Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling

Abstract

The resistance spot welding (RSW) process is still widely used to weld panels and bodies, particularly in the automotive, railroad, and aerospace industries. The purpose of this research is to examine how RSW factors such as welding current, welding pressure, welding time, holding time, squeezing time, and pulse welding affect the shear force, micro-hardness, and failure mode of spot welded titanium sheets (grade 2). Resistance spot welded joints of titanium sheets with similar and dissimilar thicknesses of 1–1 mm, 0.5–0.5 mm, and 1–0.5 mm were evaluated. The experimental conditions were arranged using the design of experiments (DOE). Moreover, artificial neural network (ANN) models were used. Different training and transfer functions were tested using the feed-forward backpropagation approach to find the optimal ANN model. According to the experimental results, the maximum shear force was 5.106, 4.234, and 4.421 kN for the 1–1, 0.5–0.5, and 1–0.5 mm cases, respectively. The hardness measurements showed noticeable improvement for the welded joints compared to the base metal. The findings revealed that the 0.5–0.5 mm case gives the highest nugget and heat-affected zone (HAZ) hardness compared to other cases. Moreover, different failure modes like pull-out nugget, interfacial, and partial failure between the pull-out nugget and interfacial failure were noticed. The ANN outcomes based on the mean squared error (MSE) and coefficient of determination (R2) as validation metrics demonstrated that using the Levenberg–Marquardt (Trainlm) training function with the log sigmoid transfer function (Logsig) gives the best prediction, where R2 and MSE values were 0.98433 and 0.01821, respectively.

Keywords

• resistance spot welding
• titanium sheet
• artificial neural network
• shear force
• micro-hardness
• unequal thicknesses