High accuracy roll forming springback prediction model of SVR based on SA-PSO optimization

Jingsheng He (), Shiyi Cu (), Hui Xia (), Yong Sun (), Wenchao Xiao () and Yinwang Ren ()
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Jingsheng He: Shenzhen Institute for Advanced Study, UESTC
Shiyi Cu: Shenzhen Institute for Advanced Study, UESTC
Hui Xia: Shenzhen Institute for Advanced Study, UESTC
Yong Sun: Shenzhen Institute for Advanced Study, UESTC
Wenchao Xiao: Shenzhen Institute for Advanced Study, UESTC
Yinwang Ren: Shenzhen Institute for Advanced Study, UESTC

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 8, 167-183

Abstract: Abstract Springback is one of the major shape defects in roll forming. It is difficult to predict springback accurately and efficiently because the process involves complicated deformation. In this paper, a high accuracy Support Vector Regression (SVR) algorithm based on the Simulated Annealing Particle Swarm Optimization algorithm (SAPSO) is proposed to predict springback. Firstly, simulations of the forming process of V-channel profile are carried out to investigate the springback at different fillet radii, yield strengths, uphill volumes and roll span spaces. Then with the data obtained, the accuracy of SAPSO-SVR, SVR, and Back Propagation Neural Network (BPNN) prediction models was tested. The experimental results show that SAPSO-SVR has the highest prediction accuracy, and its average absolute error is about 0.11 $$^\circ $$ ∘ , which is 38.7% less than BPNN, and 61.8% less than SVR. Furthermore, in order to explore the applicability of emerging Artificial Intelligence (AI) models in investigating forming mechanisms, the relationship between forming parameters and springback was elucidated based on the prediction model. It is found that the roll span space had a small impact on springback, but a larger roll span space would reduce the impact of the uphill volume on springback. And applying an uphill volume of $$-\,$$ - 10 mm will minimize the variation of longitudinal strain in the cross section, thereby reducing the trend of flange inward shrinkage and leading to an increase in springback. The above conclusions are mutually verified with traditional theoretical research. This paper establishes a high accuracy prediction model and explores the springback mechanism, which can provide important theoretical references for future research on intelligent roll forming.

Keywords: Springback prediction model; Machine learning; Roll forming; SAPSO-SVR (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02222-0

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