Parametric optimization in turning of AA2014/Al2O3 nano composite for machinability assessment using sensors

Abstract

Aluminium metal matrix nano-composites (AMMNC) reinforced with various weight percentages of micro and nano Al2O3 particles have outstanding mechanical properties for variety of industrial, aerospace and automotive applications. However, the machinability of AMMNC is still a problem. The presence of abrasive particulates behaves like cutting edge for the tool during machining, resulting in unexpected tool wear, high tool workpiece interface temperature, enormous amount of cutting forces and vibration. In this study, experimental investigations were carried out to assess the machinability in turning of AMMNC under dry condition. A mathematical model was developed to predict the responses, namely surface finish, tool wear, work-tool interface temperature and cutting forces using linear regression analysis. Taguchi based optimization technique has been used to optimize the turning parameters for obtaining the best surface roughness of the components with reduced tool wear, temperature and cutting force. Multiple sensors were used to measure the responses to identify the optimum machining parameters. The frequency domain analysis is carried out to predict the dominant frequency band. Chip morphology analysis is also carried out to assess the machinability. Thus, this work helps to know about the effect of combined micro and nano-particles in the properties of AMMNC and its machinability.