2026-04-17T20:35:20Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/93642024-07-12T00:05:24Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109
Repositorio Institucional de la Universidad de Burgos
author
Bustillo Iglesias, Andrés
author
Sedano, Javier
author
Ramón Villar, José
author
Curiel Herrera, Leticia Elena
author
Corchado, Emilio
2024-07-11T09:34:03Z
2024-07-11T09:34:03Z
2008-09-16
978-0-7695-3325-4
http://hdl.handle.net/10259/9364
10.1109/EMS.2008.106
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.
eng
Conventional Methods and AI models for Solving an Industrial an Industrial Problem
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/9364/1/Bustillo-Conventional_Methods_AI_Solving_2008.pdf
File
MD5
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application/pdf
Bustillo-Conventional_Methods_AI_Solving_2008.pdf