2026-04-17T20:16:56Zhttps://riubu.ubu.es/oai/request

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

Redondo Guevara, Raquel Santos González, Pedro Bustillo Iglesias, Andrés Sedano, Javier Villar, José R. Correa, Maritza Alique, José Ramón Corchado, Emilio 2009 Comunicación presentada en: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II 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. application/pdf https://hdl.handle.net/10259/11485 eng Springer A Soft Computing System to Perform Face Milling Operations info:eu-repo/semantics/conferenceObject TEXT RIUBU. Repositorio Institucional de la Universidad de Burgos Hispana