Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process
In this study, an artificial neural network (ANN)–based method is presented to predict the experimental effective demolding forces (EDFs) produced during the injection molding of a polycarbonate polymer material. To evaluate the prediction accuracy and capability of the proposed method, three differ...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advances in Polymer Technology |
| Online Access: | http://dx.doi.org/10.1155/adv/1528204 |
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| author | Oluwole Abiodun Raimi Bong-Kee Lee |
| author_facet | Oluwole Abiodun Raimi Bong-Kee Lee |
| author_sort | Oluwole Abiodun Raimi |
| collection | DOAJ |
| description | In this study, an artificial neural network (ANN)–based method is presented to predict the experimental effective demolding forces (EDFs) produced during the injection molding of a polycarbonate polymer material. To evaluate the prediction accuracy and capability of the proposed method, three different algorithms, namely Levenberg–Marquardt (lm), BGFS quasi-Newton (bfg), and scale conjugate gradient (scg), were included in the proposed model. The generated models were validated by comparing the experimental and ANN results, which showed good quantitative agreement. The performance of the algorithms was evaluated using the R2 and root mean square error (RMSE) values, which indicated that scg exhibited the best performance with an R2 of 0.9655 and an RMSE of 0.0223. The relative contribution plot of the ANN models showed that packing pressure had a stronger influence than mold temperature, filling time, and melt temperature. These results will form the basis for predicting the EDF of a comparable molded part using ANN and will help to significantly improve the demolding properties of polymer materials. |
| format | Article |
| id | doaj-art-cf0d86fcda2a459d810bbbec52b02748 |
| institution | OA Journals |
| issn | 1098-2329 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Polymer Technology |
| spelling | doaj-art-cf0d86fcda2a459d810bbbec52b027482025-08-20T01:51:46ZengWileyAdvances in Polymer Technology1098-23292025-01-01202510.1155/adv/1528204Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding ProcessOluwole Abiodun Raimi0Bong-Kee Lee1School of Mechanical EngineeringSchool of Mechanical EngineeringIn this study, an artificial neural network (ANN)–based method is presented to predict the experimental effective demolding forces (EDFs) produced during the injection molding of a polycarbonate polymer material. To evaluate the prediction accuracy and capability of the proposed method, three different algorithms, namely Levenberg–Marquardt (lm), BGFS quasi-Newton (bfg), and scale conjugate gradient (scg), were included in the proposed model. The generated models were validated by comparing the experimental and ANN results, which showed good quantitative agreement. The performance of the algorithms was evaluated using the R2 and root mean square error (RMSE) values, which indicated that scg exhibited the best performance with an R2 of 0.9655 and an RMSE of 0.0223. The relative contribution plot of the ANN models showed that packing pressure had a stronger influence than mold temperature, filling time, and melt temperature. These results will form the basis for predicting the EDF of a comparable molded part using ANN and will help to significantly improve the demolding properties of polymer materials.http://dx.doi.org/10.1155/adv/1528204 |
| spellingShingle | Oluwole Abiodun Raimi Bong-Kee Lee Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process Advances in Polymer Technology |
| title | Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process |
| title_full | Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process |
| title_fullStr | Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process |
| title_full_unstemmed | Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process |
| title_short | Artificial Neural Network (ANN)–Based Prediction Model of Demolding Force in Injection Molding Process |
| title_sort | artificial neural network ann based prediction model of demolding force in injection molding process |
| url | http://dx.doi.org/10.1155/adv/1528204 |
| work_keys_str_mv | AT oluwoleabiodunraimi artificialneuralnetworkannbasedpredictionmodelofdemoldingforceininjectionmoldingprocess AT bongkeelee artificialneuralnetworkannbasedpredictionmodelofdemoldingforceininjectionmoldingprocess |