Optimization of machining parameters for turning AISI 4340 steel using Taguchi based grey relational analysis

In metal cutting, the choice of cooling method influences the deformation mechanism, which is related to the cutting forces, tool wear and surface finish of the parts. The deformation mechanism of AISI 4340 steels machining conditions is known to be very different from that of commonly used industrial materials. Therefore, the effect of cutting parameters and cooling methods on cutting forces, tool wear and surface roughness in machining of AISI 4340 steel is of particular interest. This paper investigates experimentally and analytically the influence of various process parameters, given as cutting speed (v), feed rate (f) and different cooling conditions (i.e. dry, wet and cryogenic in which liquid nitrogen used as a coolant) using uncoated tungsten carbide insert tool on three major characteristics (cutting force, tool wear and surface roughness) of a turned AISI 4340 steel part. The Taguchi's L9 orthogonal array, analysis of variance (ANOVA) and grey relational analysis (GRA) are executed to study the effects, significance, percentage contribution and optimum settings of given process parameters. The results obtained show that the machining performance can be improved by this approach.