Prediction of dental milling time-error by flexible neural trees and fuzzy rules

Authors:
Pavel Krömer;Tomáš Novosád;Václav Snášel;Vicente Vera;Beatriz Hernando;Laura García-Hernandez;Héctor Quintián;Emilio Corchado;Raquel Redondo;Javier Sedano;Alvaro E. García
Affiliations:
Dept. of Computer Science, VŠB-Technical University of Ostrava, Czech Republic,IT4Innovations, Ostrava, Czech Republic;Dept. of Computer Science, VŠB-Technical University of Ostrava, Czech Republic;Dept. of Computer Science, VŠB-Technical University of Ostrava, Czech Republic,IT4Innovations, Ostrava, Czech Republic;Facultad de Odontología, UCM, Madrid, Spain;Facultad de Odontología, UCM, Madrid, Spain;Area of Project Engineering, University of Cordoba, Spain;Departamento de Informática y Automática, Universidad de Salamanca, Spain;IT4Innovations, Ostrava, Czech Republic,Departamento de Informática y Automática, Universidad de Salamanca, Spain;Department of Civil Engineering, University of Burgos, Burgos, Spain;Dept. of AI & Applied Electronics, Castilla y León Technological Institute, Burgos, Spain;Facultad de Odontología, UCM, Madrid, Spain
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures --- evolutionary fuzzy rules and flexible neural trees --- for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.