A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding
The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning mode...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/13/16/2652 |
| _version_ | 1797522286846148608 |
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| author | Matthias Schmid Dominik Altmann Georg Steinbichler |
| author_facet | Matthias Schmid Dominik Altmann Georg Steinbichler |
| author_sort | Matthias Schmid |
| collection | DOAJ |
| description | The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes. |
| first_indexed | 2024-03-10T08:27:15Z |
| format | Article |
| id | doaj.art-4b21ba2894844a67a6966d35f2f3a7b7 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-10T08:27:15Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-4b21ba2894844a67a6966d35f2f3a7b72023-11-22T09:22:16ZengMDPI AGPolymers2073-43602021-08-011316265210.3390/polym13162652A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection MoldingMatthias Schmid0Dominik Altmann1Georg Steinbichler2Institute of Polymer Injection Moulding and Process Automation, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, AustriaInstitute of Polymer Injection Moulding and Process Automation, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, AustriaInstitute of Polymer Injection Moulding and Process Automation, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, AustriaThe optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes.https://www.mdpi.com/2073-4360/13/16/2652machine learningmultilayer perceptronneural networkregressionplasticizingpolymers |
| spellingShingle | Matthias Schmid Dominik Altmann Georg Steinbichler A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding Polymers machine learning multilayer perceptron neural network regression plasticizing polymers |
| title | A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding |
| title_full | A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding |
| title_fullStr | A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding |
| title_full_unstemmed | A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding |
| title_short | A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding |
| title_sort | simulation data based machine learning model for predicting basic parameter settings of the plasticizing process in injection molding |
| topic | machine learning multilayer perceptron neural network regression plasticizing polymers |
| url | https://www.mdpi.com/2073-4360/13/16/2652 |
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