2026-04-17T20:38:53Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/93642024-07-12T00:05:24Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109

00925njm 22002777a 4500 dc Bustillo Iglesias, Andrés author Sedano, Javier author Ramón Villar, José author Curiel Herrera, Leticia Elena author Corchado, Emilio author 2008-09-16 This study presents a research that identifies and applies unsupervised connectionist models in conjunction with modelling systems, in order todetermine optimal conditions to perform laser milling of metallic components. This industrial problem is defined by a data set relayed through sensors situated on a laser milling centre that is a machine-tool used to manufacture high value micro-molds and micro-dies. The results of the study and the application of the connectionist architectures allow the identification, in a second phase, of a model for the milling machine process based on low-order models such as Black Box, which are capable of approximating the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples. 978-0-7695-3325-4 http://hdl.handle.net/10259/9364 10.1109/EMS.2008.106 Conventional Methods and AI models for Solving an Industrial an Industrial Problem