Hybridization of Principal Component Analysis-Entropy-TOPSIS Techniques for Minimization of Angular Deviation in Gas Metal Arc Welded Stainless Steel Plates

Abstract

The current study involves optimizing gas metal arc welding input parameters by hybridization techniques such as principal component analysis, entropy, and TOPSIS for minimizing the angular distortion resulting from the welding process. Structural steel has been considered as the research material by varying the process parameters such as electrode angle with workpiece, time gap between passes, wire feed rate, and welding speed, each varying with five levels. Experimental layout has been prepared through response surface methodology with 125 experimental trials combining different parameters. The measured angular distortion of the welded plates has been considered for multi-criteria decision making to find the optimal parameter combination for reducing the angular distortion. The optimal combination comprising 110 degrees of electrode to workpiece angle, 25 min time gap, 5.75 mm/min wire feed rate, and 10.8 cm/min welding speed is found to hold top ranking (A2B2C1D2) from both PCA and entropy weighted values through TOPSIS technique. The closeness coefficient value has been trained and tested through supervised ML techniques. AdaBoost algorithm outperformed other regression algorithms with excellent evaluation scores. The microstructure analysis of the welded plates reveals the presence of coarse and fine structures in the welded area.

Keywords

• GMAW
• supervised ML techniques
• PCA
• entropy
• TOPSIS