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A classical-cum-Evolutionary Multi-objective optimization for optimal machining parameters

Optimal machining parameters are very important for every machining process. This paper presents an Evolutionary Multi-objective Genetic Algorithm based optimization technique to optimize the machining parameters (cutting speed, feed and depth of cut) in a turning process. The effect of these parameters on production time, production cost and surface roughness (which are conflicting to each other) are mathematically formulated. The non-dominated sorting genetic algorithm (NSGA-II) is used to get a Pareto-optimal front of the machining problem. The Pareto-optimal points are checked using ε-constraint single objective GA as well as using a classical optimization (SQP) method. An analysis of the obtained points is carried out to find the useful relation between the objective function and variable values.