Modification of a neural network utilizing hybrid filters for the compensation of thermal deformation in machine tools

This study proposes a modified method that combines feed-forward neural network (FNN) and hybrid filters to improve the accuracy and reduce computation times for the prediction of thermal deformation in a machine tool. The hybrid filter consists of the linear regression (LR), moving average (MA) and autoregression (AR). Their outputs serve as input of FNN, which are estimated by the static and dynamic relationships between the temperature distributions and thermal deformations. This modified method enables the propagation accuracy between input and output layers of a static FNN to be improved and the learning time to be reduced. Furthermore, the modified method is compared with other three ones, which are traditional ARMA, FNN, and FNN combined with LR by numerical analysis and practical experiments. In analysis, the error margins of various approaches are compared using a finite element model that is determined for the relationships between thermal deformation and temperature distribution. Also, practical experiments of these approaches for a grinding machine are realized to compare the deformation predications according to temperature measurements.