Shaft system thermal error modeling method and thermal error compensation system based on SLSTM neural network

A neural network, modeling method technology, applied in the field of mechanical error analysis, can solve problems such as poor robustness, poor predictive performance, and inability to apply thermal information

Active Publication Date: 2020-06-05
CHONGQING UNIV
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Problems solved by technology

Traditional thermal error models cannot apply past thermal informa

Method used

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  • Shaft system thermal error modeling method and thermal error compensation system based on SLSTM neural network
  • Shaft system thermal error modeling method and thermal error compensation system based on SLSTM neural network
  • Shaft system thermal error modeling method and thermal error compensation system based on SLSTM neural network

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Embodiment Construction

[0073] The present invention will be further described below in conjunction with drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not as limitations of the present invention.

[0074] Under a single heat load, the core temperature of a shaft with the same circular cross-section can be expressed as:

[0075]

[0076] where k 0 , h and T(0) are thermal conductivity, convection coefficient and heat source temperature respectively, and λ is the axial core thermal expansion coefficient of the shaft; L is the initial length of the shaft; T 0 is the initial temperature.

[0077] The thermal elongation of the spindle core is expressed as:

[0078]

[0079] An accurate model of the thermal expansion of the shaft core depends on the temperature response of the shaft to the thermal load. The thermal expansion coefficient λ is a function of temperature. Therefore, the sha...

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Abstract

The invention discloses a shaft system thermal error modeling method based on an SLSTM neural network. The method comprises the following steps: 1) inputting thermal error data of a shaft system changing with time; 2) decomposing the thermal error data into N intrinsic mode components and a residual component by using an EMD algorithm, and respectively converting the component data into a three-dimensional input matrix; 3) encoding the initial time window size, the batch processing size and the unit number of each piece of component data to obtain an original generation bat population; 4) initializing the original generation bat population by adopting a BA algorithm to obtain SLSTM neural networks with different time window sizes, different batch processing sizes and different unit numbers; 5) training the SLSTM neural network by using the thermal error data of the shaft system to determine hyper-parameters; and constructing an EMD-BA-SLSTM network model by using the optimal hyper-parameter, and then reconstructing a prediction component to obtain the output of a prediction result, i.e., the invention also discloses a shaft system thermal error compensation system based on the SLSTM neural network.

Description

technical field [0001] The invention belongs to the technical field of mechanical error analysis, specifically a shaft system thermal error modeling method based on SLSTM neural network and a thermal error compensation system Background technique [0002] The thermal expansion of the shaft system has a hysteresis effect, and the hysteresis effect is significant for the shaft system. Hysteresis effects are important for robust modeling of thermal errors, which lead to time-varying, nonlinear and non-steady-state behavior of thermal expansion temperature behavior. The hysteresis effect means that the current thermally induced error not only depends on the current input, but also has a memory characteristic to the historical thermal effect and is significantly affected by the historical thermal effect. Therefore, the influence of historical thermal information on the current thermal error should be considered in the thermally induced error modeling of the shaft system. Tradit...

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Application Information

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IPC IPC(8): G05B19/404
CPCG05B19/404G05B2219/35015
Inventor 马驰刘佳兰王时龙易力力
Owner CHONGQING UNIV
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