High-fidelity prediction of forming quality for self-piercing riveted joints in aluminum alloy based on machine learning

The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:L(C)h(1A)h(2A),L(A)h(1A)h(2B),L(A)h(1A)h(2B) and L(B)h(1A)h(2B). A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization Algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the model consistently achieved mean absolute percentage error (MAPE) values below 10 %, correlation coefficients (R-2) exceeding 0.98, and stable mean squared error (MSE) values around 0.0002 across the prediction of three metrics. These results underscore the model's high precision and stability. Consequently, the proposed enhanced model offers a solution that is more stable, accurate, and robust for the prediction of forming quality in self-piercing riveted joints within engineering applications.