Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 7; p. 2704
• Main Authors: Párizs, Richárd Dominik, Török, Dániel, Ageyeva, Tatyana, Kovács, József Gábor
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2022; MDPI
• Subjects: Accuracy; Algorithms; Bayes Theorem; Big Data; cavity pressure curve; Classification; classifiers; Discriminant Analysis; Industry; Industry 4.0; Injection molding; Machine Learning; Manufacturing; Methods; Neural networks; quality control; Support Vector Machine; classifiers; quality control; injection molding; machine learning; cavity pressure curve
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22072704

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.