2026-04-19T09:29:40Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/93662024-07-12T00:05:26Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109

Bustillo Iglesias, Andrés Sedano, Javier Ramón Villar, José Curiel Herrera, Leticia Elena Corchado, Emilio 2008 Trabajo presentado en: Intelligent Data Engineering and Automated Learning – IDEAL 2008, realizado del 2 al 5 de noviembre de 2008, en Daejeon (Corea del Sur) 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 http://hdl.handle.net/10259/9366 eng Springer Nature AI for Modelling the Laser Milling of Copper Components info:eu-repo/semantics/conferenceObject TEXT RIUBU. Repositorio Institucional de la Universidad de Burgos Hispana