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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)

Abstract

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.

Keywords

Test Piece Angle Error Industrial Case Study Depth Error 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.

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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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