Advertisement

Neural Computing and Applications

, Volume 19, Issue 5, pp 741–754 | Cite as

A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

  • M. Fernández-Delgado
  • M. Reboreda
  • E. Cernadas
  • S. Barro
Original Article
  • 218 Downloads

Abstract

In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.

Keywords

Automotive industry Plastic injection mold Support vector regression Radial basis function Multi-layer perceptron Generalized regression neural networks Cascade correlation K-nearest neighbors Generalized ART 

Notes

Acknowledgments

This work was supported by the Spanish Ministry of Education and Science (MEC) and the European Regional Development Fund of the European Commission (FEDER) under project TIN2006-15460-C04-02, and by the Xunta de Galicia under project 08MMA010402PR.

References

  1. 1.
    Plossl G (1994) Orlicky’s material requirements planning. McGraw-Hill, NYGoogle Scholar
  2. 2.
    Cheng T, Podolsky S (1996) Just-in-Time manufacturing—an introduction. Springer, NYGoogle Scholar
  3. 3.
    Troqueles y Moldes de Galicia S.A. http://www.tromosa.es
  4. 4.
  5. 5.
    Li X, Hu B, Du R (2008) Predicting the parts weight in plastic injection molding using least squares support vector regression. IEEE Trans Syst Man Cybern—Part C: Appl Rev 38(6):827–833CrossRefGoogle Scholar
  6. 6.
    Shen C, Wang L, Li Q (2007) Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J Mater Process Technol 183(2–3):412–418CrossRefGoogle Scholar
  7. 7.
    Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18(2):135–140CrossRefGoogle Scholar
  8. 8.
    Yarlagadda P, Khong C (2001) Development of an integrated neural network system for prediction of process parameters in metal injection moulding. J Mater Process Technol 118(1–3):109–115CrossRefGoogle Scholar
  9. 9.
    Kenig S, Ben-David A, Omer M, Sadeh A (2001) Control of properties in injection molding by neural networks. Eng Appl Artif Intell 14(6):819–823CrossRefGoogle Scholar
  10. 10.
    Chen WC, Tai PH, Wang MW, Deng WJ, Chen CT (2008) A neural network-based approach for dynamic quality prediction in a plastic injection molding process. Expert Syst Appl 35(3):843–849CrossRefGoogle Scholar
  11. 11.
    Huang S, Tan K, Lee T (2004) Neural-network-based predictive learning control of ram velocity in injection molding. IEEE Trans Syst Man Cybern Part C: Appl Rev 34(3):363–368CrossRefGoogle Scholar
  12. 12.
    Smola AJ, Scholkopf B (1998) A Tutorial on Support Vector Regression. NeuroCOLT2 Technical Report Series, NC2-TR-1998-030. http://www.citeseer.ist.psu.edu/smola98tutorial.html
  13. 13.
    Vapnik V (1998) Statistical learning theory. Wiley, NYzbMATHGoogle Scholar
  14. 14.
    Chen S, Cowan C, Grant P (1991) Orthogonal least squares algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309CrossRefGoogle Scholar
  15. 15.
    Chen S, Hong X, Harris C, Hanzo L (2008) Fully complex-valued radial basis function networks: orthogonal least squares regression and classification. Neurocomputing 71(16–18):3421–3433CrossRefGoogle Scholar
  16. 16.
    Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78(9):1481–1497CrossRefGoogle Scholar
  17. 17.
    Yao W, Chen X, Luo W (2009) A gradient-based sequential radial basis function neural network modeling method. Neural Comput Appl 18(5):477–484CrossRefGoogle Scholar
  18. 18.
    Haddadnia J, Faez K, Ahmadi M (2003) A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition. Pattern Recognit 36(5):1187–1202zbMATHCrossRefGoogle Scholar
  19. 19.
    Yu DL, Yu DW (2007) A new structure adaptation algorithm for RBF networks and its application. Neural Comput Appl 16(1):91–100Google Scholar
  20. 20.
    Nissen S (2003) FANN: Fast Artificial Neural Networks, v. 2.1.0. http://www.fann.sourceforge.net
  21. 21.
    Igel C, Husken M (2003) Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 50:105–123zbMATHCrossRefGoogle Scholar
  22. 22.
    Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proceedings of 1993 IEEE International Conference on Neural Networks, pp 586–591Google Scholar
  23. 23.
    Fahlman SE (1988) Faster-learning variations on back-propagation: an empirical study. In: Proceedings of the 1988 connectionist models summer school, pp 38–50. Morgan-KaufmannGoogle Scholar
  24. 24.
    Specht D (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576CrossRefGoogle Scholar
  25. 25.
    Li W, Luo T, Zhu Q, Liu J, Le J (2008) Applications of AR*-GRNN model for financial time series forecasting. Neural Comput Appl 17(5–6):441–448Google Scholar
  26. 26.
    Fahlman SE, Lebiere C (1990) The cascade-correlation learning architecture. In: Advances in neural information processing systems, vol 2. Morgan-Kaufmann, pp 524–532Google Scholar
  27. 27.
    Gao X, Wang X, Ovaska S (2009) Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network. Neurocomputing 72:2483–2490CrossRefGoogle Scholar
  28. 28.
    Duda R, Hart P, Storck D (2001) Pattern classification. Wiley, NYzbMATHGoogle Scholar
  29. 29.
    Yap K, Lim C, LZ A (2008) A hybrid ART-GRNN online learning neural network with an ɛ-insensitive loss function. IEEE Trans Neural Netw 19(9):1641–1646CrossRefGoogle Scholar
  30. 30.
    Williamson J (1996) Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw 9(5):881–897CrossRefGoogle Scholar
  31. 31.
    Chang CC, Lin CJ (2008) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
  32. 32.
    Hsu CH, Chang CC (2008) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
  33. 33.
    Sheskin D (2006) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca RatonGoogle Scholar
  34. 34.
    Garcia D, Herrera F, Research Group on soft computing and information intelligent systems. http://www.sci2s.ugr.es/sicidm/#eight

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • M. Fernández-Delgado
    • 1
  • M. Reboreda
    • 2
  • E. Cernadas
    • 1
  • S. Barro
    • 1
  1. 1.Intelligent Systems Group, Department of Electronics and Computer ScienceUniversity of Santiago de CompostelaSantiago de CompostelaSpain
  2. 2.Troqueles y Moldes de Galicia S.A. (TROMOSA)Santiago de CompostelaSpain

Personalised recommendations