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

Wen-Lin Chu,Min-Jia Xie,Yu-Syong Guo,Li-Wei Wu,Her-Terng Yau

doi:10.1109/access.2020.3022648

Wen-Lin Chu, Min-Jia Xie + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3022648

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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.

Highlights

CNC machined workpieces must reach a certain level of precision to be considered as a primary processing and the parameters are often chosen by an experienced machine operator who sets the machine
By using the Hybrid Taguchi-Genetic Algorithm for multi-objective optimization, we get many sets of cutting parameters from the results of the multi-objective optimization, and we choose the best processing parameters according to the quality of the surface roughness
The experiment is based on the Pareto-optimal solution obtained from the multi-objective optimization of weighted combinatorial method with HTGA, and the best process parameters for the specified accuracy have been found

Summary

Introduction

CNC machined workpieces must reach a certain level of precision to be considered as a primary processing and the parameters are often chosen by an experienced machine operator who sets the machine This method frequently produces the required specifications, it may not be the best for large scale production [1]. That the finished surface roughness should be within the required tolerance depends on the settings, and these in turn, depend on the experience of the person making them [2]. Another processing quality indictor that needs attention is the MRR. The cost of the production process needs consideration as well as the overall cost of the process to the environment and of the materials generated [4]

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Results

Conclusion