2026-04-17T20:25:52Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/114852026-03-21T01:05:42Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109

00925njm 22002777a 4500 dc Redondo Guevara, Raquel author Santos González, Pedro author Bustillo Iglesias, Andrés author Sedano, Javier author Villar, José R. author Correa, Maritza author Alique, José Ramón author Corchado, Emilio author 2009 In this paper we present a soft computing system developed to optimize the face milling operation under High Speed conditions in the manufacture of steel components like molds with deep cavities. This applied research presents a multidisciplinary study 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 milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures industrial tools. 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. The second phase is focus on identifying a model for the face 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 tools. 978-3-642-02480-1 978-3-642-02481-8 https://hdl.handle.net/10259/11485 10.1007/978-3-642-02481-8_190 A Soft Computing System to Perform Face Milling Operations