The present work concerns optimal design for the injection molding process of a deflection yoke (coil separator). The optimal design for the injection molding process is developed using design of experiments and finite element analysis. Two design of experiments approaches are applied such as: the design of experiment for mold design and the design of experiments for determination of process parameters. Finite element analyses have been carried out as a design of experiments for mold design: runner system and cooling channel. In order to determine optimal process parameters, experiments have been performed for various process conditions with the design of experiments scheduling.
{"title":"Optimal Design for Injection Molding Process Using Design of Experiments and Finite Element Analysis","authors":"K. Park, J. Ahn, S. R. Choi","doi":"10.1115/imece2000-1225","DOIUrl":"https://doi.org/10.1115/imece2000-1225","url":null,"abstract":"\u0000 The present work concerns optimal design for the injection molding process of a deflection yoke (coil separator). The optimal design for the injection molding process is developed using design of experiments and finite element analysis. Two design of experiments approaches are applied such as: the design of experiment for mold design and the design of experiments for determination of process parameters. Finite element analyses have been carried out as a design of experiments for mold design: runner system and cooling channel. In order to determine optimal process parameters, experiments have been performed for various process conditions with the design of experiments scheduling.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123157803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the co-injection molding process, two (or more) different polymers are injected into the cavity simultaneously or sequentially. Different properties of these two polymers and their distribution in the cavity greatly affect the applications of this molding process. The skin layer can use special polymers to provide good appearance and texture, strength, chemical resistance, EMI shielding and other functions. The core layer can use recycled or inexpensive materials. Together these can improve part quality and lower the cost. However, due to the dynamic interaction of two polymers in the manufacturing process and their difference in properties, process control becomes more complicated and process design becomes a challenge. The rules used for the traditional injection molding process design may not always be useful for co-injection molding any more. An integrated CAE software has been developed to simulate the co-injection molding process. In this study, the capability and usefulness of the CAE tool will be shown. The control of polymer distribution will be discussed. The effects of polymer properties and their distribution on part quality will also be studied.
{"title":"Numerical Simulation of Co-Injection Molding","authors":"James T. Wang","doi":"10.1115/imece2000-1241","DOIUrl":"https://doi.org/10.1115/imece2000-1241","url":null,"abstract":"\u0000 In the co-injection molding process, two (or more) different polymers are injected into the cavity simultaneously or sequentially. Different properties of these two polymers and their distribution in the cavity greatly affect the applications of this molding process. The skin layer can use special polymers to provide good appearance and texture, strength, chemical resistance, EMI shielding and other functions. The core layer can use recycled or inexpensive materials. Together these can improve part quality and lower the cost.\u0000 However, due to the dynamic interaction of two polymers in the manufacturing process and their difference in properties, process control becomes more complicated and process design becomes a challenge. The rules used for the traditional injection molding process design may not always be useful for co-injection molding any more. An integrated CAE software has been developed to simulate the co-injection molding process. In this study, the capability and usefulness of the CAE tool will be shown. The control of polymer distribution will be discussed. The effects of polymer properties and their distribution on part quality will also be studied.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117190997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Improvements in part quality and cost reduction are the primary objectives of CAE use in the injection molding industry. Engineers use advanced injection molding simulation software to analyze and verify their part designs. Traditionally, engineers have had to rerun simulations to verify the effects of changes in gate locations. For complex models, simulations are very time consuming. To reduce the design cycle time, a Design Optimization Module is developed by C-MOLD. One of the functions of this new software module is to automatically select optimal gate locations. This innovative technology is the result of close R&D collaboration between C-MOLD and LG-PRC in Korea. An overview of gate location optimization technology is presented in this paper, and several examples are also presented as illustration.
{"title":"Gate Location Optimization in Injection Molding Processing","authors":"Baojiu Lin, Won Gil Ryim","doi":"10.1115/imece2000-1234","DOIUrl":"https://doi.org/10.1115/imece2000-1234","url":null,"abstract":"\u0000 Improvements in part quality and cost reduction are the primary objectives of CAE use in the injection molding industry. Engineers use advanced injection molding simulation software to analyze and verify their part designs. Traditionally, engineers have had to rerun simulations to verify the effects of changes in gate locations. For complex models, simulations are very time consuming. To reduce the design cycle time, a Design Optimization Module is developed by C-MOLD. One of the functions of this new software module is to automatically select optimal gate locations. This innovative technology is the result of close R&D collaboration between C-MOLD and LG-PRC in Korea. An overview of gate location optimization technology is presented in this paper, and several examples are also presented as illustration.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"79 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During resin transfer molding (RTM), dry spot formation and air entrapment during the filling stage often lead to inferior parts and high scrap rate. These problems are usually caused by improper design of inlet conditions and vent locations that prevent the Last Point to Fill (LPF) location from coinciding with the preset vent location. This paper presents a methodology to design the RTM process with a desired filling pattern free of dry spots. Unlike the traditional filling simulation that predicts the filling pattern using prescribed inlet conditions and the specification of the preform permeability field, this methodology calculates the optimum inlet conditions based on the specification of the desired filling pattern and the prescription of preform permeability. The use of this algorithm greatly enhances the process design capability by reducing trial-and-error procedures that use traditional direct filling simulation as a primary process design tool. The numerical algorithm is described along with RTM design example showing that use of the proposed methodology results in the LPF location coinciding with the preset vent location.
