Modeling and Optimization of Surface Grinding Process Parameters Using Non-dominated Sorting Genetic Algorithm (NSGA)

Surface grinding is the most common process used in the manufacturing sector to produce smooth finish on flat surfaces. Surface quality and metal removal rate are the two important performance characteristics to be considered in the grinding process. The economics of the machining process is affected by several factors such as abrasive wheel grade, wheel speed, depth of cut, table speed and material properties. In this work, empirical models are developed for surface roughness and metal removal rate by considering wheel speed, table speed and depth of cut as control factors using response surface methodology. The mathematical models developed were checked for their adequacy using the analysis of variance and these models were used for the optimization. Since the influences of machining parameters on the metal removal rate and surface roughness are opposite in nature, the problem is formulated as multi objective optimisation problem. An efficient evolutionary optimisation algorithm, Nondominated sorting genetic algorithm (NSGA) is then applied for optimisation and to obtain the Pareto optimal set of solutions.