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Glowworm swarm optimization (GSO) for optimization of machining parameters

Author

Listed:
  • 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))

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.

Suggested Citation

  • Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:4:d:10.1007_s10845-014-0914-7
    DOI: 10.1007/s10845-014-0914-7
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    References listed on IDEAS

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    1. Tiwari, M.K. & Raghavendra, N. & Agrawal, Shubham & Goyal, S.K., 2010. "A Hybrid Taguchi-Immune approach to optimize an integrated supply chain design problem with multiple shipping," European Journal of Operational Research, Elsevier, vol. 203(1), pages 95-106, May.
    2. Unknown, 2004. "End Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 19(4), pages 1-1.
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    Citations

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    Cited by:

    1. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    4. N. A. Fountas & R. Benhadj-Djilali & C. I. Stergiou & N. M. Vaxevanidis, 2019. "An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1581-1599, April.
    5. Neeraj Kumar Bhoi & Harpreet Singh & Saurabh Pratap & Pramod K. Jain, 2022. "Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 350-363, March.
    6. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    7. Yongmao Xiao & Wei Yan & Ruping Wang & Zhigang Jiang & Ying Liu, 2021. "Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    8. Maciej Grzenda & Andres Bustillo, 2019. "Semi-supervised roughness prediction with partly unlabeled vibration data streams," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 933-945, February.

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