Machine Learning-Based Analysis of Elastic Springback in Bending of SS, Al, and Cu Sheets with Localized Heating

Elastic springback is a critical challenge in sheet metal bending that directly affects dimensional accuracy and manufacturing efficiency. This study presents a comparative experimental and machine learning-based analysis of elastic springback behavior in three widely used sheet metals like stainless steel, aluminum, and copper, which are subjected to folding bending. The influence of key process parameters, namely sheet thickness (0.5 to 1.5 mm) and bending temperature (room temperature to 200 °C), was systematically examined under cold working. A cost-effective localized heating approach using a direct flame was introduced to enhance process control and reduce elastic recovery without the complexity associated with heated dies. Experimental results revealed substantial variability in elastic springback, ranging from 0.15% to 12.41%, emphasizing the fact that they are nonlinear in nature. Statistical evaluation confirmed that sheet thickness is the dominant factor governing elastic springback, while material type and temperature exhibit secondary yet meaningful effects. To improve predictive capability, five regression models (Linear, Polynomial, Support Vector, Random Forest, and Gradient Boosting) were developed and assessed. Among them, Random Forest demonstrated superior performance with the lowest prediction errors and strongest explanatory power, achieving an R2 of approximately 0.85. Cross-validation further validated its robustness and generalization capability. Feature importance and SHapley Additive exPlanations (SHAP) analyses reinforced the primary role of thickness in determining elastic recovery behavior. The findings provide practical insights for selecting materials and process conditions to minimize elastic springback while highlighting the effectiveness of ensemble learning techniques for accurate prediction. This work contributes a consistent framework for enhancing bending precision and supports data-driven decision-making in modern manufacturing environments.