the measured pressure had a low significant increase and it started to increase more significantly at the end of the filling. This is explained by the fact that the sensors are located at the top of the ribs, therefore, the base of the part is being fully filled before the top of the ribs. Additionally, the pressure has different variation during the filling indicating that the melt flow is not symmetric. In the short shot injection (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) this behaviour is visible, where the polymer front is more advanced in the base while some parts of the top of the ribs are still unfilled. Moreover, is observed, and identified with red circles(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e Exp3), that the P1 was already filled while the P2 was not yet totally replicated. Focusing on the packing phase in the pressure curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e EXP3), it is noticeable the contraction of the material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation of the process simulation with experimental results\u003c/h2\u003e \u003cp\u003eAs observed in the real-time pressure measurements, there was an insufficient venting system. Therefore, the consequence of the existing venting system was studied, by process simulation, comparing the results from a suitable and a non-suitable venting system.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e is possible to observe that the process simulation predicted that in the absence of a suitable venting system in the mould, a non-complete filling of the part was obtained. In the areas of the part where the pressure air contained in the cavity was higher than the front flow, it was not able to advance in the cavity, resulting in an incomplete part, not only in the areas at the end of filling, but also in the centre of the part (zoomed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy considering a venting system, at the end of filling, at the gaps in the extractors and at the gaps in the pins it was possible to obtain a filled cavity. In Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e is presented the filling profile at V/P switchover and at the volumetric filling of the part with the venting system. Although resulted on a complete filling of the cavity is possible to see that the software predicted a difficulty to fill the areas at the ends of the part. Moreover, it is observed in the injected part flow hesitation marks in the same area are identified in the simulation affirming the flow difficulty in advancing because of the trapped air.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the comparison between the filling profile of the process simulation and the progressive filling in the experimental work. On a direct observation it may be confirmed that the process simulation has given a similar profile to the one obtained in the experimental work. However, the process simulation did not foresee that the ribs were not fully filled at the same time as the base of the part. On the other hand, the parts obtained from the progressive filling presents the front flow more advanced than shown in the simulation results; this behaviour is associated to the consequent force in the mould given by the dosage during the progressive filling. As mentioned above is visible the non-symmetric filling, that may be related to the mould wear in the gate that is not considered in the process simulation. The same behaviour can justify the difference in the injection pressure, that was lower for the process simulation (814,5 bar).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the cavity pressure at the probes sensors given by the process simulation. Associated with the previous analyses, the measured pressure both by sensors P1 and P2 and by P3 and P4 predicted a symmetric filling, with an identical pressure variation in the two sensors near the gate and the two at the end of the filling. Moreover, during the filling phase, the pressure was felt more significantly when compared with the experimental measured cavity pressure, once more meaning that the process simulation did not predict the filled of the base of the part more advanced than the top of the ribs. Although the process simulation predicted problems with the venting system, and the cavity peak pressure in the process software for each sensor (707,1 bar for P1, 702,2 bar for P2, 691,8bar for P3 and 691,7 bar for P4) was higher than the values measured in the experimental data collection, this behaviour did not result in a pressure rise in the P3 and P4 sensors. At the packing phase of the pressure variation, is also noticeable the material contraction, as observed in the measured data from the experimental experiences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe time difference between the volumetric filling and the start of pressure rising, resulting in the filling time of the cavity, as the difference between the contraction point and the volumetric feeling in the sensors, for both process simulation and experimental injections are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It was observed a non-significant difference between the time reported on process simulation and the experimental component in terms of flow front reaching the sensors, meaning that the process simulation gives an accurate prediction on the pressure variation tendency. Sensors P3 and P4 weren\u0026rsquo;t considered on the filling time calculus since they are located at the end of the filling.\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\u003e\u0026ndash; Difference between the volumetric filling and the start of the pressure measurement, in seconds, to each sensor, for the process simulation and the experimental work.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVolumetric filling \u003cem\u003eVS\u003c/em\u003e Pressure measurements (s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eContraction point \u003cem\u003eVS\u003c/em\u003e Volumetric feeling (s)\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\u003eProcess simulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProcess simulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this paper, the feasibility of the use of cavity pressure sensors for process monitoring and diagnose was demonstrated. The in-cavity pressure was monitored and acquired by a data acquisition system and then the influence of the process variables in the cavity pressure and part quality was analysed. The methodology for measurements and analysis of the cavity pressure and its correlation with the behaviour of the polymer flow during the injection process of the final moulded component was demonstrated. In addition to the pressure variation analysis during V/P switchover, focus was given to an analysis on the initial part of the curve to obtain more information about the filling phase of the process and its influence in the final part quality. Apart from the process related defects, the pressure cavity may also indicate tool mould problems that affects the final component. The control of the process is a prompt and precise solution for the identification and elimination of problems, particularly in this case study related to the mould, enabling a direct corrective action to eliminate potential defects.\u003c/p\u003e \u003cp\u003eOn other hand, in the process simulation, although the sensor probes did not predict the exact behaviour of the material when compared with the experimental process, this divergence may be explained by the fact that the mould in the computational model did not replicate the tool wear. Furthermore, the general results of the process simulation, namely the influence of the venting system did prove that the process simulation is a useful tool to predict the melt behaviour and to assist in the mould tool design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Interface Programme, through the FITEC \u0026ndash; Fund for Innovation, Technology Transfer and Circular Economy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen JY, Liu CY, Huang MS (2019) Tie-bar elongation based filling-to-packing switchover control and prediction of injection molding quality. 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J Manuf Process 60:134\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmapro.2020.10.028\u003c/span\u003e\u003cspan address=\"10.1016/j.jmapro.2020.10.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":true,"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":"Injection moulding, Pressure sensors, Failure diagnosis, Process simulation","lastPublishedDoi":"10.21203/rs.3.rs-1697245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1697245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pressure profile recognition technique for monitoring and diagnose processing failures during an injection moulding process, such as burn marks, and short shots, is a useful instrument for process and part quality control, production with zero defects and greater efficiency. Therefore, this work aims to demonstrate the feasibility of the in-cavity pressure monitorization for failure diagnose and injection moulding process optimization. The methodology used to analyse the obtained pressure variation is presented. The results were correlated to the theoretical profile curve, which enables to obtain information about the process and the moulding tool. This way, it was possible to determine the origin of the defects present in the injected parts, focusing not only on the velocity to pressure switchover, but also in the initial part of the curve, related to the filling phase. Moreover, the obtained measured results and the studied processing conditions were correlated with the injection moulding process simulation.\u003c/p\u003e","manuscriptTitle":"In-cavity pressure measurements for failure diagnosis in injection moulding process and correlation with Numerical Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-16 15:17:04","doi":"10.21203/rs.3.rs-1697245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2022-08-03T06:53:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-06-09T05:57:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-06-08T21:31:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-27T12:00:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2022-05-26T13:14:25+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":"ffe3e41e-5d90-407e-b94e-61ba9d4a52ef","owner":[],"postedDate":"June 16th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:05:40+00:00","versionOfRecord":{"articleIdentity":"rs-1697245","link":"https://doi.org/10.1007/s00170-023-11100-1","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2023-02-24 18:59:37","publishedOnDateReadable":"February 24th, 2023"},"versionCreatedAt":"2022-06-16 15:17:04","video":"","vorDoi":"10.1007/s00170-023-11100-1","vorDoiUrl":"https://doi.org/10.1007/s00170-023-11100-1","workflowStages":[]},"version":"v1","identity":"rs-1697245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1697245","identity":"rs-1697245","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}