Grey relational analysis coupled with principal component analysis to optimize the machining process of ductile iron.

The strict specifications for specific applications of the ductile iron casting must qualify the defined standards with limiting tensile strength and the correct material composition limits. The undesired combinations of properties are reported during casting of ductile iron components because of the graphite presence as spheroids rather than as flakes. The design specific grades of ductile Iron comprising desired matrix microstructure can be produced by controlling the matrix microstructure with alloy additions around the graphite either by heat treatment or as-cast. The present research investigates the machinability of ductile iron grade EN-GJS-500-7and the development of model and optimized design for the major machining characteristic indexes viz. metal removal rate, cutting forces and surface roughness in turning process. The cutting parameters selected are cutting speed, feed rate and depth of cut. The grey relational analysis adopting grey relational grades as performance index is used to determine the optimal combination of cutting parameters. The set of experiments on the basis of response surface methodology (RSM) are employed in preparing the objective model to study the effect of the main turning parameters. Further principal component analysis is applied to calculate the weighting values corresponding to the selected machining characteristic indexes in order to determine their relative importance during the process. The results obtained from the confirmation experiments revealed that grey relational analysis coupled with principal component analysis is an appropriate approach and thus proposed to be a useful tool to improve the cutting performance in turning process. (c) 2017 Elsevier Ltd. All rights reserved.