Steepest ant sense algorithm for parameter optimisation of multi-response processes based on taguchi design
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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.
KeywordsTaguchi 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.
- Chen, H., Zhang, J., Dang, Y., & Shu, G. (2014). Optimisation for immobilisation of \(\beta \)-galactosidase using plackettburman design and steepest ascent method. Journal of Chemical and Pharmaceutical Research, 6(4), 612–616.Google Scholar
- Harrington, E, Jr. (1965). The desirability function. Industrial Quality Control, 21, 494–498.Google Scholar
- Hron, J., & Macak, T. (2013). Optimisation of food packaging to improve food safety. Journal of Food, Agriculture and Environment, 11(3–4), 423–428.Google Scholar
- Joyce, A. P., & Leung, S. S. (2013). Use of response surface methods and path of steepest ascent to optimise ligand-binding assay sensitivity. Journal of Immunological Methods, 392, 12–23.Google Scholar
- Kuo, C. F. J., Lan, W. L., Chang, Y. C., & Lin, K. W. (2016). The preparation of organic light-emitting diode encapsulation barrier layer by low-temperature plasma-enhanced chemical vapor deposition: a study on the SiO x N y film parameter optimisation. Journal of Intelligent Manufacturing, 27(3), 581–593.CrossRefGoogle Scholar
- Montgomery, D. C. (2012). Design and analysis of experiments. New York: Wiley.Google Scholar
- Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimisation using designed experiments. New York: Wiley.Google Scholar
- Taguchi, G., Yokoyama, Y., & WU, Y. (1993). Taguchi methods—Design of experiments. Dearborn, Michigan: ASI Press.Google Scholar