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Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in mul...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002478/ https://www.ncbi.nlm.nih.gov/pubmed/35408318 http://dx.doi.org/10.3390/s22072704 |
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author | Párizs, Richárd Dominik Török, Dániel Ageyeva, Tatyana Kovács, József Gábor |
author_facet | Párizs, Richárd Dominik Török, Dániel Ageyeva, Tatyana Kovács, József Gábor |
author_sort | Párizs, Richárd Dominik |
collection | PubMed |
description | One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm. |
format | Online Article Text |
id | pubmed-9002478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90024782022-04-13 Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction Párizs, Richárd Dominik Török, Dániel Ageyeva, Tatyana Kovács, József Gábor Sensors (Basel) Article One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm. MDPI 2022-04-01 /pmc/articles/PMC9002478/ /pubmed/35408318 http://dx.doi.org/10.3390/s22072704 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 Párizs, Richárd Dominik Török, Dániel Ageyeva, Tatyana Kovács, József Gábor Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title | Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title_full | Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title_fullStr | Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title_full_unstemmed | Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title_short | Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction |
title_sort | machine learning in injection molding: an industry 4.0 method of quality prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002478/ https://www.ncbi.nlm.nih.gov/pubmed/35408318 http://dx.doi.org/10.3390/s22072704 |
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