TY - GEN
T1 - A Data-Driven Framework for Axis Prediction and Springback Compensation in Spatial Metal Tube Bending
AU - Tao, Yonglin
AU - Wang, Zili
AU - Zhang, Shuyou
AU - Tan, Jianrong
AU - Li, Zheyi
N1 - Publisher Copyright:
© The Chinese Mechanical Engineering Society 2026.
PY - 2026
Y1 - 2026
N2 - Spatial metal tubes with free-form curved axes are critical components in advanced industrial applications, such as aerospace and nuclear power systems, but their manufacturing is challenged by springback, which compromises axial precision. Traditional bending methods and theoretical models fall short in addressing the complex plastic deformation in spatial tube bending. This study introduces an innovative data-driven framework that integrates a Multi-layer Perceptron (MLP) for axis prediction with an enhanced Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for springback compensation in Multi-roller bending (MRB). A finite element simulation sample library trains the MLP to capture the nonlinear relationship between geometric/process parameters and springback responses. The MOEA/D, enhanced with reference points and hybrid evolution strategies, optimizes forming parameters to minimize axis deviation. Simulation results demonstrate high prediction accuracy and significant springback reduction, with base circle radius errors reduced by 90% and pitch errors by 33%, offering a robust and efficient solution for precision manufacturing of spatial metal tubes.
AB - Spatial metal tubes with free-form curved axes are critical components in advanced industrial applications, such as aerospace and nuclear power systems, but their manufacturing is challenged by springback, which compromises axial precision. Traditional bending methods and theoretical models fall short in addressing the complex plastic deformation in spatial tube bending. This study introduces an innovative data-driven framework that integrates a Multi-layer Perceptron (MLP) for axis prediction with an enhanced Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for springback compensation in Multi-roller bending (MRB). A finite element simulation sample library trains the MLP to capture the nonlinear relationship between geometric/process parameters and springback responses. The MOEA/D, enhanced with reference points and hybrid evolution strategies, optimizes forming parameters to minimize axis deviation. Simulation results demonstrate high prediction accuracy and significant springback reduction, with base circle radius errors reduced by 90% and pitch errors by 33%, offering a robust and efficient solution for precision manufacturing of spatial metal tubes.
KW - Data-driven framework
KW - MOEA/D
KW - Multi-layer perceptron
KW - Spatial metal tube
KW - Springback compensation
UR - https://www.scopus.com/pages/publications/105041233773
U2 - 10.1007/978-981-95-7342-4_98
DO - 10.1007/978-981-95-7342-4_98
M3 - 会议稿件
AN - SCOPUS:105041233773
SN - 9789819573417
T3 - Mechanisms and Machine Science
SP - 1392
EP - 1407
BT - Advances in Mechanical Design - Proceedings of the 2025 International Conference on Mechanical Design ICMD 2025
A2 - Tan, Jianrong
A2 - Liu, Zhenyu
A2 - Hu, Weifei
PB - Springer Science and Business Media B.V.
T2 - International Conference on Mechanical Design, ICMD 2025
Y2 - 9 May 2025 through 11 May 2025
ER -