Cargando…
Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding
Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt tem...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783370/ https://www.ncbi.nlm.nih.gov/pubmed/36559915 http://dx.doi.org/10.3390/polym14245548 |
_version_ | 1784857562305265664 |
---|---|
author | Jeon, Joohyeong Rhee, Byungohk Gim, Jinsu |
author_facet | Jeon, Joohyeong Rhee, Byungohk Gim, Jinsu |
author_sort | Jeon, Joohyeong |
collection | PubMed |
description | Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis. |
format | Online Article Text |
id | pubmed-9783370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97833702022-12-24 Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding Jeon, Joohyeong Rhee, Byungohk Gim, Jinsu Polymers (Basel) Article Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis. MDPI 2022-12-18 /pmc/articles/PMC9783370/ /pubmed/36559915 http://dx.doi.org/10.3390/polym14245548 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jeon, Joohyeong Rhee, Byungohk Gim, Jinsu Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title | Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title_full | Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title_fullStr | Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title_full_unstemmed | Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title_short | Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding |
title_sort | melt temperature estimation by machine learning model based on energy flow in injection molding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783370/ https://www.ncbi.nlm.nih.gov/pubmed/36559915 http://dx.doi.org/10.3390/polym14245548 |
work_keys_str_mv | AT jeonjoohyeong melttemperatureestimationbymachinelearningmodelbasedonenergyflowininjectionmolding AT rheebyungohk melttemperatureestimationbymachinelearningmodelbasedonenergyflowininjectionmolding AT gimjinsu melttemperatureestimationbymachinelearningmodelbasedonenergyflowininjectionmolding |