Multi-response Optimization in Face Milling of EN-31 Steel Using Hybrid CRITIC and DFA Technique

High-temperature development while machining difficult to machine metals like EN-31 steel is the principal cause of lower tool life and poor surface integrity. The use of cutting fluid is necessary for lowering the temperature of cutting, but excess use of cutting fluid harmfully affects the environment and health of the worker. Minimum quantity lubrication (MQL) presents itself as a solution to this problem. In this work, face milling experimentation is executed on EN-31 steel with uncoated carbide inserts under the MQL environment. The machining parameters are coupled with the MQL variables as per Taguchi-based orthogonal array L27. CRITIC method is used to calculate the weight of each output response for multi-response optimization. Then, the Taguchi-based desirability function analysis (DFA) method is utilized to simultaneously optimize the tool flank wear and surface roughness. The ideal parametric combination for the multi-response factor is obtained which is further authenticated using confirmation experiments. During confirmation experiments, an improvement of 2.58% in the value of the combined multi-response factor is reported optimum parameter setting.