Modeling and Optimization of Cutting Parameters in Dry Turning of Inconel 718 Using Coated Carbide Inserts

Abstract This work presents a statistical approach for optimization of dry turning parameters of Inconel 718. Based on Taguchi's L9 orthogonal array, turning experiments were carried out at various levels of cutting parameters to evaluate the performance measures such as cutting force, surface roughness and tool wear. The turning operations were carried out in a medium duty lathe machine with PVD coated carbide cutting insert. The optimal cutting conditions were determined using the Taguchi's Signal to Noise (S/N) ratio which was calculated for R a , R t , R z and F z according to the “smaller-the-better” criteria. Tool wear was analyzed using scanning electron micrograph. The statistical analysis of results using ANOVA reveals that feed rate and depth of cut has maximum weightage for affecting the responses. The mathematical model for the individual responses has been developed using regression analysis as a function of the cutting parameters as independent variables. The developed regression model shows a high determination coefficient i.e., R 2 = 0.912 for R a , R t , 0.943 for R z and 0.882 for F z which proves the model accuracy. The predicted value from the developed model and experimental values are found to be very close to each other justifying the significance of the model. Confirmation experiments were conducted on the optimum cutting conditions to illustrate the effectiveness of Taguchi's optimization technique.