Abstract:To reduce the influence of thermal error on the machining accuracy of the electric spindle, it is necessary to establish a thermal error compensation system for the electric spindle. Its performance mainly depends on the accuracy of the thermal errorprediction model and the temperaturequality of themodel input. To ensurethe temperature qualityofthe inputmodel,a comprehensivealgorithm that fusesfuzzyCmeansclusteringand graycorrelation analysis isusedtooptimizethe temperaturemeasurement points. The numberoftemperature measurement points is reduced from 10 to 3. The main spindle of the electric spindle is the test object. The temperature variable of the electric spindle speed of 7 000 r/min is used as the input, and the thermal error variable is the output. The adaptive neural fuzzy inference system is used to establish the thermal error prediction model of the electric spindle. The experimental data of 5 000 and 9 000 r/min are used as evaluation. Experimental results show that the formulated ANFIS thermal error prediction model can effectively predict the thermal error of the electric spindle. The residual error of the prediction model is less than 1 μm. Finally, compared with the back propagation neural network, results show that the prediction model has higher accuracy and antiinterference ability.