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



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.


  1. Baskar, N., Asokan, P., & Prabhaharan, G. (2005). Optimisation of machining parameters for milling operations using non-conventional methods. The International Journal of Advanced Manufacturing Technology, 25(11), 1078–1088.CrossRefGoogle Scholar
  2. 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
  3. Çiçek, A., Kıvak, T., & Ekici, E. (2015). Optimisation of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing, 26(2), 295–305.CrossRefGoogle Scholar
  4. Dorigo, M., & Stutzle, T. (2004). Ant colony optimisation. Massachusetts: The MIT Press Cambridge.Google Scholar
  5. Edwards, D. J., & Fuerte, J. N. (2011). Compromise ascent directions for multiple-response applications. Quality and Reliability Engineering International, 27(8), 1107–1118.CrossRefGoogle Scholar
  6. Harrington, E, Jr. (1965). The desirability function. Industrial Quality Control, 21, 494–498.Google Scholar
  7. 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
  8. 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
  9. 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
  10. Kumagai, M., Uchiyama, N., Ohmura, E., Sugiura, R., Atsumi, K., & Fukumitsu, K. (2007). Advanced dicing technology for semiconductor wafer-stealth dicing. IEEE Transactions on Semiconductor Manufacturing, 20(3), 259–265.CrossRefGoogle Scholar
  11. Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimisation: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194, 3902–3933.CrossRefGoogle Scholar
  12. Lin, W. T., Tsao, L. C., Shie, A. J., Chang, S. T., & Yang, T. C. (2016). The application of Taguchi methods to parameters optimisation for preventing coagulation in artificial kidneys. Journal of Industrial and Production Engineering, 33(4), 247–252.CrossRefGoogle Scholar
  13. Montgomery, D. C. (2012). Design and analysis of experiments. New York: Wiley.Google Scholar
  14. Mukherjee, R., Chakraborty, S., & Samanta, S. (2012). Selection of wire electrical discharge machining process parameters using non-traditional optimisation algorithms. Applied Soft Computing, 12, 2506–2516.CrossRefGoogle Scholar
  15. 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
  16. Saravanan, R., Sankar, R. S., Asokan, P., Vijayakumar, K., & Prabhaharan, G. (2005). Optimisation of cutting conditions during continuous finished profile machining using non-traditional techniques. The International Journal of Advanced Manufacturing Technology, 26(1), 30–40.CrossRefGoogle Scholar
  17. Sibalija, T. V., & Majstorovic, V. D. (2012). An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing, 23(5), 1511–1528.CrossRefGoogle Scholar
  18. Taguchi, G., Yokoyama, Y., & WU, Y. (1993). Taguchi methods—Design of experiments. Dearborn, Michigan: ASI Press.Google Scholar
  19. Zhang, B., Chen, D., & Zhao, W. (2005). Iterative ant-colony algorithm and its application to dynamic optimisation of chemical process. Computers & Chemical Engineering, 29(10), 2078–2086.CrossRefGoogle Scholar
  20. Zhang, J., Sun, H., Pan, C., Fan, Y., & Hou, H. (2016). Optimisation of process parameters for directly converting raw corn stalk to biohydrogen by clostridium sp. FZ11 without substrate pretreatment. Energy and Fuels, 30(1), 311–317.CrossRefGoogle Scholar
  21. Zirehpour, A., Rahimpour, A., Jahanshahi, M., & Peyravi, M. (2014). Mixed matrix membrane application for olive oil wastewater treatment: Process optimisation based on Taguchi design method. Journal of Environmental Management, 132, 113–120.CrossRefGoogle Scholar

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

Personalised recommendations