Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: modelling and optimization approach

Abstract

The statistical mathematical models are developed to investigate the influence of cutting parameters on surface roughness, tool wear, cutting force, tangential force and the work piece vibration in boring of AISI 4340 steels. A full factorial design of experiments is used to conduct 27 experiments on AISI 4340 as the work piece material and TiCN–Al2O3–TiN multi-layered coated carbide inserts. Online data acquisition of cutting forces on the cutting tool and the work piece vibrations are measured by using piezo-electric dynamometer and laser Doppler vibrometer, respectively. This paper proposes optimization method using grey relational analysis (GRA) and predictive models like support vector machine and response surface method are used to predict the GRG values and optimize the machining parameters. The GRA is used for converting multi response optimization problem into optimization of single objective of grey relational grade (GRG). Finally confirmation test was performed and also optimized the machining parameters to minimize the surface roughness (Ra), tool wear (VB), cutting force (Fx), tangential force (Fz) and work piece vibration (VA).