Intelligent parametric design for a multiple-quality-characteristic glue-dispensing process



For double-sided circuit boards, a wave soldering carrier is generally used to shield the devices mounted on the surface of the first side of the printed circuit board (PCB), so that the solder joints are not melted again through exposure to tin wave, causing the devices to deviate or fall as a result of flushing. However, carrier adoption increases production costs. This study proposes a glue-dispensing process to replace the wave soldering carrier. In addition, glue curing and reflow soldering were performed simultaneously to enhance production efficiency. An ecofriendly glue-dispensing process using low-cost CEM-1 substrates and a glue materials featuring a low curing temperature helps reduce energy consumption and carbon emissions. The Taguchi method was used to plan and execute this experiment. The quality characteristics of assembly reliability and manufacturing costs were considered in terms of glue thrust strength and per-PCB manufacturing cost, respectively. An intelligent parametric design applying PCA statistical methods and artificial neural networks (ANN) model was proposed. Results of a confirmation test indicated that the optimal parameter combination suggested by the ANN model was superior. The most satisfactory procedure parameter combination obtained comprised GMIR-130HF for the glue material, a curing temperature of \(140\,^{\circ }\hbox {C}\), a 1.1 m/min conveyor velocity, and a 0.09 Mpa dispensing pressure.


Surface-mount technology Taguchi method Principal component analysis Artificial neural network Genetic algorithm 



Funding was provided by Ministry of Science and Technology ROC (Grant No. MOST 103-2221-E-027 -112).


  1. Abdul Aziz, M. S., Abdullah, M. Z., & Khor, C. Y. (2015). Thermal fluid–structure interaction of PCB configurations during the wave soldering process. Soldering and Surface Mount Technology, 27(1), 31–44.CrossRefGoogle Scholar
  2. Al-Refaie, A., Al-Alaween, W., Diabat, A., & Li, M. H. (2017). Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis. Journal of Intelligent Manufacturing, 28(2), 387–403.CrossRefGoogle Scholar
  3. Balkova, R., Holcnerova, S., & Cech, V. (2002). Testing of adhesives for bonding of polymer composites. International Journal of Adhesion & Adhesives, 22, 291–295.CrossRefGoogle Scholar
  4. Biswasa, S., & Satapathya, A. (2010). A study on tribological behavior of alumina-filled glass–epoxy composites using Taguchi experimental design. Tribology Transactions, 53(4), 520–532.CrossRefGoogle Scholar
  5. Brodsky, W. L., Parker, F. D., & Shoenthaler, D. (1980). Development of a 68-Pin multiple in-line package. IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 3(4), 594–601.CrossRefGoogle Scholar
  6. Chan, K. Y., Kwong, C. K., & Tsim, Y. C. (2010). Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms. Engineering Applications of Artificial Intelligence, 23(1), 18–26.CrossRefGoogle Scholar
  7. Conseil, H., Jellesen, M. S., & Ambat, R. (2014). Contamination profile on typical printed circuit board assemblies vs soldering process. Soldering and Surface Mount Technology, 26(4), 194–202.CrossRefGoogle Scholar
  8. Cook, D. F., Ragsdale, C. T., & Major, R. L. (2000). Combining a neural network with a genetic algorithm for process parameter optimization. Engineering Application of Artificial Intelligence, 13(4), 391–396.CrossRefGoogle Scholar
  9. Deng, G., Cui, H., Peng, Q., & Zhong, J. (2005). Experiment study influences of some process parameters on dispensing dots consistency in contact dispensing process. In High density microsystem design and packaging and component failure analysis conference, Shanghai, pp. 1–7.Google Scholar
  10. Derringer, G. C., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Artificial Intelligence, 12(4), 214–219.Google Scholar
  11. Gordon, T. L., & Fakley, M. E. (2003). The Influence of elastic modulus on adhesion to thermoplastics and thermoset materials. International Journal of Adhesion & Adhesives, 23(2), 95–100.CrossRefGoogle Scholar
  12. Hasan, K., Babur, O., & Tuncay, E. (2005). Warpage optimization of a busceiling lamp base using neural network model and genetic algorithm. Journal of Materials Processing Technology, 169(2), 314–319.CrossRefGoogle Scholar
  13. Huang, C. Y. (2015). Innovative parametric design for environmentally conscious adhesive dispensing process. Journal of Intelligent Manufacturing, 26(1), 1–12.CrossRefGoogle Scholar
  14. Huang, C. Y., & Huang, H. H. (2014). Process optimization of SnCuNi soldering material using artificial parametric design. Journal of Intelligent Manufacturing, 25(4), 813–823.CrossRefGoogle Scholar
  15. Li, M. H., Abbas, A. R., & Tai, K. C. (2008). Optimizing SUS 304 wire drawing process by grey analysis utilizng Taguchi method. Journal of University of Science and Technology Beijing, 15(6), 714–722.CrossRefGoogle Scholar
  16. Li, X., Ye, N., Xu, X., & Sawhey, R. (2007). Influencing factors of job waiting time variance on a single machine. European Journal of Industrial Engineering, 1(1), 56–73.CrossRefGoogle Scholar
  17. Lin, H. C., Su, C. T., Wang, C. C., Chang, B. H., & Juang, R. C. (2012). Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms. Expert Systems with Applications, 39(7), 12918–12925.CrossRefGoogle Scholar
  18. Liu, S., & Mei, Y. (1995). Behavior of delaminated plastic IC packages subjected to encapsulation cooling, moisture absorption, and wave soldering. IEEE Transactions On Components, Packaging, and Manufacturing Technology, 18(3), 634–645.CrossRefGoogle Scholar
  19. Lu, D., & Antony, J. (2002). Optimization of multiple responses using a fuzzy-rule based inference system. International Journal of Production Research, 40(7), 1613–1625.CrossRefGoogle Scholar
  20. Luangpaiboon, P., Boonhao, S., & Montemanni, R. (2016). Steepest ant sense algorithm for parameter optimisation of multi-response processes based on Taguchi design. Journal of Intelligent Manufacturing.
  21. Su, C. T., & Chiang, T. L. (2002). Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach. Journal of Intelligent Manufacturing, 14(2), 229–238.Google Scholar
  22. Su, C. T., & Tong, L. I. (1997). Multi-response robust design by principal component analysis. Total Quality Management, 8(6), 409–416.CrossRefGoogle Scholar
  23. Sun, R., Tsung, F., & Qu, L. (2007). Evolving kernel principal component analysis for fault diagnosis. Computers and Industrial Engineering, 53(2), 361–371.CrossRefGoogle Scholar
  24. Thamizhmanii, S., Saparudin, S., & Hasan, S. (2007). Analyses of surface roughness by turning process using Taguchi method. Journal of Achievements in Materials and Manufacturing Engineering, 20(1–2), 503–506.Google Scholar
  25. Tong, L. I., Wang, C. H., & Chen, H. C. (2005). Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution. International Journal of Advanced Manufacturing Technology, 27(3–4), 407–414.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial Engineering and ManagementNational Taipei University of TechnologyTaipeiTaiwan, ROC

Personalised recommendations