Thermal Error Modelling of the Spindle Using Neurofuzzy Systems

Abstract

This paper proposes a new combined model to predict the spindle deformation, which combines the grey models and the ANFIS (adaptive neurofuzzy inference system) model. The grey models are used to preprocess the original data, and the ANFIS model is used to adjust the combined model. The outputs of the grey models are used as the inputs of the ANFIS model to train the model. To evaluate the performance of the combined model, an experiment is implemented. Three Pt100 thermal resistances are used to monitor the spindle temperature and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that the combined model can better predict the spindle deformation compared to BP network, and it can greatly improve the performance of the spindle.

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

“…The empirical-based modeling method aims at thermal error compensation. The prediction models are established by statistical methods, such as regression [3,4] and neural network [5,6]. The dependent variables of the models are the thermal-induced displacements.…”