# Evolutionary Algorithm to Optimize Process Parameters of AI/Steel Magnetic Pulse Welding
###### Abstract
[14] analyzed the fatigue fracture properties of Al/steel joints using SEM, TEM, and EDS. Recently, [PERSON] et al. [15] performed non-destructive evaluation and investigated the corrosion of Al/steel joints through compositional analysis. [PERSON] et al. [16] discussed the mechanical properties and element diffusion in an additively manufactured 316L to AA5052 MPW joint. This process utilized electromagnetic force, making numerical analysis highly effective for understanding and applying it on site. As a result, researchers have conducted studies using numerical simulations. [PERSON] et al. [17] used a high-accuracy FE model to investigate the characteristics of Al/steel magnetic pulse tubular joints according to discharge time. [PERSON] et al. [18] analyzed the effects of gaps between workpieces and of input voltage energy on the mechanical and microstructural properties of joints. [PERSON] et al. [19], utilizing numerical simulations, performed magnetic pulse welding (MPW) with a multiple-seam field-shaper to generate Al/steel joints with uniform circumferential deformation distribution. However, optimizing the MPW process using experimental and numerical methods is challenging due to the need for a large number of trials. Figure 1: Schematic layout of magnetic pulse welding [12]. Nowadays, optimization methods are widely used to address problems in welding processes. Notably, response surface methodology (RSM) and the Taguchi method [20; 21] are commonly used to determine the relationship between input parameters and output responses, such as joint quality. These methods are effective in minimizing the number of experiments needed for analysis. [PERSON] et al. [1] used RSM to develop a prediction model for burst pressure in dissimilar joints, such as Al/steel and Al/Cu. [PERSON] et al. [22] used RSM to investigate the effects of process parameters on the microstructural evolution and mechanical properties of stainless steel and copper joints. Several evolutionary algorithms have been developed to address optimization problems across a range of scientific and engineering disciplines. These algorithms include the Genetic Algorithm (GA) [23; 24; 25; 26; 27], the ant colony optimization algorithm, the bee algorithm, and the Imperialist Competitive Algorithm (ICA) [28; 29; 30; 31; 32]. These algorithms establish robust process parameter combinations for achieving superior joint quality. However, the application of evolutionary algorithms to select optimal process parameters in the MPW process has not been explored. Therefore, the objective of this study is to use an evolutionary algorithm to determine the optimal process parameters for achieving maximum weld length. For this, the peak current, gap between workpieces, and frequency were selected as input parameters, while the maximum load and weld length were chosen as output responses. The relationship between the input parameters and the output responses was first examined. Then, a prediction model was developed for application to the evolutionary algorithm. Both ICA and GA were employed as evolutionary algorithms; the determined optimal process parameters were confirmed through verification experiments.