Empowering Die Selection in V-Bending: Insights from Decision Tree Algorithms

This study presents the application of tree-based algorithms to predict springback in the V-bending process of sheet metals, particularly for SUS304 material. V-bending, a critical process in metal forming, often faces challenges due to springback, which affects dimensional accuracy and product quality. Using virtual experiments conducted via ANSYS software, the study evaluates the influence of variables such as die angle, die radius, material thickness, and punch displacement on springback. Four tree-based algorithms—Decision Trees, Random Forest, Gradient Boosting Machines (GBM), and Extra Trees—were used to predict springback, with the Decision Tree model showing the highest accuracy (Mean Absolute Error MAE=0.35; Mean Square Error=0.20). The findings indicate that incorporating predictive models into die and punch displacement selection can enhance operational efficiency and ensure consistent product quality. This methodology offers a cost-effective and efficient approach to improving the precision of sheet metal V-bending processes, with potential applications across various sheet metal materials.