Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules

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.