Process optimisation of multi response characteristics of GTA welding joint for copper-SS304 using multi criteria decision making method - Alam Z.; Panda S.S.; Tanmay; Misra J.P. (My University, 2025)
Joining copper to stainless steel poses significant difficulties because of their large differences in their thermo-physical characteristics and the presence of a miscibility gap in the Fe-Cu system. This study introduces a novel approach to optimizing GTA welding of pure copper and SS304 stainless steel through a hybrid multi-criteria decision-making (MCDM) method. The optimization process combines Grey Relational Analysis (GRA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), with entropy-based weighting, to fine-tune key welding parameters for enhanced tensile strength and hardness. The experimental design was structured utilizing a Taguchi L9 orthogonal array, with welding current, welding speed, and gas flow rate as the primary variables. Entropy weighting determined the importance of each response, while the hybrid GRA-TOPSIS method ranked the welding parameter sets. This led to the identification of an optimal combination that maximized mechanical performance. The significance of each parameter's effect on the welding results was analyzed using ANOVA. Validation through confirmation tests demonstrated that the hybrid entropy-weighted GRA-TOPSIS method significantly outperformed compared to conventional TOPSIS. Additionally, microstructural analysis, morphological studies, Energy Dispersive Spectroscopy (EDS), and X-ray Diffraction (XRD) were performed on the welded joints under optimal conditions, while corrosion testing was conducted for both the initial and optimal parameters. The findings demonstrate that corrosion resistance under optimal conditions is significantly better than that of the initial conditions for the Cu-SS weld joints. These comprehensive assessments confirmed the high integrity of the weld joints as well as their superior mechanical performance. © 2025 Elsevier Ltd