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Nonlinear autoregressive neural network machine tool thermal error modeling method with external input

A nonlinear autoregressive and neural network technology, applied in the field of thermal error compensation of precision CNC machine tools, can solve the problem of difficult to establish thermal error prediction models, improve prediction accuracy and adaptability, improve modeling accuracy, overcome hysteresis Effects of Features

Pending Publication Date: 2020-01-17
QUFU NORMAL UNIV
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Problems solved by technology

Due to this characteristic of temperature-thermal error, it is difficult to build an accurate thermal error prediction model

Method used

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  • Nonlinear autoregressive neural network machine tool thermal error modeling method with external input
  • Nonlinear autoregressive neural network machine tool thermal error modeling method with external input
  • Nonlinear autoregressive neural network machine tool thermal error modeling method with external input

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Experimental program
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Embodiment

[0050] Embodiment: Carry out modeling test to the axial thermal error of a CNC lathe spindle, the measured modeling temperature curve is as follows image 3 As shown, the thermal error as Figure 4 shown. Follow step 2 to normalize all measurement data; according to step 3, take N=50 when grouping data, calculate the relative information entropy of each temperature point, select temperature measurement point 6 as the modeling input point, temperature measurement point 6 hysteresis curves with thermal errors such as Figure 5 As shown; in step 3, the input and output delay of the NARX model is selected to be 2 orders, the hidden layer is 10 layers, and the training algorithm is Levengerg-Marquardt, and then the model is trained. After getting the model use Figure 5 , 6 The data is tested, and the results are as follows Figure 7 As shown, the maximum error between the model prediction data and the measured data is about 1.5 microns, which can meet the requirements of prac...

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Abstract

The invention discloses a numerical control machine tool thermal error modeling method. The method comprises the steps of 1, testing machine tool temperature and thermal error data; 2, selecting a keytemperature measuring point as the input of a thermal error model by adopting an information entropy algorithm; 3, training an NARX model; and 4, calculating a real-time thermal error compensation value of the machine tool through the model. According to the method provided by the invention, a temperature measurement point which is most relevant to a thermal error can be found by adopting a relative information entropy algorithm; the NARX model can overcome the hysteresis characteristic of a temperature-thermal error, and has the advantages of high prediction precision and high adaptability.

Description

technical field [0001] The invention belongs to the technical field of thermal error compensation of precision numerical control machine tools, and relates to a thermal error modeling method of a machine tool with an external input nonlinear autoregressive neural network NARX (Nonlinear AutoRegressive with eXternal input neural network). Background technique [0002] During the working process of the machine tool, due to factors such as spindle heating, frictional heat generation of moving parts, cutting heat and environmental temperature changes, various components of the machine tool will be thermally deformed, resulting in changes in the ideal position of the cutting tool and the workpiece, resulting in machining errors. At present, due to the improvement of machining and manufacturing technology, the geometric error of precision machine tools is small, and thermal error has become the most important factor affecting its accuracy. Relevant studies have shown that thermal ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F30/17G06N3/08
CPCG06N3/08
Inventor 张成新孔凡红崔敏
Owner QUFU NORMAL UNIV
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