Coupled Taguchi-Pareto-Box Behnken Design-Grey Wolf Optimization Methods for Optimization Decisions when Boring IS 2062 E250 Steel Plates on CNC Machine
doi: 10.14456/mijet.2024.4
Keywords:
Grey wolf optimisation, CNC machine, Taguchi-Pareto, fitness values, parametersAbstract
Optimizing process parameters in boring operation is extremely important as aids to maintain high resource conservation and efficiency and the free flow of boring data while optimally using boring resources. With optimal parameters, real-time information is offered to process engineers for the practical control the boring operation thus reducing the cost of boring operations. This article presents an investigation on the use of optimization and prioritization in the boring process of IS 2062 E250 steel plates using coupled Taguchi-Pareto-Box Behnken Design-grey wolf optimization approach. The experimental data, drawn from the literature, was initially provided by Patel and Deshpande on CNC TC machine. The objective function, constraints, population size, number of iterations, and fitness function were determined. Then the solution for the grey wolf optimization is generated using the python programming language. Three representative parameters of speed, feed and depth of cut were foundations of the Taguchi’s experimental design used for solving the problem. The optimal parameters were determined from the experiments. For the first time, the coupled Taguchi-Pareto-Box Behnken Design-grey wolf optimization method to make optimization decisions for the boring process. Using 50 iterations and 200 wolfs, the best fitness value of wolves at the end of the 50th iteration is 872728.53 when the objective function is generated from optimized Box Behnken Design parameters. It also has an optimal solution of speed, feed, depth of cut and nose radius as 800rpm, 0.06,1 and 0, respectively. However, on the application of the regression equation from the Box Behnken Design to form an objective function, using the same 50 iteration and 200 wolves the best fitness value of wolves at the end of 50th iteration is -51.49 while the optimal parameters are 1189.58, 0.089, 1.22, 0.55 for the speed, feed, depth of cut and nose radius, respectively. Hence, the outcome of this study may be a route to reducing time and money associated with unnecessary usage of non-optimal boring data during operations planning decisions.
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