{"title":"Regulating Filling Pattern for Optimum Design of Resin Transfer Molding","authors":"Y. Chen, B. Minaie, A. Mescher","doi":"10.1115/imece2000-1238","DOIUrl":"https://doi.org/10.1115/imece2000-1238","url":null,"abstract":"\u0000 During resin transfer molding (RTM), dry spot formation and air entrapment during the filling stage often lead to inferior parts and high scrap rate. These problems are usually caused by improper design of inlet conditions and vent locations that prevent the Last Point to Fill (LPF) location from coinciding with the preset vent location. This paper presents a methodology to design the RTM process with a desired filling pattern free of dry spots. Unlike the traditional filling simulation that predicts the filling pattern using prescribed inlet conditions and the specification of the preform permeability field, this methodology calculates the optimum inlet conditions based on the specification of the desired filling pattern and the prescription of preform permeability. The use of this algorithm greatly enhances the process design capability by reducing trial-and-error procedures that use traditional direct filling simulation as a primary process design tool. The numerical algorithm is described along with RTM design example showing that use of the proposed methodology results in the LPF location coinciding with the preset vent location.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123130675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this paper is to investigate the optimization schemes for intelligent process control based on neural networks in injection molding. To achieve the goal of intelligent process control, performance indexes, formulating by multi-losses functions, are adaptively optimized for reverse deducing the process control parameters from the quality factors of parts. In addition, the requirements on quality factors such as dimensions, shrinkage, and warpage are predicted by making use of the Computer-Aided Engineering software, namely C-MOLD, with the process window pre-screened by the Design of Experiments procedure. Hereby, a model consisting of Radial Basis Functions Networks (RBFN) is employed for representing the causal factors between the process control parameters and the quality factors. And, the RBFN model is then trained for optimizing the given performance indexes with an adaptive optimization scheme. Finally, two example cases based on numerical simulations on process control are demonstrated for verifications. It is observed that the proposed intelligent process control in injection molding could automatically achieve stable and nearly optimal process conditions within a short period of time for the given quality requirements. Therefore, the intelligent expert controller could be applied for practical uses on the shop floor in the future.
{"title":"An Injection Molding Expert Controller Based on Neural Network Optimization Schemes","authors":"Pei-Jen Wang, J. Liang","doi":"10.1115/imece2000-1227","DOIUrl":"https://doi.org/10.1115/imece2000-1227","url":null,"abstract":"\u0000 The objective of this paper is to investigate the optimization schemes for intelligent process control based on neural networks in injection molding. To achieve the goal of intelligent process control, performance indexes, formulating by multi-losses functions, are adaptively optimized for reverse deducing the process control parameters from the quality factors of parts. In addition, the requirements on quality factors such as dimensions, shrinkage, and warpage are predicted by making use of the Computer-Aided Engineering software, namely C-MOLD, with the process window pre-screened by the Design of Experiments procedure. Hereby, a model consisting of Radial Basis Functions Networks (RBFN) is employed for representing the causal factors between the process control parameters and the quality factors. And, the RBFN model is then trained for optimizing the given performance indexes with an adaptive optimization scheme. Finally, two example cases based on numerical simulations on process control are demonstrated for verifications. It is observed that the proposed intelligent process control in injection molding could automatically achieve stable and nearly optimal process conditions within a short period of time for the given quality requirements. Therefore, the intelligent expert controller could be applied for practical uses on the shop floor in the future.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115863840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new elongational viscosity model along with the Carreau-Yasuda model for shear viscosity is used for a finite element simulation of the flow in a capillary rheometer die. The entrance pressure loss predicted by the finite element flow simulation is matched with the corresponding experimental data to predict the parameters in the new elongational viscosity model. For two different polymers, the predicted elongational viscosity is compared with the corresponding predictions from Cogswell’s analysis and K-BKZ model.
{"title":"Estimation of Elongational Viscosity Using Entrance Flow Simulation","authors":"D. Sarkar, M. Gupta","doi":"10.1115/imece2000-1245","DOIUrl":"https://doi.org/10.1115/imece2000-1245","url":null,"abstract":"\u0000 A new elongational viscosity model along with the Carreau-Yasuda model for shear viscosity is used for a finite element simulation of the flow in a capillary rheometer die. The entrance pressure loss predicted by the finite element flow simulation is matched with the corresponding experimental data to predict the parameters in the new elongational viscosity model. For two different polymers, the predicted elongational viscosity is compared with the corresponding predictions from Cogswell’s analysis and K-BKZ model.","PeriodicalId":198750,"journal":{"name":"CAE and Related Innovations for Polymer Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121668042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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