Evolutionary Algorithm to Optimize Process Parameters of Al/Steel Magnetic Pulse Welding

The Magnetic Pulse Welding (MPW) process uses only electromagnetic force to create a solid-state metallurgical bond between a working coil and outer workpiece. The electromagnetic force drives the outer tube to collide with the inner rod, resulting in successful bonding. However, due to the dissimilarity of the MPW joint, only a portion of the interface forms a metallurgical bond, which affects the quality of the joint. Therefore, the purpose of this study is to analyze the effects of process parameters on joint quality through experimental work using RSM. Furthermore, an optimization algorithm is utilized to optimize the process parameters used in magnetic pulse welding. A1070 aluminum and S45C carbon steel were used as the materials, while peak current, gap between working coil and outer tube, and frequency were chosen as the process parameters for MPW. The welding conditions are determined through experimental design. After welding, the maximum load and weld length are measured to analyze the effect of the process parameters, and a prediction model is developed. Specifically, to achieve a high-quality joint, the process parameters are optimized using the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA). The results reveal that the peak current is a significant parameter, and the developed prediction model exhibits high accuracy. Furthermore, the ICA algorithm proves very effective in determining the process parameters for achieving a high-quality Al/Steel MPW joint.