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

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.

Improving RSW nugget diameter prediction method:unleashing the power of multi-fidelity neural networks and transfer learning

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning model.Subsequently,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques.The accuracy and generalization performance of the models were thoroughly validated.The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.