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

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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 map** 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.

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Abbreviations

frt:

Friction coefficient between the rivet and the top sheet

frb:

Friction coefficient between the rivet and the bottom sheet

fss:

Friction coefficient between the top and bottom sheets

rivsfo:

Scaling factor for the material curve of the rivet

maxf:

Peak load of the load-displacement curve

stiff:

Stiffness of the load-displacement curve

maxdis:

Displacement at peak load of the load-displacement curve

failuredis:

Failure displacement of the load-displacement curve

\({\text{maxf}}_{{\text{s}}}\) :

Peak load in simulation

\({\text{maxf}}_{{\text{E}}}\) :

Peak load in the experiment

\(K_{{\text{s}}}\) :

Stiffness in simulation

\(K_{{\text{E}}}\) :

Stiffness in experiment

\(E_{{{\text{maxf}}}}\) :

Peak load error between simulation and experiment

\(E_{{{\text{stiff}}}}\) :

Stiffness error between simulation and experiment

\({\text{maxdis}}_{{\text{s}}}\) :

Displacement at peak load in simulation

\({\text{maxdis}}_{{\text{E}}}\) :

Displacement at peak load in experiment

\({\text{failuredis}}_{{\text{s}}}\) :

Failure displacement in simulation

\({\text{failuredis}}_{{\text{E}}}\) :

Failure displacement in the experiment

\(E_{{{\text{maxdis}}}}\) :

Error of displacement at peak load between simulation and experiment

\(E_{{{\text{failuredis}}}}\) :

Failure displacement error between simulation and experiment

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Acknowledgments

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).

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Authors and Affiliations

  1. Key Laboratory for Light-weight Materials, Nan**g Tech University, Nan**g, 210009, People’s Republic of China

    Yu-**ang Ji, Li Huang, Qiu-Ren Chen & Qing Liu

  2. Design School, **’an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People’s Republic of China

    Yu-**ang Ji & Charles K. S. Moy

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

    Yu-**ang Ji, Li Huang, Qiu-Ren Chen, **g-Yi Zhang, Guo-Bi Tan & Qing Liu

  4. School of Mechanical Engineering, Nan**g University of Science and Technology, Nan**g, 210094, People’s Republic of China

    **ao-Ya Hu & Jian Wang

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  1. Yu-**ang Ji
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Correspondence to Li Huang or Charles K. S. Moy.

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Ji, YX., Huang, L., Chen, QR. et al. A machine learning-based calibration method for strength simulation of self-piercing riveted joints. Adv. Manuf. (2024). https://doi.org/10.1007/s40436-024-00502-3

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