REVIEW AND ANALYSIS ON RESISTANCE SPOT WELDED JOINTS OF STAINLESS-STEEL ALLOY USING MULTI-OBJECTIVE OPTIMIZATION
In industries such as automobile and railway manufacturing, the resistance spot welding (RSW) process is used extensively. An electric current is used in conjunction with mechanical pressure in the creation of RSW joints. Ferritic stainless steels are extensively utilized in the construction of buses and trains because of their excellent strength and corrosion resistance. Stainless steel austenitic is regarded to be a more expensive alternative. RSW welding of these steels is frequently required in the rail coach manufacturing industry, whether it is with the same material or with materials that are dissimilar. RSW has been the subject of a massive amount of research. Materials like low carbon steel, advanced high-strength steel, and austenitic stainless steel are frequently discussed in RSW literature. Ferritic stainless steel spot-welded joints have a dearth of data on their mechanical and microstructural properties. For ferritic stainless steel welds, optimization of the RSW method is absent in the literature. Size of the nuggets utilised in the welding process has a direct impact on the mechanical qualities of ferritic stainless steel weld joints, such as load bearing and energy absorption capacity. In the weld fusion zone, microhardness values exceeded those of base metal. The weld's high-temperature heat-affected zone was found to have grain growth. Columnar ferrite was found to be the dominant microstructure in the fusion zone. The relationship between welding current and failure mode was examined. Investigations were made into the correlations between input parameters and output quality characteristics. Multi-response optimization based on Taguchi’s quality loss function approach was used to optimize the RSW process of AISI steel to achieve maximum weld strength with minimum electrode indentation. The obtained results were validated with a confirmation test. Furthermore, a linear first-order surface response model was developed using response surface methodology (RSM) and MINITAB software, for correlating both peak load and indentation, with the input parameters from the experimental data.
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