Skip to main content
Erschienen in: Journal of Intelligent Manufacturing 2/2017

15.11.2014

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

verfasst von: Kuo-Ming Tsai, Hao-Jhih Luo

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 31, 599–604.CrossRef Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 31, 599–604.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Chaulia, P. K., & Das, R. (2008). Process parameter optimization for fly ash brick by Taguchi method. Materials Research, 11, 159–164.CrossRef Chaulia, P. K., & Das, R. (2008). Process parameter optimization for fly ash brick by Taguchi method. Materials Research, 11, 159–164.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Li, D., Zhou, H., Zhao, P., & Li, Y. (2009). A real-time process optimization system for injection molding. Polymer Engineering & Science, 49, 2031–2040.CrossRef Li, D., Zhou, H., Zhao, P., & Li, Y. (2009). A real-time process optimization system for injection molding. Polymer Engineering & Science, 49, 2031–2040.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Montgomery, D. C. (2001). Design and analysis of experiments (3rd ed.). New York: Wiley. Montgomery, D. C. (2001). Design and analysis of experiments (3rd ed.). New York: Wiley.
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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. 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.
Zurück zum Zitat Ross, P. J. (1996). Taguchi techniques for quality engineering. New York: McGraw-Hill. Ross, P. J. (1996). Taguchi techniques for quality engineering. New York: McGraw-Hill.
Zurück zum Zitat Sadeghi, B. H. M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Material Processing Technology, 103, 411–416.CrossRef Sadeghi, B. H. M. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Material Processing Technology, 103, 411–416.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. Hoboken: Wiley. Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi’s quality engineering handbook. Hoboken: Wiley.
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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. 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.
Zurück zum Zitat 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.CrossRef 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.CrossRef
Zurück zum Zitat 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.CrossRef 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.CrossRef
Metadaten
Titel
An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm
verfasst von
Kuo-Ming Tsai
Hao-Jhih Luo
Publikationsdatum
15.11.2014
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 2/2017
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-014-0999-z

Weitere Artikel der Ausgabe 2/2017

Journal of Intelligent Manufacturing 2/2017 Zur Ausgabe

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.