Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 stainless steel

The use of a composite welded joint consisting of titanium and austenitic stainless steel metals is evidently a favourable selection for industrial applications employing the resistance spot welding (RSW) operation. Nevertheless, achieving a high-quality welded joint proved challenging owing to the properties of the diverse range of materials' used. To improve the quality of dissimilar welded joints, the welding parameters should be selected precisely. With that in mind, the current research endeavoured to figure out the ideal RSW parameters for a dissimilar resistance spot-welded joint between grade 2 titanium alloy (Ti) and AISI 304 austenitic stainless steel (ASS) with equal and unequal thicknesses of 0.5 and 1 mm. The RSW cases based on the selected thickness were referred to as the following: similar thickness of 1 mm for Ti and ASS as case E, dissimilar thickness of 0.5 mm for Ti and 1 mm for ASS as case F, dissimilar thickness of 0.5 mm for ASS and 1 mm for Ti as case I, and similar thickness of 0.5 mm as case J. Tensile shear force, failure mode, and micro-hardness were the metrics used to assess the dissimilar joint's soundness. The RSW variables utilized in the scope of the present investigation were "welding current, pressure, welding time, squeeze time, holding time, and pulse welding". Models from gradient boosting, CatBoost, and random forest machine learning (ML) algorithms were used to guarantee an accurate analysis, along with the artificial neural network regressions. Therefore, the ML and artificial neural network (ANN) models were trained using real data collected from 100 experimental RSW samples conducted under different RSW process parameters. Various transfer and training functions were applied with the multilayer perceptron employing the feed-forward-back propagation approach when building the ANN models. Also, for the first time in the RSW field, an estimation of the relative importance of the RSW variables regarding the predicted shear force is presented in the current study. Evaluating the experimental findings demonstrated that the highest shear force was 2.183, 2.589, 1.708, and 1.851 kN for cases E, F, I, and J. The micro-hardness data indicated that the nugget zone had a much higher hardness than the heat-affected zone (HAZ) and base metal (BM) zones, wherein case F showed the peak nugget hardness in comparison to the other cases. The best prediction model was found to be the ANN model when training the conjugate gradient with the Polak-Ribiere updates (Traincgp) training function with the hyperbolic tangent sigmoid transfer function (Tansig) with the mean squared error (MSE) and correlation coefficient (R²) values recorded as 0.01886 and 0.94973, respectively. However, the random forest algorithm gave the second best prediction of the MSE while the CatBoost and gradient boosting algorithms were third and fourth, respectively. The automotive and aerospace sectors are anticipated to benefit from the present research in applications associated with body component production that result in a superior strength-to-weight ratio.