Optimization of friction stir welding process parameters using soft computing techniques
作者： Nizar Faisal Alkayem , Biswajit Parida , Sukhomay Pal
DOI: 10.1007/S00500-016-2251-6
关键词:
摘要: In welding processes, desired weld quality is highly dependent on the selection of optimal process conditions. this work, influence input parameters friction stir studied using Taguchi method and full factorial design experiment. The experimental data set used to develop multilayer feed-forward artificial neural network (ANN) models back-propagation training algorithm. These are predict qualities as a function eight parameters. welded joint, such ultimate tensile strength, yield stress, percentage elongation, bending angle hardness, considered. order offline optimize these characteristics, four evolutionary algorithms, namely binary-coded genetic algorithm, real-coded differential evolution particle swarm optimization, coupled with developed ANN models. optimized characteristics obtained from proposed techniques compared verified results.
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