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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...

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Autores principales: Párizs, Richárd Dominik, Török, Dániel, Ageyeva, Tatyana, Kovács, József Gábor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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