Correlation of material properties and machine learning techniques on a TIG cladded SS 316L base alloy with SS 304 filler

Tungsten Inert Gas (TIG) cladding is the most promising and feasible technique adopted for the weldability of stainless-steel alloys. In this work, SS 304 fillers were deposited over the SS 316L through the TIG cladding process, and the alloy behavior was studied. The optical study, tensile, and microhardness values of TIG-cladded SS 316L base metal with the SS 304 filler material correlate with the machine learning technique. The Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model is utilized in this study to predict the values theoretically. The correlation between the experimental values and theoretical values is shown to be in good agreement. The enhancement in the mechanical properties of TIG-cladded SS 316L alloy is found to be sounder and more reliable than that of the SS 316L base alloy. The suitable selection of process parameters and the type of cladding (single or double pass) had a significant effect on the improvement of material properties to a greater extent. From the experimental results, the increase in the tensile and microhardness values was found to be 13.2 % and 42.5 %. However, a wide range of methodologies/techniques are available for the theoretical prediction of values, whereas the machine learning technique had a significant effect on the reliable prediction of values. Therefore, it is found that the adoption of machine learning techniques can help flexibly for the identification of a reliable and optimal process parameter in a fabrication process.