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.
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Krömer, P. et al. (2012). Prediction of Dental Milling Time-Error by Flexible Neural Trees and Fuzzy Rules. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_100
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DOI: https://doi.org/10.1007/978-3-642-32639-4_100
Publisher Name: Springer, Berlin, Heidelberg
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