Abstract
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.
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Krömer, P. et al. (2013). Evaluation of Novel Soft Computing Methods for the Prediction of the Dental Milling Time-Error Parameter. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_17
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DOI: https://doi.org/10.1007/978-3-642-32922-7_17
Publisher Name: Springer, Berlin, Heidelberg
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