2009 | OriginalPaper | Buchkapitel
A Soft Computing System to Perform Face Milling Operations
verfasst von : Raquel Redondo, Pedro Santos, Andres Bustillo, Javier Sedano, José Ramón Villar, Maritza Correa, José Ramón Alique, Emilio Corchado
Erschienen in: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Verlag: Springer Berlin Heidelberg
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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.