Abstract
Wire arc additive manufacturing (WAAM) is a technology that has revolutionized the fabrication of large metallic components in various industries. However, previous studies have predominantly relied on trial and error or statistical techniques to determine process parameters, neglecting the potential of evolutionary algorithms for optimizing. In the present work, detailed investigations were performed on the WAAM-based gas tungsten arc welding to predict and optimize the process parameters of the 316L austenitic stainless steel. The input process parameters viz., current, wire feed speed, travel speed, and wire feed direction play a significant role in determining the single bead geometry. The study investigated the influence of these parameters on bead characteristics (such as height to width ratio and percentage dilution) and surface roughness. Multiobjective optimization was carried out using both the response surface methodology-desirability approach (RSM-DA) and the non-dominated sorting genetic algorithm-II (NSGA-II). In the case of NSGA-II, the optimal parameters were determined through a fuzzy decision method and it was validated experimentally. The NSGA-II approach showed superior performance over the traditional RSM-DA model in predicting the responses. In addition, the thin-walled structure printed for the parameters suggested using RSM-DA and NSGA-II approach. In a comprehensive property-based comparative analysis, the NSGA-II optimized parameters demonstrated improved mechanical properties attributed to increased low-angle grain boundaries and kernel average misorientation values. This study highlights the effectiveness of evolutionary computation methods in achieving superior performance and mechanical characteristics in the fabrication of SS316L components.
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