A Soft Computing System to Perform Face Milling Operations

Authors:
Raquel Redondo;Pedro Santos;Andres Bustillo;Javier Sedano;José Ramón Villar;Maritza Correa;José Ramón Alique;Emilio Corchado
Affiliations:
Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain;Department of Electromechanical Engineering, University of Burgos, Burgos, Spain;Department of Computer Science, University of Oviedo, Oviedo, Spain;Department of Industrial Informatic, Instituto de Automática Industrial, Spanish National Research Council, Madrid, Spain;Department of Industrial Informatic, Instituto de Automática Industrial, Spanish National Research Council, Madrid, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

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Abstract

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.