Implementation of Machine Learning in Flat Die Extrusion of Polymers
Achieving a uniform thickness and defect-free production in the flat die extrusion of polymer sheets and films is a major challenge. Dies are designed for one extrusion scenario, for a polymer grade with specified rheological behavior, and for a given throughput rate. The extrusion of different poly...
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MDPI AG
2025-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/9/1879 |
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| author | Nickolas D. Polychronopoulos Ioannis Sarris John Vlachopoulos |
| author_facet | Nickolas D. Polychronopoulos Ioannis Sarris John Vlachopoulos |
| author_sort | Nickolas D. Polychronopoulos |
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| description | Achieving a uniform thickness and defect-free production in the flat die extrusion of polymer sheets and films is a major challenge. Dies are designed for one extrusion scenario, for a polymer grade with specified rheological behavior, and for a given throughput rate. The extrusion of different polymer grades and at different flow rates requires trial-and-error procedures. This study investigated the application of machine learning (ML) to provide guidance for the extrusion of sheets and films with a reduced thickness, non-uniformities, and without defects. A dataset of 200 cases was generated using computer simulation software for flat die extrusion. The dataset encompassed variations in die geometry by varying the gap under a restrictor, polymer rheological and thermophysical properties, and processing conditions, including throughput rate and temperatures. The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). The ML models were trained to predict thickness variations, pressure drops, and the lowest wall shear rate (targets). Using the SHapley Additive exPlanations (SHAP) analysis provided valuable insights into the influence of input features, highlighting the critical roles of polymer rheology, throughput rate, and the gap beneath the restrictor in determining targets. This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures. |
| format | Article |
| id | doaj-art-19bffab1b83b499fbaf7bbfdb10bc907 |
| institution | OA Journals |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj-art-19bffab1b83b499fbaf7bbfdb10bc9072025-08-20T02:24:49ZengMDPI AGMolecules1420-30492025-04-01309187910.3390/molecules30091879Implementation of Machine Learning in Flat Die Extrusion of PolymersNickolas D. Polychronopoulos0Ioannis Sarris1John Vlachopoulos2Department of Mechanical Engineering, University of West Attica, Ancient Olive Grove Campus, Egaleo, 12241 Athens, GreeceDepartment of Mechanical Engineering, University of West Attica, Ancient Olive Grove Campus, Egaleo, 12241 Athens, GreeceDepartment of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, CanadaAchieving a uniform thickness and defect-free production in the flat die extrusion of polymer sheets and films is a major challenge. Dies are designed for one extrusion scenario, for a polymer grade with specified rheological behavior, and for a given throughput rate. The extrusion of different polymer grades and at different flow rates requires trial-and-error procedures. This study investigated the application of machine learning (ML) to provide guidance for the extrusion of sheets and films with a reduced thickness, non-uniformities, and without defects. A dataset of 200 cases was generated using computer simulation software for flat die extrusion. The dataset encompassed variations in die geometry by varying the gap under a restrictor, polymer rheological and thermophysical properties, and processing conditions, including throughput rate and temperatures. The dataset was used to train and evaluate the following three powerful machine learning (ML) algorithms: Random Forest (RF), XGBoost, and Support Vector Regression (SVR). The ML models were trained to predict thickness variations, pressure drops, and the lowest wall shear rate (targets). Using the SHapley Additive exPlanations (SHAP) analysis provided valuable insights into the influence of input features, highlighting the critical roles of polymer rheology, throughput rate, and the gap beneath the restrictor in determining targets. This ML-based methodology has the potential to reduce or even eliminate the use of trial and error procedures.https://www.mdpi.com/1420-3049/30/9/1879dataset generationpolymer rheologyML algorithmsSHAP analysis |
| spellingShingle | Nickolas D. Polychronopoulos Ioannis Sarris John Vlachopoulos Implementation of Machine Learning in Flat Die Extrusion of Polymers Molecules dataset generation polymer rheology ML algorithms SHAP analysis |
| title | Implementation of Machine Learning in Flat Die Extrusion of Polymers |
| title_full | Implementation of Machine Learning in Flat Die Extrusion of Polymers |
| title_fullStr | Implementation of Machine Learning in Flat Die Extrusion of Polymers |
| title_full_unstemmed | Implementation of Machine Learning in Flat Die Extrusion of Polymers |
| title_short | Implementation of Machine Learning in Flat Die Extrusion of Polymers |
| title_sort | implementation of machine learning in flat die extrusion of polymers |
| topic | dataset generation polymer rheology ML algorithms SHAP analysis |
| url | https://www.mdpi.com/1420-3049/30/9/1879 |
| work_keys_str_mv | AT nickolasdpolychronopoulos implementationofmachinelearninginflatdieextrusionofpolymers AT ioannissarris implementationofmachinelearninginflatdieextrusionofpolymers AT johnvlachopoulos implementationofmachinelearninginflatdieextrusionofpolymers |