Trade-off analysis of machinability of steel alloy AISI 304L using Taguchi-grey integrated approach

Energy analysis during machine tool operations in manufacturing sector is becoming one of the prominent research avenues due to rising energy costs and environmental impact brought on by high energy consumption. Nevertheless, surface quality and production rates also hold significant value for overall optimization of any manufacturing setup. In fact, machinability of a material can only be assessed by collectively optimizing all machining responses. To address this shortcoming, multi-objective optimization of specific cutting energy, surface roughness, and material removal rate during turning of AISI 304L stainless steel was conducted at diverse machining parameters. Influential variables to include depth of cut, feed rate and cutting speed were taken as the input parameters. Efficient Taguchi design of experimentation was employed for formulation of L16 orthogonal array. Effect of each cutting parameter on the response variables was investigated using main effects plot and analysis of variance was done to ascertain influence of each input through its contribution ratio. Feed rate was found to be the most influential input with 88.94% contribution ratio for surface roughness and 57.29% contribution ratio for specific cutting energy. Cutting speed had contribution ratio of 31.56% for specific cutting energy. Subsequently, regression analysis was used to develop second-order mathematical models (95% confidence level) to correlate input parameters with output responses. Contour plots were developed for visual comprehension of the relationship between input parameters and output responses. Grey relational analysis was used for multi objective optimization to identify optimum cutting combination which came to be at 1.4 mm depth of cut, 160 m/min cutting speed and 0.25 mm/rev feed rate.