An evolutionary-based hybrid approach for simultaneous optimisation of multiple responses in self-propelled rotary turning process

Abstract

This investigation focused on optimisation of self-propelled rotary turning process conditions through an integrated multi-objective optimisation approach. The machining experiments are conducted for machining hardened EN24 (SAE4340) steel with TiN-coated tungsten carbide rotary insert. Two important machining responses such as surface roughness and metal removal rate were measured by varying the machining variables such as depth of cut, inclination angle of the rotary tool, feed rate and spindle speed. To deal with the simultaneous optimisation of two conflicting process characteristics governed by four process variables, an evolutionary-based hybrid optimisation approach is proposed. The method integrated gray relational analysis (GRA) for deriving the overall process performance index, response surface methodology (RSM) to analyse the significance and variation process variables and genetic algorithm (GA) to derive the optimal values of the process variables which will give maximum process performance. The derived optimal machining conditions were confirmed through validation machining experiments. [Submitted 15 August 2017; Accepted 16 March 2018]