Modified Elman network for thermal deformation compensation modeling in machine tools

Thermal deformation is one of the most significant causes of machining errors in machine tools. One effective method is to build a compensation system to offset the thermal errors. Therefore, an accurate model is the key part of the compensation system. This study proposed a modified Elman network (EN) to improve the prediction accuracy of the compensation model in machine tools. And the improved EN can be regarded as a feed-forward neural network with feedback from hidden layer and output layer as an additional set of inputs. The structure of this network determines its dynamic characteristic with memory function. On the other hand, thermal deformation of the spindle contributes the largest part of total thermal errors in precision machining. Then a precise finite element model of machine tool spindle was established. And a new method for calculating the heat transfer convection coefficient on the surface of the spindle was proposed in this paper. The improved EN was used to map the nonlinear relationship between temperature field and thermal errors of the spindle. At last, a verification experiment was implemented on a CNC center and some satisfying results were achieved.