Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error

Abstract

This paper presents a new modeling methodology for nonstationary machine tool thermal errors. The method uses the dynamic neural network model to track nonlinear time-varying machine tool errors under various thermal conditions. To accommodate the nonstationary nature of the thermo-elastic process, an Integrated Recurrent Neural Network (IRNN) is introduced to identify the nonstationarity of the thermo-elastic process with a deterministic linear trend. Experiments on spindle thermal deformation are conducted to evaluate the model performance in terms of model estimation accuracy and robustness. The comparison indicates that the IRNN performs better than other modeling methods, such as, multi-variable regression analysis (MRA), multi-layer feedforward neural network (MFN), and recurrent neural network (RNN), in terms of model robustness under a variety of working conditions.