AI for Modelling the Laser Milling of Copper Components

Authors:
Andrés Bustillo;Javier Sedano;José Ramón Villar;Leticia Curiel;Emilio Corchado
Affiliations:
Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Electromechanical Engineering, University of Burgos, Burgos, Spain;Department of Computer Science, University of Oviedo, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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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.