Optimization of turning process parameters using Multi-objective Evolutionary algorithm

Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective Evolutionary Algorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constraints, which depends on the turning parameters, are optimized using Genetic algorithm (GAs). The Pareto-optimal front of the bi-objective problem is obtained using Non-dominated Sorting Genetic Algorithm (NSGA-II). The extreme and intermediate points of Pareto optimal front is verified using Real coded Genetic Algorithm (RGA) as well as ε-constraint method. The performance of NSGA-II is found to be more effective and efficient as compared to micro-GA. Innovization study carried out to correlate cutting parameters with the aforementioned objective functions. The effect of cutting speed is found more as compared to feed rate and depth of cut.