A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert

The prime factor for selecting equipment is its performance capability and reliability without compromising on quality. Materials for aerospace application such as aluminum and its alloys have limited applications because of their complications in machining, effectively and economically. There is no further development in raising the effectiveness above the optimal level in cutting tool materials. The surface roughness influences the determination of the quality of the product. The present study focuses on finding optimal end milling process parameters by considering multiple performance characteristics using grey fuzzy approach. In this work, Aluminum Alloy 6082T6 (AA6082T6) is used as workpiece material which was end milled using Aluminum Chromo Nitride (AP3) coated milling insert. Three process performance parameters namely Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR) were optimized. The grey output is fuzzified into five membership functions and also with twenty-seven rules. Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) shows that the maximum contribution in the depth cut is (31.785%) followed by feed (28.212%). Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been developed with the help of the same input values compared to the performance of the fuzzy logic model. With the help of detailed analysis, it has been found that the fuzzy logic based model gives more reasonable results when compared to ANFIS model.