Identification of position of key thermal susceptible points for thermal error compensation of machine tool by neural network

It is very important to determine the thermal susceptible points accurately in thermal error compensation of a machine tool. These points would be used to arrange temperature measurement points and compensation thermal sources. In this paper, based on the study of relationship between temperature change and thermal deform of a machine tool, a new method to identify the position of these points by neural network has been proposed. The method uses neural network to establish a model which represents the relationship between thermal displacement and temperature of measurement points according to experimental data, then uses the derivative of thermal error to temperature change of every measurement point obtained from that model to identify the position of these thermal susceptible points. The practical application proves this method to be very effective.