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Steepest ant sense algorithm for parameter optimisation of multi-response processes based on taguchi design

Author

Listed:
  • P. Luangpaiboon

    (Thammasat University)

  • S. Boonhao

    (Thammasat University)

  • R. Montemanni

    (University of Applied Sciences of Southern Switzerland (SUPSI))

Abstract

Due to the continuous refinements in engineering operations, process parameters need to be optimised in order to improve the production quality. In this study we present a novel method based on the hybridisation of an ant colony system search mechanism with a steepest ascent method to achieve such a parameter optimisation. The proposed algorithm has been implemented and run on two real time industrial applications. Experimental results showed that the optimised parameters for a stealth laser dicing process provided by the new method were able to increase the production quality by improving production precision, which is measured in terms of average deviation from the expected result and relative variance. The novel method we propose was able to identify improved settings for a stealth laser dicing process with five parameters, resulting in a greatly reduced rate of product failures. Additionally six parameters were optimised for another industrial application, namely a grease filling system with twin towers, using only 23 experiments, leading to an increase in the tool life (objective of the optimisation) from the previous average of 9236 U produced to 13,883 U. The new method performed better than conventional response surface methods, showing therefore to be promising for other similar industrial applications.

Suggested Citation

  • P. Luangpaiboon & S. Boonhao & R. Montemanni, 2019. "Steepest ant sense algorithm for parameter optimisation of multi-response processes based on taguchi design," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 441-457, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1257-3
    DOI: 10.1007/s10845-016-1257-3
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    References listed on IDEAS

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    1. Chung-Feng Jeffrey Kuo & Wei-Lun Lan & Yu-Cheng Chang & Kun-Wei Lin, 2016. "The preparation of organic light-emitting diode encapsulation barrier layer by low-temperature plasma-enhanced chemical vapor deposition: a study on the $$\hbox {SiO}_\mathrm{x}\hbox {N}_\mathrm{y}$$ ," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 581-593, June.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
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