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A machine learning-based calibration method for strength simulation of self-piercing riveted joints

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This paper presents a new machine learning-based calibration framework for strength simulation mod-els of self-piercing riveted(SPR)joints.Strength simula-tions were conducted through the integrated modeling of SPR joints from process to performance,while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions.A sensitivity study of the critical simulation parameters(e.g.,friction coefficient and scaling factor)was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection.Subsequently,machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve.Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments.A multi-objective genetic algorithm(MOGA)was chosen for optimization.The three combinations of SPR joints illustrated the effectiveness of the proposed framework,and good agreement was achieved between the calibrated models and experiments.

Machine learningSelf-piercing riveting(SPR)Sensitivity analysisMulti-objective optimization

Yu-Xiang Ji、Li Huang、Qiu-Ren Chen、Charles K.S.Moy、Jing-Yi Zhang、Xiao-Ya Hu、Jian Wang、Guo-Bi Tan、Qing Liu

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Key Laboratory for Light-weight Materials,Nanjing Tech University,Nanjing 210009,People's Republic of China

Design School,Xi'an Jiaotong-Liverpool University,Suzhou 215123,Jiangsu,People's Republic of China

Materials Bigdata and Applications Division,Materials Academy Jitri,Suzhou 215131,Jiangsu,People's Republic of China

School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,People's Republic of China

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2024

10.1007/s40436-024-00502-3

先进制造进展(英文版)

先进制造进展(英文版)

ISSN：
年,卷(期)：2024.12(3)