Thermal Error Modeling of a Machining Center using Grey System Theory and HGA-Trained Neural Network

Abstract

The thermal effect on machine tools has become a well-recognized problem in response to the increasing requirement of product quality. The performance of a thermal error compensation system strongly depends on the accuracy of the thermal error model. This paper presents a novel thermal error modeling technique including two mathematic schemes: GM(1,N) model of the grey system theory and the hierarchy-genetic-algorithm (HGA) trained neural network in order to map the temperature ascent against thermal drift of the machine tool. Fist, the GM(1,N) scheme of the grey system theory was applied to minimize the numbers of the temperature sensors on machine. Then, the HGA method is incorporated into the neural network training to optimize its layer numbers and neurons in each layer. These two schemes provide an efficient and accurate thermal error compensation for CNC machine tools. The thermal error compensation technique built in this study can be applied to any type of CNC machine tool because the error model parameters are only calculated mathematically