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Machine tool thermal error modeling method based on ant colony neural network

A modeling method and neural network technology, applied in the field of thermal error modeling of machine tools based on ant colony neural network, can solve the problems of falling into local extremum and long convergence time, achieve strong thermal error approximation ability, improve prediction ability, Controlling the Effect of Thermal Distortion

Inactive Publication Date: 2015-11-25
SHANDONG UNIV OF TECH
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Problems solved by technology

It solves the problems that the current BP neural network mainly uses the gradient descent method to train the connection weights, the convergence time is long, and it is easy to fall into local extremum. It enhances the prediction ability of the model and the thermal error approximation ability, and improves the efficiency of thermal error compensation. Provides a useful reference for thermal error modeling of other types of machine tools

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  • Machine tool thermal error modeling method based on ant colony neural network
  • Machine tool thermal error modeling method based on ant colony neural network
  • Machine tool thermal error modeling method based on ant colony neural network

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Embodiment Construction

[0019] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0020] The technical flow chart of the present invention is as figure 1 As shown, the main heat source of the gear hobbing machine during processing and the influence of thermal deformation on the machining accuracy of the gear are analyzed. The measured temperature signal is used as the model input layer, and the thermal deformation error is used as the model output layer. Based on the neural network algorithm, the thermal error model of the gear hobbing machine is established. , based on the ant colony algorithm to optimize the weights in the learning process of the neural network, the ant colony neural network model is obtained. Through the self-developed thermal error compensation system, the approximation performance of the built model is verified, and the three models are applied to predict and analyze the radial thermal deformation err...

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Abstract

The invention relates to a data processing method in the field of precise machining technology, and particularly to a machine tool thermal error element modeling method based on an ant colony neural network. The machine tool thermal error modeling method comprises the steps of (1) analyzing a thermal error source of a hobbing machine; (2) establishing a neural network model; (3) performing network weights training based on an ant colony algorithm; and (4) performing a thermal error compensation experiment. The machine tool thermal error modeling method has advantages of high thermal error approximation ability, high prediction precision and high robustness. Thermal deformation of the hobbing machine can be effectively controlled, and furthermore a gear machining precision is improved.

Description

technical field [0001] The invention relates to a data processing method in the technical field of precision machining, in particular to a machine tool thermal error modeling method based on an ant colony neural network. Background technique [0002] A large number of studies have shown that thermal error is the largest error source of machine tools, accounting for 40% to 70% of the entire machine tool error. After the gear hobbing machine works for a long time, the heat generated has a great impact on its machining accuracy. With the increase of cutting speed and cutting power, the error caused by thermal deformation will seriously affect the gear machining accuracy. With the continuous development of modern manufacturing technology, the effect of eliminating thermal deformation by controlling the main heat source or changing the structure of the gear hobbing machine is no longer obvious, and the implementation of thermal error compensation for gear hobbing is being obtaine...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/404
CPCG05B19/404
Inventor 郭前建徐汝锋贺磊
Owner SHANDONG UNIV OF TECH
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