Multi-objective Optimization and Modelling of AISI D2 Steel Using Grey Relational Analysis and RSM Approaches Under Nano-based MQL Hard Turning

Abstract

AISI D2 is a high-carbon and high-chromium cold-work tool steel, which exhibits various properties such as high wear resistance, high hardness, high strength and rapid strain hardening. Because of such kind of properties, the AISI D2 cold work steel is considered as a difficult material to be machined. The selection of process parameters plays a very vital role in terms of output quality characteristics such as surface roughness and cutting force. The current work focuses on the application of grey relation theory combined with central composite design (CCD) for optimizing the process parameters for machining AISI D2 hardened steel (54 ± 1HRC). TiAlN PVD-coated inserts of different geometry are selected to machine the AISI D2. Surface roughness and cutting force are chosen as an output response. By executing the confirmation experiments, it is observed that grey relational analysis is a valuable methodology to find out the optimum level of parameters. The optimum parameters are cutting speed 105 m/min, feed 0.04 mm/rev, depth of cut 0.4 mm and rake angle 1°. Regression-based models are also developed for predicting the cutting force and surface roughness values using the RSM approach.