Multi-criteria decision making for evaluating a suitable MOO technique to improve the performance of AISI 1040 carbon steel and Ti-6Al-4V alloy

The objective of this work is to compare the machining performance of AISI 1040 Carbon Steel and Ti-6Al-4V alloy for a slot cutting operation using a 3-axis vertical milling machine. A set of 16 experiments was conducted, considering four factors: Feed rate, depth of cut, spindle speed, and coolant concentration, to remove material up to a finite depth. Three multi-objective optimization techniques namely, Grey Relational Analysis (GRA), Multi-Response Signal-to-Noise Ratio (MRSN Ratio), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were employed to determine the optimal machining conditions by maximizing the material removal rate and minimizing surface roughness and material hardness. It has been observed that the response performance index varies with different optimization techniques as well as different workpiece materials. The most significant contributions of each technique were evaluated through ANOVA analysis. A prediction model was developed using linear regression analysis. Finally, comparing the results of the three objective optimization techniques, GRA gives better performance than both TOPSIS and MRSN by effectively satisfying two objectives (i.e., maximizing material removal rate and minimizing surface roughness) for AISI 1040 Carbon Steel. In contrast, the MRSN technique provides better results than GRA and TOPSIS by effectively satisfying two objectives (i.e., maximizing material removal rate and minimizing material hardness) for Ti-6Al-4V alloy.