Multi-response optimization in small scale resistance spot welding of titanium alloy by principal component analysis and genetic algorithm

The present study aims to solve multi-response optimization problem in small scale resistance spot welding of 0.4-mm-thick TC2 titanium alloy sheets. Welding parameters of electrode force, welding current and welding time were arranged by the central composite experimental design. Principal component analysis was conducted first on quality indicators of nugget diameter, failure load, failure displacement and failure energy. Different weighted principal components selection strategies were performed to calculate the composite weld quality indexes. Multiple stepwise regression analysis was applied to develop the mathematical function for weighted principal components prediction. Welding parameters effects and sensitivity analysis on the composite weld quality index were discussed. Welding current was found the most significant factor affecting weld quality. Optimum welding parameters determined by genetic algorithm were validated through experiments. The first principal component was supposed as the most effective and simplest quality index. Weld quality was considerably improved based on the proposed model.