Application of machine learning algorithm to prediction of thermal spring back of hot press forming

Abstract

Some steels are very difficult to fabricate using cold forming, which is a conventional sheet metal forming method. As a result, hot stamping is one of the ways utilized to manufacture components made of advanced high strength steel (AHSS). Although hot press sheet forming can form the high-strength steels, it also can cause thermal springback defects. In this paper, thermal spring back simulation data is used to examine and predict the springback condition by using machine learning algorithm. The focus of this research is to determine which machine learning model performs best for thermal springback predictions. To forecast thermal springback, three machine learning techniques were used in this paper: the KNN algorithm, the DT algorithm, and the SVR algorithm. Furthermore, the predicting errors of these three models are compared. The compared results indicate that the Decision Trees model can properly forecast and capture thermal springback variation trends.