Abstract
Thermal sensor selection is a work of great importance when modeling thermal error. The proper selection of thermal sensors and their locations may greatly improve the prediction accuracy. In this article, the fuzzy C means (FCM) clustering method and the ISODATA method are used to group the data of thermal sensors and a genetic algorithm–back propagation artificial neural network thermal model is established to testify the accuracy. A validity criterion for the FCM method is put forward to guarantee the precision of the model. Both the FCM and the ISODATA methods are effective for thermal sensor selection.
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Wang, H., Wang, L., Li, T. et al. Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method. Int J Adv Manuf Technol 69, 121–126 (2013). https://doi.org/10.1007/s00170-013-4998-6
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DOI: https://doi.org/10.1007/s00170-013-4998-6