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An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm

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Abstract

This study combined the artificial neural network (ANN) with a genetic algorithm (GA) to establish an inverse model of injection molding for optical lens form accuracy. The Taguchi parameter design was used for screening experiments of the injection molding parameters, and the significant factors influencing lens form accuracy were found to be mold temperature, cooling time, packing pressure, and packing time. These significant factors were used for full factorial experiments, and the experimental data then were used as training and checking data sets for the ANN prediction model. Finally, the ANN prediction model was combined with the GA to construct an inverse model of injection molding. Lens form accuracies of 0.5, 0.7, and \(1\,\upmu \hbox {m}\) were taken as examples for validation, and when the error of the set lens form accuracy target value was within 2 % there were 26, 17, and six sets of the injection molding parameters, respectively, that met the desired form accuracy obtained by using the inverse model. The result indicated that the proposed strategy was successful in identifying process parameters for products with reliable accuracy. In addition, using the GA as a global search algorithm for the optimal solution could further optimize the Taguchi optimal process parameters. The validation experiments revealed that the form accuracy of the lens was improved.

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References

  • Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 31, 599–604.

    Article  Google Scholar 

  • Ashhab, M. S., Breitsprecher, T., & Wartzack, S. (2014). Neural network based modeling and optimization of deep drawing: Extrusion combined process. Journal of Intelligent Manufacturing, 25, 77–84.

    Article  Google Scholar 

  • Chaulia, P. K., & Das, R. (2008). Process parameter optimization for fly ash brick by Taguchi method. Materials Research, 11, 159–164.

    Article  Google Scholar 

  • Che, Z. H. (2010). PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Computers & Industrial Engineering, 58, 625–637.

    Article  Google Scholar 

  • Chen, C. P., Chuang, M. T., Hsiao, Y. H., Yang, Y. K., & Tsai, C. H. (2009a). Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiment analysis. Expert Systems with Applications, 36, 10752–10759.

    Article  Google Scholar 

  • Chen, W. C., Fu, G. L., Tai, P. H., & Deng, W. J. (2009b). Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Systems with Applications, 36, 1114–1122.

    Article  Google Scholar 

  • Chen, Z., & Turng, L. S. (2005). A review of current development in process and quality control for injection molding. Advances in Polymer Technology, 24, 165–182.

    Article  Google Scholar 

  • He, W., Zhang, Y. F., Lee, K. S., Fuh, J. Y. H., & Nee, A. Y. C. (1998). Automated process parameter resetting for injection moulding: A fuzzy-neuro approach. Journal of Intelligent Manufacturing, 9, 17–27.

    Article  Google Scholar 

  • Huang, C. N., & Chang, C. C. (2011). Optimal-parameter determination by inverse model based on MANFIS: The case of injection molding for PBGA. IEEE Transactions on Control Systems Technology, 19, 1596–1603.

    Article  Google Scholar 

  • Kamoun, A., Jaziri, M., & Chaabouni, M. (2009). The use of the simplex method and its derivatives to the on-line optimization of the parameters of an injection moulding process. Chemometrics and Intelligent Laboratory Systems, 96, 117–122.

    Article  Google Scholar 

  • Katherasan, D., Elias, J. V., Sathiya, P., & Noorul, Haq A. (2014). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing, 25, 67–76.

    Article  Google Scholar 

  • Kurtaran, H., Ozcelik, B., & Erzurumlu, T. (2005). Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm. Journal of Material Processing Technology, 169, 314–319.

    Article  Google Scholar 

  • Kwak, T. S., Suzuki, T., Bae, W. B., Uehara, Y., & Ohmori, H. (2005). Application of neural network and computer simulation to improve surface profile of injection molding optic lens. Journal of Material Processing Technology, 170, 24–31.

    Article  Google Scholar 

  • Lau, H. C. W., Lee, C. K. M., Ip, W. H., Chan, F. T. S., & Leung, R. W. K. (2005). Design and implementation of a process optimizer: A case study on monitoring molding operations. Expert System, 22, 12–21.

