REVIEW AND ANALYSIS ON RESISTANCE SPOT WELDED JOINTS OF STAINLESS-STEEL ALLOY USING MULTIOBJECTIVE 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 RSWjoints. 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. RSWwelding 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 spotwelded 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.