Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 441–457 | Cite as

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

  • P. LuangpaiboonEmail author
  • S. Boonhao
  • R. Montemanni


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.


Taguchi design Multiple linear regression Desirability function Steepest ant sense Stealth laser dicing Twin grease filling system 



This work was supported by the National Research University Project of Thailand Office of Higher Education Commission. The first author wishes to thank the Faculty of Engineering, Thammasat University, THAILAND.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Industrial Statistics and Operational Research Unit (ISO-RU), Department of Industrial Engineering, Faculty of EngineeringThammasat UniversityPathumthaniThailand
  2. 2.Dalle Molle Institute for Artificial Intelligence (IDSIA)University of Applied Sciences of Southern Switzerland (SUPSI)MannoSwitzerland

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