Multi-objective Optimization on Abrasive Water Jet Machining of AA6082 using Teaching Learning Based Optimization

Abstract

An evolutionary algorithm provides an efficient and systematic method of generating and equating the machining parameters in order to attain optimal machining. An optimal solution is to reduce the numerous objectionable values and to exploit the most substantial enviable effect. Many practical assessment problems include numerous and contradictory objectives, which required to be optimized concurrently while regarding several complex constrictions. Aluminium alloys are widely used in various automotive sectors due to their superior properties and high strength. In this study, AA6082 aluminium alloy was investigated its machinability using the abrasive water jet cutting process. Teaching Learning Based Optimization (TLBO) was used to optimize the experimental parameters by varying the parameter influences such as abrasive feed, stand-off distance and transverse speed. The effect of depth of cut, hardness and surface roughness was investigated by forming a multi-objective optimization by Assignment of Weights method.