RT info:eu-repo/semantics/bookPart
T1 A Soft Computing System for Modelling the Manufacture of Steel Components
A1 Bustillo Iglesias, Andrés
A1 Sedano, Javier
A1 Curiel Herrera, Leticia Elena
A1 Villar, José Ramón
A1 Corchado, Emilio
K1 Test Piece
K1 Angle Error
K1 Wall Angle
K1 Steel Component
K1 Soft Computing Model
K1 Informática
K1 Computer science
AB In this paper we present a soft computing system developed to optimize the laser milling manufacture of high value steel components, a relatively new and interesting industrial technique. This multidisciplinary study is based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a laser milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures steel components like high value molds and dies. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough based on the existence of internal patterns. The second phase is focus on identifying a model for the laser-milling process based on low-order models such as Black Box ones. The whole 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 such industrial task for the case of steel components.
PB Springer
SN 978-3-540-93905-4
SN 1867-5662
YR 2009
FD 2009
LK http://hdl.handle.net/10259/7411
UL http://hdl.handle.net/10259/7411
LA eng
NO This research has been partially supported through project BU006A08 of JCyL and through project CIT-020000-2008-2 of Spanish Ministry of Education and Innovation.
DS Repositorio Institucional de la Universidad de Burgos
RD 02-may-2024