Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model
The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Polymers |
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| Online Access: | https://www.mdpi.com/2073-4360/13/19/3297 |
| _version_ | 1797515828218822656 |
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| author | Jinsu Gim Byungohk Rhee |
| author_facet | Jinsu Gim Byungohk Rhee |
| author_sort | Jinsu Gim |
| collection | DOAJ |
| description | The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process. |
| first_indexed | 2024-03-10T06:52:45Z |
| format | Article |
| id | doaj.art-190fe4c83cbb47ea9bcb1e7c41986de5 |
| institution | Directory Open Access Journal |
| issn | 2073-4360 |
| language | English |
| last_indexed | 2024-03-10T06:52:45Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Polymers |
| spelling | doaj.art-190fe4c83cbb47ea9bcb1e7c41986de52023-11-22T16:38:35ZengMDPI AGPolymers2073-43602021-09-011319329710.3390/polym13193297Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning ModelJinsu Gim0Byungohk Rhee1Department of Chemical Engineering, Hanyang University, 55 Hanyangdeahak-ro, Ansan 15588, KoreaDepartment of Mechanical Engineering, Ajou University, 206, Worldcup-ro, Suwon 16499, KoreaThe cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.https://www.mdpi.com/2073-4360/13/19/3297injection moldingcavity pressureinterpretable machine learning |
| spellingShingle | Jinsu Gim Byungohk Rhee Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model Polymers injection molding cavity pressure interpretable machine learning |
| title | Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model |
| title_full | Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model |
| title_fullStr | Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model |
| title_full_unstemmed | Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model |
| title_short | Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model |
| title_sort | novel analysis methodology of cavity pressure profiles in injection molding processes using interpretation of machine learning model |
| topic | injection molding cavity pressure interpretable machine learning |
| url | https://www.mdpi.com/2073-4360/13/19/3297 |
| work_keys_str_mv | AT jinsugim novelanalysismethodologyofcavitypressureprofilesininjectionmoldingprocessesusinginterpretationofmachinelearningmodel AT byungohkrhee novelanalysismethodologyofcavitypressureprofilesininjectionmoldingprocessesusinginterpretationofmachinelearningmodel |