Tool wear prediction by Regression Analysis in turning A356 with 10% SiC

In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased predominantly. The need for accurate machining of these composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In general 70% of the components need machining to attain the final shape. In the present work, the tool wear has been studied in this paper by turning the composite bars using HSS and Carbide tools. The paper presents the results of experimental investigation machinability properties of silicon carbide particle (SiC-p) reinforced aluminum metal matrix composite. The effect of machining parameters, e.g. cutting speed, feed rate and depth of cut on tool wear and surface roughness was studied. Machinability properties of the selected material were studied using HSS and Carbide tool material; surface roughness was generally affected by feed rate and cutting speed. Hence the tool wear were measured at different speed and feed conditions. Experimental data collected are tested with Multiple Regression Analysis. On completion of the experimental test, multiple regression analysis is used to predict the wear behavior of the system under any condition within the operating range.