Advertisement

A Soft Computing System to Perform Face Milling Operations

  • Raquel Redondo
  • Pedro Santos
  • Andres Bustillo
  • Javier Sedano
  • José Ramón Villar
  • Maritza Correa
  • José Ramón Alique
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough. The second phase is focus on identifying a model for the face milling process based on low-order models such as Black Box ones. The whole 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 such industrial task for the case of steel tools.

Keywords

Spindle Speed High Speed Machine Deep Cavity Face Milling Final Prediction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altintas, Y.: Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design. Cambridge University Press, Cambridge (2000)Google Scholar
  2. 2.
    Diaconis, P., Freedman, D.: Asymptotics of Graphical Projections. The Annals of Statistics 12(3), 793–815 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    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
  4. 4.
    Friedman, J.H., Tukey, J.W.: Projection Pursuit Algorithm for Exploratory Data-Analysis. IEEE Transactions on Computers 23(9), 881–890 (1974)CrossRefzbMATHGoogle Scholar
  5. 5.
    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
  6. 6.
    Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. In: Advances in Neural Information Processing Systems, vol. 10, pp. 350–356 (1998)Google Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Ljung, L.: System Identification, Theory for the User. Prentice-Hall, Englewood Cliffs (1999)zbMATHGoogle Scholar
  10. 10.
    Nögaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)CrossRefGoogle Scholar
  11. 11.
    Söderström, T., Stoica, P.: System identification. Prentice-Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  12. 12.
    Nelles, O.: Nonlinear System Identification, From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  13. 13.
    Haber, R., Keviczky, L.: Nonlinear System Identification, Input-Output Modelling Approach. In: Part. 2: Nonlinear System structure Identification. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  14. 14.
    Haber, R., Keviczky, L.: Nonlinear System Identification, Input-Output Modelling Approach. In: Part 1: Nonlinear System Parameter Estimation. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  15. 15.
    Stoica, P., Söderström, T.: A useful parametrization for optimal experimental design. IEEE Trans. Automatic. Control AC-27 (1982)Google Scholar
  16. 16.
    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
  17. 17.
    Akaike, H.: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 20, 425–439 (1969)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Behrens, A., Westhoff, B.: Fundamental aspects of investigating the HSC-Chip formation process by FEM. Scientific Fundamentals of HSC. Carl Hanser Verlag (2001)Google Scholar
  19. 19.
    Correa, M., Bielza, C., de Ramirez, M.J., Alique, J.R.: A Bayesian network model for surface roughness prediction in the machining process. International Journal of Systems Science 39(12), 1181–1192 (2008)CrossRefzbMATHGoogle Scholar
  20. 20.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)zbMATHGoogle Scholar
  21. 21.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Raquel Redondo
    • 1
  • Pedro Santos
    • 1
  • Andres Bustillo
    • 1
  • Javier Sedano
    • 2
  • José Ramón Villar
    • 3
  • Maritza Correa
    • 4
  • José Ramón Alique
    • 4
  • 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 OviedoOviedoSpain
  4. 4.Department of Industrial InformaticInstituto de Automática Industrial, Spanish National Research CouncilMadridSpain

Personalised recommendations