Optimization of friction stir welding process parameters using soft computing techniques

In welding processes, desired weld quality is highly dependent on the selection of optimal process conditions. In this work, the influence of input parameters of friction stir welding process is studied using Taguchi method and full factorial design of experiment. The experimental data set is used to develop multilayer feed-forward artificial neural network (ANN) models using back-propagation training algorithm. These models are used to predict weld qualities as a function of eight process parameters. The weld qualities of the welded joint, such as ultimate tensile strength, yield stress, percentage elongation, bending angle and hardness, are considered. In order to offline optimize these quality characteristics, four evolutionary algorithms, namely binary-coded genetic algorithm, real-coded genetic algorithm, differential evolution and particle swarm optimization, are coupled with the developed ANN models. The optimized quality characteristics obtained from these proposed techniques are compared and verified with experimental results.