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Journal of Intelligent Manufacturing

Erschienen in:

29.04.2014

Glowworm swarm optimization (GSO) for optimization of machining parameters

verfasst von: Nurezayana Zainal, Azlan Mohd Zain, Nor Haizan Mohamed Radzi, Muhamad Razib Othman

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2016

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Abstract

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.

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Titel: Glowworm swarm optimization (GSO) for optimization of machining parameters
verfasst von: Nurezayana Zainal
Azlan Mohd Zain
Nor Haizan Mohamed Radzi
Muhamad Razib Othman
Publikationsdatum: 29.04.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-014-0914-7

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