Experimental investigation and parametric optimization of a milling process using multi-criteria decision making methods: a comparative analysis

This paper deals with exploring the influences of cutting speed, feed rate and depth of cut on material removal rate (MRR) and average surface roughness (Ra) during milling operation of aluminum 1100 alloy work material. The experiments are conducted based on Taguchi’s L₈ design plan. It is noticed that MRR increases and Ra deteriorates with higher cutting speed and feed rate. Thus, it becomes imperative to deploy multi-criteria decision making (MCDM) tools to identify the most appropriate combination of the considered milling parameters leading to a compromise solution resulting in higher MRR and lower Ra. Six popular MCDM techniques in the form of weighted sum model, weighted product model, weighted aggregated sum product assessment, multi-objective optimization on the basis of ratio analysis, evaluation based on distance from average solution and technique for order preference by similarity to the ideal solution are employed and comprehensively assessed here to search out the optimal machining condition for the said process. It is revealed that most of the adopted MCDM techniques are successful in identifying the corresponding compromise solution. Excellent values of Spearman’s rank correlation (≥ 0.93) prove high similarities between the ranking patterns derived using the considered MCDM techniques, except weighted sum model. It can be revealed from the detailed analysis that higher MRR can be obtained at an optimal parametric combination of cutting speed = 210 rpm, feed rate = 40 mm/min and depth of cut = 0.4 mm. On the other hand, an optimal parametric intermix of cutting speed = 170 rpm, feed rate = 40 mm/min and depth of cut = 0.4 mm would lead to better surface quality of the machined components.