Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites

Abstract

Reducing the energy consumption during machining of metal matrix composites (MMC) can significantly improve the environmental performance of manufacturing systems. To achieve this, calculation of energy consumption in the computerized numerical controlled (CNC) turning machine is required. It is important to minimize the power consumption and maximize tool life during machining operations, being performed on the CNC turning machine. However, this is challenging due to complexity of manufacturing systems and the nature of material being machined. This paper presents the findings of experimental investigations into the effects of cutting speed, feed rate, depth of cut and nose radius in CNC turning of 7075 Al alloy 15 wt% SiC (particle size 20–40 μm) composite. Design of experiment techniques, i.e. response surface methodology (RSM) has been used to accomplish the objective of the experimental study. The machining parameters such as cutting speed, feed rate, depth of cut and nose radius are optimized by multi-response considerations namely power consumption and tool life. A composite desirability value is obtained for the multi-responses using individual desirability values from the desirability function analysis. Based on composite desirability value, the optimum levels of parameters have been identified, and significant contribution of parameters is determined by analysis of variance. Confirmation test is also conducted to validate the test result. It is clearly shown that the multi-responses in the machining process are improved through this approach. Thus, the application of desirability function analysis in response surface methodology proves to be an effective tool for optimizing the machining parameters of 7075 Al alloy 15 wt% SiC (20–40 μm) composite. Result of this research work show that when turning is be carried out at values of machining parameters obtained by multi response optimization through desirability analysis route this will reduce power consumption by13.55% and increase tool life by 22.12%.