    Article  Google Scholar 

  • Li, D., Zhou, H., Zhao, P., & Li, Y. (2009). A real-time process optimization system for injection molding. Polymer Engineering & Science, 49, 2031–2040.

    Article  Google Scholar 

  • Loera, V. G., Castro, J. M., Diaz, J. M., Mondrago’n, O. L. C., & Cabrera-R’ıos, M. (2008). Setting the processing parameters in injection molding through multiple-criteria optimization: A case study. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 38, 710–715.

    Article  Google Scholar 

  • Mok, S. L., Kwong, C. K., & Lau, W. S. (1999). Review of research in the determination of process parameters for plastic injection molding. Advances in Polymer Technology, 18, 225–236.

    Article  Google Scholar 

  • Mok, S. L., Kwong, C. K., & Lau, W. S. (2000). An intelligent hybrid system for initial process parameter setting of injection moulding. International Journal of Production Research, 38, 4565– 4576.

    Article  Google Scholar 

  • Mok, S. L., Kwong, C. K., & Lau, W. S. (2001). A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding. International Journal of Advanced Manufacturing Technology, 18, 404–409.

    Article  Google Scholar 

  • Mok, S. L., & Kwong, C. K. (2002). Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding. Journal of Intelligent Manufacturing, 13, 165–176.

    Article  Google Scholar 

  • Montgomery, D. C. (2001). Design and analysis of experiments (3rd ed.). New York: Wiley.

    Google Scholar 

  • Ozcelik, B., & Erzurumlu, T. (2005). Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm. International Communications in Heat and Mass Transfer, 32, 1085–1094.

    Article  Google Scholar 

  • Ozcelik, B., & Erzurumlu, T. (2006). Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of Material Processing Technology, 171, 437–445.

    Article  Google Scholar 

  • Ozcelik, B. (2011). Optimization of injection parameters for mechanical properties of specimens with weld line of polypropylene using Taguchi method. International Communications in Heat and Mass Transfer, 38, 1067–1072.

    Google Scholar 

  • Ross, P. J. (1996). Taguchi techniques for quality engineering. New York: McGraw-Hill.

    Google Scholar 

  • Sadeghi, B. H. M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Material Processing Technology, 103, 411–416.

    Article  Google Scholar 

  • Shen, C., Wang, L., & Li, Q. (2007). Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. Journal of Material Processing Technology, 183, 412–418.

    Article  Google Scholar 

  • Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. Hoboken: Wiley.

    Google Scholar 

  • Tang, C., Rohani, J. M., & Yajid, M. A. M. (2013). Characterization of green corrosion inhibitor using Taguchi dynamic approach. International Journal of Electrochemical Science, 8, 7991–8004.

  • Yen, C., Lin, J. C., Li, W., & Huang, M. F. (2006). An abductive neural network approach to the design of runner dimensions for the minimization of warpage in injection mouldings. Journal of Material Processing Technology, 174, 22–28.

  • Yin, F., Mao, H., & Hua, L. (2011). A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Materials & Design, 32, 3457–3464.

    Article  Google Scholar 

  • Zhu, J., & Chen, J. C. (2006). Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations. International Journal of Advanced Manufacturing Technology, 29, 308–316.

    Article  Google Scholar 

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Acknowledgments

The authors are obliged to thank the National Science Foundation, Taiwan, ROC, for the Project fund (NSC100-2221-E-167-011).

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, Republic of China

    Kuo-Ming Tsai & Hao-Jhih Luo

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  1. Kuo-Ming Tsai
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  2. Hao-Jhih Luo
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Correspondence to Kuo-Ming Tsai.

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Tsai, KM., Luo, HJ. An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. J Intell Manuf 28, 473–487 (2017). https://doi.org/10.1007/s10845-014-0999-z

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  • DOI: https://doi.org/10.1007/s10845-014-0999-z

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