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

doi:10.1007/s40436-024-00502-3

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 465-483.doi: 10.1007/s40436-024-00502-3

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

Yu-Xiang Ji^1,2,3, Li Huang^1,3, Qiu-Ren Chen^1,3, Charles K. S. Moy², Jing-Yi Zhang³, Xiao-Ya Hu⁴, Jian Wang⁴, Guo-Bi Tan³, Qing Liu^1,3

1. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
2. Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China;
3. Materials Bigdata and Applications Division, Materials Academy Jitri, Suzhou, 215131, Jiangsu, People's Republic of China;
4. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China

收稿日期:2023-10-16 修回日期:2023-11-26 发布日期:2024-09-07
通讯作者: Li Huang,E-mail:hli@jitri-amrd.com;Charles K.S.Moy,E-mail:cloo8000@uni.sydney.edu.au E-mail:hli@jitri-amrd.com;cloo8000@uni.sydney.edu.au
作者简介:Yu-Xiang Ji Ph.D student of Xi’an Jiaotong Liverpool University. He received his bachelors degree at Nanjing Tech University in Mechanical Engineering. He also earned a Masters degree from the University of Manchester in Advanced Manufacturing in the research of laser drilling on aircraft CFRP. Now, his research for the PhD program focuses on “Digital Twin Prototype for Vehicle Structural and Crashworthiness Design” and AI for Engineering (AI4E-Material/Joints);
Li Huang Doctor, vice director of the Materials Data Department at the Materials Academy, JITRI, and Guest Professor at Nanjing Tech University. His research interests include AI Algorithms and Engineering Applications (AI for Engineering), Integrated Computational Materials Engineering (ICME), digital design of material manufacturing and joining processes, and material fatigue and fracture;
Qiu-Ren Chen Distinguished Professor of Nanjing Tech University and a technical expert at the JITRI Academy. His primary research interests include fatigue durability analysis methods, failure behavior modeling of joints, and industrial simulation software development;
Charles K. S. Moy He received his Ph.D. degree in Structural Engineering from the University of Sydney, Australia, in 2012. He is currently an Associate Professor at Xi’an Jiaotong-Liverpool University, Suzhou, China. His current research interests include optimisation of structural systems, computational materials engineering, mechanics of composites, and machine learning for engineering;
Jing-Yi Zhang CAE Engineer, he graduated in 2016 with a master’s degree in Mechanical Engineering from South China University. He is currently employed at the Materials Bigdata and Applications Division, Delta Advanced Materials Research Institute, Suzhou;
Xiao-Ya Hu Structural Design Engineer, earned her bachelor’s degree from Nanjing Institute of Technology and subsequently attained her master’s degree from Nanjing University of Science and Technology. She is currently employed at the 45th Research Institute of China Electronics Technology Group Corporation;
Jian Wang Master’s student at Nanjing University of Science and Technology. His research interests include the fatigue life prediction of adhesive- selfpiercing rivet hybrid joints;
Guo-Bi Tan Process Engineer, graduated with a Bachelor’s degree from Sanjiang University, Nanjing. He currently works at the Materials Big Data and Applications Division of the Delta Advanced Materials Research Institute, Suzhou, China;
Qing Liu Director of Material Academy, JITRI, Suzhou and the Director of the Lightweight Materials Center at Nanjing Tech University. His research interests include electron microscopy techniques for characterizing the microstructure of deformed metals, micromechanisms and mechanical behaviors of metal deformation, and formation mechanisms.
基金资助:
This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the National Key Research and Development Program (Grant No. 2022YFB4601804), and the Key Basic Research Project of Suzhou (Grant Nos. SJC2022031, SJC2022029).

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

Yu-Xiang Ji^1,2,3, Li Huang^1,3, Qiu-Ren Chen^1,3, Charles K. S. Moy², Jing-Yi Zhang³, Xiao-Ya Hu⁴, Jian Wang⁴, Guo-Bi Tan³, Qing Liu^1,3

1. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
2. Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China;
3. Materials Bigdata and Applications Division, Materials Academy Jitri, Suzhou, 215131, Jiangsu, People's Republic of China;
4. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China

Received:2023-10-16 Revised:2023-11-26 Published:2024-09-07
Contact: Li Huang,E-mail:hli@jitri-amrd.com;Charles K.S.Moy,E-mail:cloo8000@uni.sydney.edu.au E-mail:hli@jitri-amrd.com;cloo8000@uni.sydney.edu.au
Supported by:
This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the National Key Research and Development Program (Grant No. 2022YFB4601804), and the Key Basic Research Project of Suzhou (Grant Nos. SJC2022031, SJC2022029).

摘要/Abstract

摘要： This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted (SPR) joints. Strength simulations 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00502-3

关键词: Machine learning, Self-piercing riveting (SPR), Sensitivity analysis, Multi-objective optimization

Abstract: This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted (SPR) joints. Strength simulations 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00502-3

Key words: Machine learning, Self-piercing riveting (SPR), Sensitivity analysis, Multi-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. A machine learning-based calibration method for strength simulation of self-piercing riveted joints[J]. Advances in Manufacturing, 2024, 12(3): 465-483.

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

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

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