Sensor Selection Method to Accurately Model the Thermal Error in a Spindle Motor

Abstract

Thermal error in machine tools arises from error in positioning the tool due to thermal distortion of the machine structure. Thermal error can be effectively reduced by implementing a compensation algorithm into the machine control loop. The compensation algorithm is derived from a disturbance model or mathematical representation of the thermal error. In this paper, thermal error in a spindle motor assembly is predicted from temperature measurement through the disturbance model. Model reduction technique is applied to eliminate the temperature sensor, which has least effect on the model output (thermal error). Applying the sensor selection method resulted in a reduced number of temperature sensors (10 to 4) required to achieve the same model prediction accuracy. In the final experiment, the spindle motor is subjected to a 5-h cyclical heat load and the maximum prediction accuracy achieved using the reduced set sensors is 1.5 $\mu$ m (time averaged accuracy of 0.77 $\mu$ m).