Springback Angle Prediction for High-Strength Aluminum Alloy Bending via Multi-Stage Regression

Abstract

The landing gear of an aircraft plays a crucial role in ensuring the safe takeoff and landing of the aircraft. Several defects in landing gear press molding may occur, including cross-section distortion, wall thickness thinning, and the springback phenomenon. These defects can significantly impact the quality of the molded product. This study employs a combination of finite element simulation and ML models to predict the springback angle of 7075 high-strength aluminum alloy pipes. The ABAQUS 2021 software was used to simulate the deformation behavior in the bending process based on the large deformation elastoplasticity theory. By utilizing the entropy method and analysis of variance (ANOVA), the significant factors affecting the forming quality were determined in the following order: pipe diameter > mandrel and pipe clearance > material properties > wall thickness > speed. The training set was augmented to improve the model generalization ability to build a multi-stage prediction model based on Lasso regression. The results show that the R² score of the ridge model reaches 0.9669, which is significantly better than other common machine learning methods. Finally, the model was applied to a real experimental dataset example through a transfer learning technique, showing obvious improvement compared with the control group. This study effectively predicts the springback angle of large-diameter thin-walled pipes and significantly improves the molding quality of bent fittings.