Glowworm swarm optimization (GSO) for optimization of machining parameters

被引：0
作者：
Nurezayana Zainal
Azlan Mohd Zain
Nor Haizan Mohamed Radzi
Muhamad Razib Othman
机构：
[1] Universiti Teknologi Malaysia (UTM),Faculty of Computing
来源：
Journal of Intelligent Manufacturing | 2016年 / 27卷
关键词：
Machining; Optimization; Surface roughness; Glowworm swarm optimization; Taguchi method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study proposes glowworm swarm optimization (GSO) algorithm to estimate an improved value of machining performance measurement. GSO is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. To the best our knowledge, GSO algorithm has not yet been used for optimization practice particularly in machining process. Three cutting parameters of end milling that influence the machining performance measurement, minimum surface roughness, are cutting speed, feed rate and depth of cut. Taguchi method is performed for experimental design. The analysis of variance is applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. GSO has improved machining process by estimating a much lower value of minimum surface roughness compared to the results of experimental and particle swarm optimization.
引用
收藏
页码：797 / 804
页数：7
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