Publication

Abstract : Quality and productivity are the two important issues faced by any industry. In order to sustain in a competitive market, ensuring quality of the product at minimum cost is essential. Machined parts are greatly influenced by the surface quality during their useful life and productivity also plays an important role in the existence of any product in the market. In order to achieve that, the process parameters should be suitably regulated, but both the responses are conflicting in nature as Surface Roughness (Ra) is to be minimized and Material Removal Rate (MRR) is to be maximized. Hence, modeling and optimization of any process are getting attention by researchers. This paper presents an approach for determination of the best cutting parameters leading to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology (RSM) with Multi-Objective Genetic Algorithm (MOGA) in face milling of Al-6061 alloy. Thirty experiments have been conducted based on RSM with four parameters, namely Speed (v), Feed (f), Depth of Cut (d) and Coolant Speed (c.s) and three levels each. ANOVA is performed to find the most influential parameters on both MRR and Ra. It is revealed that f and d are the most influential parameters on MRR and c.s is the most influential parameter for Ra, respectively. Later the multi-objective optimization tool GA is used to optimize the responses. A pareto-optimal set of 21 solutions is obtained and validated through confirmation test.