Machine learning for predicting dimensions of extrusion blow molded parts: A comparison of three algorithms

Abstract

In the perspective of feed-forward control through a manufacturing process chain, production data can be used downstream to correct subsequent processes and improve product quality. In an extrusion blow-moulding (EBM) use case, supervised learning (SL) has been applied to predict geometrical dimensions based on process data, as a possible basis for feed-forward control. The labeled tabular dataset showed signifcant time dependency which complicates the learning task. In this paper we present a time-series regression approach and compare three common SL algorithms – Random Forest, Gradient Boosting and XGBoost. The results show that only part of the variations in product geometry could be learned from process data and that future work will be necessary in order to increase the models’ performance.