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Grinding machine main shaft thermal error prediction method

A prediction method and thermal error technology, applied in the direction of instruments, computer control, simulators, etc., can solve the problems that the accuracy cannot meet the requirements, it is difficult to deal with and adjust, and achieve less learning samples, good adaptability, and short training time Effect

Pending Publication Date: 2022-03-08
BEIJING UNIV OF TECH
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Problems solved by technology

However, the accuracy of the current artificial neural network thermal deformation prediction model cannot meet the requirements
Difficult to deal with and adjust when forecasts do not meet expectations

Method used

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  • Grinding machine main shaft thermal error prediction method
  • Grinding machine main shaft thermal error prediction method
  • Grinding machine main shaft thermal error prediction method

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Embodiment Construction

[0059] A method for predicting the thermal error of a grinding machine spindle. The invention is based on the theory of heat conduction. The temperature difference, thermal energy and thermal expansion models of the spindle are established, and then combined with the convolution artificial neural network analysis method, the thermal error of the spindle is finally accurately predicted.

[0060] This method specifically comprises the following steps:

[0061] step one:

[0062] Establish a spindle thermal expansion model;

[0063] The essence of thermal expansion is the property that the volume of a substance increases with the increase of the heat energy contained in it. T(t) and E(t) represent the temperature and thermal energy of the substance at time t, respectively. α, ρ and C represent the thermal expansion, density and specific heat capacity coefficient of the material, and dx(t), dy(t), and dz(t) represent the length, width and height of the material at time t. There...

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Abstract

The invention discloses a grinding machine spindle thermal error prediction method, which reveals the relationship among the temperature difference, heat energy and thermal expansion of a spindle part by establishing a spindle thermal expansion model and a thermal error prediction model based on an artificial neural network, and provides a new thought for improving the machining precision of the spindle. The relationship between temperature difference and thermal deformation is established by utilizing a heat conduction theory, and the thermal deformation essence of the spindle is revealed. And predicting the thermal deformation of the main shaft by taking the temperature difference between the surface of the main shaft and the external environment as the input of the neural network model. And finally, training the prediction model through a back propagation algorithm to determine model parameters. The method has the advantages of few learning samples, short training time and good adaptability. In addition, the rotating speed and the main shaft load can be reflected through the input temperature difference of the neural network. Therefore, the method can be widely applied to different grinding conditions.

Description

technical field [0001] The invention relates to a method for predicting the thermal error of a spindle based on the principle of heat conduction and a neural network method, and belongs to the technical field of machine tool precision design. Background technique [0002] As the main tool for precision manufacturing, the performance of CNC machine tools is directly related to the development of the entire manufacturing industry. However, with the high speed and high precision of CNC machine tools, the machining accuracy of machine tools is increasingly affected by factors such as force and heat in the machining process. Research shows that the two major error sources of CNC machine tools are geometric errors and thermal errors. In fact, the manufacturing and assembly technology of machine tools has been continuously improved, so that the influence of geometric errors on the spatial accuracy of machine tools has gradually decreased. Thermal error accounts for 40%-70% of the...

Claims

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Application Information

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IPC IPC(8): G05B19/404
CPCG05B19/404G05B2219/37211
Inventor 范晋伟王培桐陶浩浩任行飞
Owner BEIJING UNIV OF TECH
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