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Glowworm swarm optimization (GSO) for optimization of machining parameters

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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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Acknowledgments

Special appreciation to reviewer(s) for useful advices and comments. The authors greatly acknowledge Soft Computing Research Group (SCRG UTM), Research Management Centre (RMC UTM) and Ministry of Higher Education Malaysia (MOHE) for financial support through the Exploratory Research Grant Scheme (ERGS) No. R.J130000.7828.4L087

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia (UTM), 81310 , Skudai, Johor, Malaysia

    Nurezayana Zainal, Azlan Mohd Zain, Nor Haizan Mohamed Radzi & Muhamad Razib Othman

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  1. Nurezayana Zainal
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  2. Azlan Mohd Zain
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  3. Nor Haizan Mohamed Radzi
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  4. Muhamad Razib Othman
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Correspondence to Azlan Mohd Zain.

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Zainal, N., Zain, A.M., Radzi, N.H.M. et al. Glowworm swarm optimization (GSO) for optimization of machining parameters. J Intell Manuf 27, 797–804 (2016). https://doi.org/10.1007/s10845-014-0914-7

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  • DOI: https://doi.org/10.1007/s10845-014-0914-7

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