2024-03-28T23:58:35Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1350162022-02-07T15:36:12Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811
Bustillo Iglesias, Ándres
Sedano Franco, Javier
Villar Flecha, José R.
Curiel, Leticia
Corchado Rodríguez, Emilio Santiago
2017-09-06T09:15:45Z
2017-09-06T09:15:45Z
2008
Intelligent Data Engineering and Automated Learning – IDEAL 2008 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5326, pp. 498-507.
978-3-540-88905-2 (Print) / 978-3-540-88906-9 (Online)
0302-9743 (Print) / 1611-3349 (Online)
http://hdl.handle.net/10366/135016
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.
application/pdf
en
Springer Science + Business Media
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Computer Science
AI for Modelling the Laser Milling of Copper Components
info:eu-repo/semantics/article