Optimization of Face Milling Parameters for Material Removal Rate and Surface Roughness on Inconel 718 using Response Surface Methodology and Genetic Algorithm

Abstract

Face milling operation is used to remove the materials in the form of chips on the work piece surface in order to improve the surface quality. It is one of the important machining processes for achieving high flatness and low surface roughness of prismatic parts. So, this work investigates the parameters influences on Material Removal Rate (MRR) and Surface Roughness (SR) in newer material Inconel 718. Inconel 718 has wide applications in aerospace industry, particularly in the hot sections of gas turbines, due to their high-temperature strength and high corrosion resistance. Because of these properties, obtaining the better surface quality will be the challenging one. However, the milling parameters such as spindle speed, feed rate and depth of cut decide the productivity and the quality of part in face milling. Hence, this work aims to formulate the relationship between input and response variables for improving the face milling performances. For identifying the effects of input parameter and interaction effect of process parameter on MRR and SR, the Response Surface Methodology (RSM) is used. Regression analysis is conducted for building empirical model. The performance of developed regression models are verified with experimental results. Verification results show the developed models have best agreement with experimental results. The developed models are used for achieving the best input parameters by using Genetic Algorithm (GA). Finally, the results of GA have good agreement with experimental results.