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Thin-walled workpiece cutter back-off deformation error prediction model building method and application thereof

A technology of deformation error and prediction model, which is applied in the field of thin-walled part tool deformation error prediction model establishment, can solve the problems of low processing error accuracy, difficult data measurement and acquisition, etc., to reduce requirements, ensure reliability, and improve The effect of forecast accuracy

Pending Publication Date: 2021-04-16
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The finite element simulation method can effectively solve the difficult problem of data measurement and acquisition in actual processing, but the finite element simulation method has certain limitations in the setting of boundary conditions.
The accuracy of the final machining error prediction result is not high

Method used

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  • Thin-walled workpiece cutter back-off deformation error prediction model building method and application thereof
  • Thin-walled workpiece cutter back-off deformation error prediction model building method and application thereof
  • Thin-walled workpiece cutter back-off deformation error prediction model building method and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] A method for establishing a thin-walled part tool deformation error prediction model, such as figure 1 shown, including:

[0052] The cutting force model is established by the finite element analysis method. The cutting force model is used to input the parameters in the processing process and output the corresponding cutting force and tool deformation error; the tool parameters of the actual production line of thin-walled parts under different processing conditions , The processing parameters and the material cutting force parameters are used as the input of the cutting force model, and the cutting force and the tool deformation error corresponding to each group of parameters are output by the cutting force model to obtain the training data set;

[0053] Obtain multiple sets of cutting force collected from the processing site of thin-walled parts and the corresponding deformation error of the tool, and obtain the test data set;

[0054] A small-sample learning model is...

Embodiment 2

[0093] A method for predicting the deformation error of a thin-walled part, including:

[0094] Input the real cutting force measured on the thin-walled part processing site into the thin-walled part tool deformation error prediction model established by the method for establishing the thin-walled tool deformation error prediction model provided by the above-mentioned embodiment 1, so that the thin-walled part allows The knife deformation error prediction model predicts the corresponding knife deformation error.

[0095] Because the thin-walled part allows the knife deformation error prediction model established by the method for establishing the thin-walled part to allow the knife deformation error prediction model provided by the above-mentioned embodiment 1 to have a higher prediction accuracy, the thin-walled part provided by the embodiment allows the knife to deform the error of the error The prediction method can accurately predict the tool-back deformation error during ...

Embodiment 3

[0097] A computer-readable storage medium, including a stored computer program; when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute the method for establishing a prediction model for the deformation error prediction of a thin-walled part provided in Embodiment 1, and / or the method for predicting the deformation error of the knife for the thin-walled part provided in the above-mentioned embodiment 2.

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Abstract

The invention discloses a thin-walled workpiece cutter back-off deformation error prediction model building method and application thereof, and belongs to the field of machining error prediction, and the method comprises the steps: building a cutting force model through a finite element analysis method, and taking cutter parameters, machining process parameters and material cutting force parameters of an actual production line under different machining conditions as the input of the cutting force model; outputting cutting force and cutter back-off deformation errors corresponding to each group of parameters to obtain a training data set; obtaining cutting force and corresponding cutter back-off deformation errors of multiple sets of machining sites, and obtaining a test data set; adding a data enhancement module between an input layer and a first hidden layer of the neural network, and establishing a small sample learning model for predicting a cutter relieving deformation error according to the cutting force; and training and testing the small sample learning model by using the training data set and the test data set respectively to obtain a thin-walled workpiece cutter back-off deformation error prediction model. The prediction precision of the cutter back-off deformation error in the thin-walled workpiece machining process can be improved.

Description

technical field [0001] The invention belongs to the field of machining error prediction, and more specifically relates to a method for establishing a thin-walled part tooling deformation error prediction model and its application. Background technique [0002] During the processing of thin-walled parts, there is a significant nonlinear relationship between the deformation error of the cutting tool and various measurable signals such as cutting force signals and vibration signals. Thin-walled components are widely used in the aerospace field. There are many types of products and fast replacement, which puts forward a high demand for rapid response in the processing process. The service performance of thin-walled parts depends largely on the level of machining accuracy. Most aerospace parts belong to free-form surfaces in space and are made of difficult-to-machine materials. The processing relies on multi-axis linkage CNC machine tools. The load is large during the cutting pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/23G06F30/27G06N3/04G06N3/08G06F113/24
Inventor 彭芳瑜赵晟强周林孙豪张腾张驰闫蓉
Owner HUAZHONG UNIV OF SCI & TECH
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