Glowworm swarm optimization (GSO) for optimization of machining parameters

Nurezayana Zainal (), Azlan Mohd Zain (), Nor Haizan Mohamed Radzi () and Muhamad Razib Othman ()
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Nurezayana Zainal: Universiti Teknologi Malaysia (UTM)
Azlan Mohd Zain: Universiti Teknologi Malaysia (UTM)
Nor Haizan Mohamed Radzi: Universiti Teknologi Malaysia (UTM)
Muhamad Razib Othman: Universiti Teknologi Malaysia (UTM)

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 4, No 7, 797-804

Abstract: 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.

Keywords: Machining; Optimization; Surface roughness; Glowworm swarm optimization; Taguchi method (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10845-014-0914-7

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