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The development of precision machine tools requires an accurate understanding of the thermal behaviour of the tools. Therefore, by developing highly accurate machine tool thermal behaviour simulations, it can accurately forecast the functional performance of a machine tool at the prototype design stage and provide guidance on its design and performance optimization. To improve the accuracy of the thermal simulation of machine tools, it proposed whole-machine temperature field and thermal deformation modeling and a simulation method for vertical machining centers. Based on the studies of thermal-structure coupling finite-element data flow and its critical conditions, it also proposed design and computational methods for obtaining the heat-source power, when various rotational speeds, the forced convection heat transfer coefficient between a rotating surface and the air, the combined heat transfer coefficient between a stationary surface and the air/environment, and key contact surface thermal resistances were considered. Furthermore, this paper performed finite-element simulation analysis based on the constructed whole-machine thermal model, and the temperature characteristics and heat deformation mechanism/state were obtained. After comparing the simulation and experimental results, it verified the effectiveness of the finite-element simulation model and the relevant methods and confirmed that the simulation accuracy for both the temperature characteristics and the thermal deformation had been improved significantly, with a simulation error less than 10 %. [ABSTRACT FROM AUTHOR]

This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]