AI for Modelling the Laser Milling of Copper Components

  • Andrés Bustillo
  • Javier Sedano
  • José Ramón Villar
  • Leticia Curiel
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


Laser milling is a relatively new micromanufacturing technique in the production of copper and other metallic components. This study presents multidisciplinary research, which is based on unsupervised connectionist architectures in conjunction with modelling systems, on the determination of the optimal operating conditions in this industrial process. Sensors on a laser milling centre relay the data used in this industrial case study of a machine-tool that manufactures copper components for high value micro-coolers. The two-phase application of the connectionist architectures is capable of identifying a model for the laser-milling process based on low-order models such as Black Box. The final system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control these industrial tasks.


Test Piece Angle Error Industrial Case Study Depth Error Final Prediction Error 
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  1. 1.
    Diaconis, P., Freedman, D.: Asymptotics of Graphical Projections. The Annals of Statistics 12(3), 793–815 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Corchado, E., Fyfe, C.: Connectionist Techniques for the Identification and Suppression of Interfering Underlying Factors. Int. Journal of Pattern Recognition and Artificial Intelligence 17(8), 1447–1466 (2003)CrossRefGoogle Scholar
  3. 3.
    Friedman, J.H., Tukey, J.W.: Projection Pursuit Algorithm for Exploratory Data-Analysis. IEEE Transactions on Computers 23(9), 881–890 (1974)CrossRefzbMATHGoogle Scholar
  4. 4.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8(3), 203–225 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information Processing Systems 10, 350–356 (1998)Google Scholar
  6. 6.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: Proc. of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148 (2002)Google Scholar
  7. 7.
    Corchado, E., Han, Y., Fyfe, C.: Structuring Global Responses of Local Filters Using Lateral Connections. Journal of Experimental & Theoretical Artificial Intelligence 15(4), 473–487 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Ljung, L.: System Identification, Theory for the User. Prentice-Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  9. 9.
    Nögaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)CrossRefGoogle Scholar
  10. 10.
    Söderström, T., Stoica, P.: System identification. Prentice-Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  11. 11.
    Nelles, O.: Nonlinear System Identification, From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  12. 12.
    Haber, R., Keviczky, L.: Nonlinear System Identification, Input-Output Modeling Approach, Part. 2: Nonlinear System structure Identification. Kluwer Academic Publishers, Dordrecht (1999)CrossRefzbMATHGoogle Scholar
  13. 13.
    Haber, R., Keviczky, L.: Nonlinear System Identification, Input-Output Modeling Approach, Part 1: Nonlinear System Parameter Estimation. Kluwer Academic Publishers, Dordrecht (1999)CrossRefzbMATHGoogle Scholar
  14. 14.
    Stoica, P., Söderström, T.: A useful parametrization for optimal experimental design. In: IEEE Trans. Automatic. Control, vol. AC-27 (1982)Google Scholar
  15. 15.
    He, X., Asada, H.: A new method for identifying orders of input-output models for nonlinear dynamic systems. In: Proc. Of the American Control Conf., S.F., California, pp. 2520–2523 (1993)Google Scholar
  16. 16.
    Akaike, H.: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 20, 425–439 (1969)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Arias, G., Ciurana, J., Planta, X., Crehuet, A.: Analyzing Process Parameters that influence laser machining of hardened steel using Taguchi method. In: Proceedings of 52nd International Technical Conference SAMPE 2007, Baltimore (2007); ISBN 978-0-938994-72-5Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrés Bustillo
    • 1
  • Javier Sedano
    • 2
  • José Ramón Villar
    • 3
  • Leticia Curiel
    • 1
  • Emilio Corchado
    • 1
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of Electromechanical EngineeringUniversity of BurgosBurgosSpain
  3. 3.Department of Computer ScienceUniversity of OviedoSpain

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