Data-Driven Approach for Minimizing Springback in Roll Forming: An Adaptive Process Parameter Control Strategy

Abstract

Springback is the most common quality defect in roll forming. It is difficult to control springback efficiently because the forming process is very complex and difficult to analyze. This paper proposes a data-driven springback control method that can adaptively adjust process parameters to minimize springback. Firstly, a digital twin model was established for efficiently collecting equipment data, formed part data, and remote equipment control. Then, based on gathered springback data considering different sheet widths, materials, uphill volumes, and roll gaps, a high-precision springback prediction model was constructed using the SAPSO-SVR algorithm. Lastly, building upon the prediction model, optimization models were developed using bat algorithm, enabling the system to automatically adjust process parameters based on optimization results when forming different sheets.The experimental results show that after optimization, the springback at the two corners of the hat-shaped part is reduced by 36.8% and 14.3% respectively. Experimental findings initially demonstrate the efficacy of this approach in controlling springback across sheets of diverse materials and varying widths. The proposed adaptive springback control method significantly enhances production efficiency and forming quality, driving the intelligent advancement of sheet metal roll forming technology.