Application of grey relational analysis based on Taguchi method for optimizing machining parameters in hard turning of high chrome cast iron

High chrome white cast iron is particularly preferred in the production of machine parts requiring high wear resistance. Although the amount of chrome in these materials provides high wear and corrosion resistances, it makes their machinability difficult. This study presents an application of the grey relational analysis based on the Taguchi method in order to optimize chrome ratio, cutting speed, feed rate, and cutting depth for the resultant cutting force (F (R)) and surface roughness (R (a)) when hard turning high chrome cast iron with a cubic boron nitride (CBN) insert. The effect levels of machining parameters on F (R) and R (a) were examined by an analysis of variance (ANOVA). A grey relational grade (GRG) was calculated to simultaneously minimize F (R) and R (a). The ANOVA results based on GRG indicated that the feed rate, followed by the cutting depth, was the main parameter and contributed to responses. Optimal levels of parameters were found when the chrome ratio, cutting speed, feed rate, and cutting depth were 12%, 100 m/min, 0.05 mm/r, and 0.1 mm, respectively, based on the multiresponse optimization results obtained by considering the maximum signal to noise (S/N) ratio of GRG. Confirmation results were verified by calculating the confidence level within the interval width.