The Optimization of Lathe Cutting Parameters Using a Hybrid Taguchi-Genetic Algorithm

Abstract

In this paper, the multi-objective Hybrid Taguchi-Genetic Algorithm is used to search for the best processing parameters with specified processing accuracy. The experimental cutting parameters used for the L9 orthogonal table process are cutting depth, cutting velocity and feed rate. The surface roughness of the machined workpiece surface was measured according to the standard of centerline average roughness. The Material Removal Rate will be calculated by measuring the diameter of the processed workpiece from the formula to give the Material Removal Rate. A linear regression model is constructed from the processed quality and the processing parameters of the orthogonal table, and the reliability of the model is confirmed by analysis of variance. A Hybrid Taguchi-Genetic Algorithm was used to calculate the optimal cutting parameters for multi-objective processing. The results of the experiments indicate that Hybrid Taguchi-Genetic Algorithm gave better convergence and robustness than the conventional Genetic Algorithm using the same number of iterations. This process produces multiple combinations of optimal cutting parameters for material removal rate and surface roughness. As the enhancement of material removal rate improved efficiency on the production line, the optimal cutting parameters were based on the tolerance range of Ra $1.6\mu \text{m}$ to $3.2\mu \text{m}$ according to the international standard of surface roughness. After actual processing with the selected optimum cutting parameters, the quality of processing is even better than the experimental design of the L9 Orthogonal table.

Keywords

• Regression analysis
• genetic algorithm
• multi-objective optimization