Predictions of Fatigue limit in Polymer Composites Employing Machine Learning

Abstract

Since there is a wide variety of evaluation items for automotive plastic parts and the evaluations for such parts require a large amount of time, alternative materials are not being adopted quickly enough in case of emergencies. Among various testing items, fatigue testing is particularly time-consuming and frequently required. This paper proposes a method for predicting fatigue limits of polymer composites by using machine learning. In this study, we employed an ensemble method of decision tree such as random forest, XGBoost and Light GBM regression. According to the domain knowledge about polymer science, we suggest that the most appropriate method is XGBoost for this dataset. As a result, we established a versatile prediction model of fatigue limits of polymer composites which coefficient of determination is 0.803 even for polymer composites from material manufacturers not used for constructing the prediction model.