OPTIMIZATION OF THERMAL SPRAYING WELDING USING ENTROPY-<i>k</i>-MEANS ANALYSIS FOR SUSTAINABLE MANUFACTURING WITH BIOMASS-BASED NANOCOMPOSITE COATINGS

In sustainable welding, using eco-friendly coating materials is crucial, especially in the thermal spraying welding process which employs different materials. The welding arc’s radiation affects the material’s surface, while convection currents help distribute heat. This study used k-means clustering, computer modeling, and entropy-based simulation to analyze the thermal spraying process, showing its effectiveness in optimizing welding outcomes. Entropy weightage analysis revealed that environmental impact achieved top priority at 58%, followed by heat release rate at 19%, material strength retention at 18%, and welding efficiency at 5%. Among various biomass-based nanocomposites, Corn starch polymer coating emerged as the best option, exhibiting the minimum centroidal distance of 1.73. The optimized outcomes achieved through this analysis were a welding efficiency of 90%, heat release rate of 12[Formula: see text]kJ/s, environmental impact of 0.1[Formula: see text]kg CO 2 e/unit, and material strength retention of 78%. In conclusion, this study highlights the effectiveness of computer modeling and simulation, combined with entropy-based k-means analysis, in evaluating the thermal spraying welding process. The findings provide a solid framework for improving welding efficiency, contributing to advancements in sustainable manufacturing.