A thermal deformation prediction method for grinding machine’ spindle

Thermal deformation is the main factor of the machining accuracy for grinding machines, which seriously restricts the precision improvement of grinding machines. However, at present, there are little researches on thermal error prediction, and the accuracy of the prediction model is comparatively low. Thus, a novel approach for thermal deformation prediction of grinding machine spindle based on heat energy conduction principle and neural network is proposed in this paper. Firstly, the temperature sensors’ pairs are applied to measure the temperature deviation between the spindle surface and its adjacent ambient which are directly related to the heat energy exchange. Secondly, the temperature deviations of each segment of the spindle are taken as inputs, which will exist and accumulate in the form of heat energy subsequently in the convolutional neural network. Meanwhile, the accumulated heat energy is mixed and transferred to the different segments of the spindle in the convolutional neural network. Thirdly, the thermal deformation caused by the increment of heat energy is considered as the output of thermal error prediction result based on the principle of heat energy conduction. Finally, the simulations and experiments are implemented to validate the feasibility and effectiveness of the proposed method.