PREDICTIVE MODELLING OF TIG WELDING PROCESS PARAMETERS: A COMPARATIVE STUDY OF TAGUCHI, FUZZY LOGIC, AND RESPONSE SURFACE METHODOLOGY (Nigerian Journal of Technology, 2025)
The optimisation design approach has garnered significant attention in experimental design due to its ability to develop unique designs that align with specific experimental objectives. Welding has been a unique joining process applied across various engineering fields. Optimisation of the welding process can significantly affect the quality of the welded joint. However, the choice of which optimisation technique to deploy for an experimental process is often a random decision taken by researchers. The aim of this study, therefore, is to perform a comparative study of the Taguchi, fuzzy, and response surface methodology optimisation techniques in the optimisation of tungsten inert gas welding parameters of current, voltage, and gas flow rate of mild steel. Results obtained from the analytical and statistical analyses, with MATLAB used for fuzzy logic modelling, Minitab used for ANOVA and main effect analyses, and Design Expert used for chart analysis, revealed that all three optimisation techniques are effective, but fuzzy logic (with a % error range of 1.8–5.4) as against RSM (with a % error range of 0.72–12.3) and Taguchi (with a % error range of 0.79–33.54) was the more robust and effective model, as its results were closer to actual experimental results than the other two traditional techniques.