Optimization and Selection of Boring Process Parameters for IS 2062 E250 Steel Plates Using Hybrid Taguchi-Pareto Box Behnken-Genetic Algorithm Method

Abstract

The integrated Taguchi-Pareto-Box-Behnken design (TP-BBD) method has been recognized as an effective method for boring operation optimization. Yet it has further optimization opportunities even with less information availability. In this study, the genetic algorithm (GA) was integrated with the TP-BBD method to form a novel TP-BBD-GA method to effectively deal with the paucity of boring data and generate multiple optimal solutions. Numerical simulation coupled with experimental data analysis was conducted to ascertain the effectiveness of the proposed method using literature data. To combine the procedure of the constituent methods, the authors analysed the literature data with the Taguchi-Pareto method. Then the output was used as inputs to the Box Behnken design method. Afterwards, linear programs with objective functions and constraints were formulated and introduced into the genetic algorithm structure and then solved using the python language. The results revealed that the proposed method exhibits good performance for boring operations as it predicts the best parameter i.e. speed, feed rate, depth of cut and noise radius values for optimal surface roughness values. This article offers a unique contribution to the boring literature since it examines an additional optimization procedure to the existing one. The study analyzes the optimization behaviour of the IS 2062 E250 steel plates in the boring process and gives an easy procedure for process engineers on improving the boring operations.