Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method

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.