Machine learning-assisted decision support in industrial manufacturing: a case study on injection molding machine selection

Abstract

Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts. This approach is time-consuming, costly, and requires a high level of expertise to ensure the optimal machine choice. This study explores the use of machine learning (ML) methods for efficient machine selection based on product, material, and mold criteria. Six supervised learning techniques were tested on a dataset comprising 70 plastic parts and five machines. Evaluation metrics like F1-score, recall, precision, and accuracy were used to compare models. The results indicate that ML can provide guidance for predicting machine selection, with a preference for the random forest (RF), decision tree (DT), and support vector machine (SVM) models. The most favorable outcome is demonstrated by the RF model, displaying an accuracy of 93%. In this manner, these findings may be helpful for injection molding businesses that are considering the significance of using classification algorithms in their manufacturing process.