Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter.

This multidisciplinary study presents the application of two well known soft computing methods flexible neural trees, and evolutionary fuzzy rules for the prediction of the error parameter 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.