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Feature strengthening method and system for on-line monitoring of state of thin-walled workpiece milling cutter

A technology for milling tools and thin-walled parts, which is applied in the field of condition monitoring, can solve the problems that the monitoring model is easily affected by environmental noise, the correlation between signal features and recognition targets is weak, and the calculation efficiency is low, so as to achieve reduced complexity and strong adaptability , the effect of high recognition accuracy

Active Publication Date: 2022-07-29
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005]Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a feature enhancement method and system for on-line monitoring of the state of thin-walled milling tools, which solves the problem of existing In the tool state monitoring method, the weak correlation between the signal feature and the recognition target leads to the problems that the monitoring model is easily affected by environmental noise, poor accuracy, long training time, and low calculation efficiency. While ensuring the calculation efficiency, the tool state monitoring algorithm is improved. Rod

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  • Feature strengthening method and system for on-line monitoring of state of thin-walled workpiece milling cutter
  • Feature strengthening method and system for on-line monitoring of state of thin-walled workpiece milling cutter
  • Feature strengthening method and system for on-line monitoring of state of thin-walled workpiece milling cutter

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Embodiment 1

[0038] This embodiment provides a feature enhancement method for online monitoring of the state of a milling tool for thin-walled parts, including:

[0039] Calculate the signal characteristics of the sensing signal of each pass before strengthening, and obtain the tool wear amount after each pass;

[0040] Calculate the correlation between each sensing feature vector and the tool wear value according to the signal feature and the tool wear amount, and obtain the feature correlation value of the entire cutting process;

[0041] Calculate the feature weight coefficient for each feature correlation value to obtain the feature weight coefficient vector of the entire cutting process;

[0042] Based on the feature matrix and the weight coefficient matrix, the enhanced feature component matrix is ​​obtained, the enhanced feature component matrix is ​​summed and the average value is calculated to obtain the enhanced feature vector, so as to improve the correlation between the feature...

Embodiment 2

[0110] In order to test the validity of the first embodiment, a feature enhancement method for online monitoring of the thin-walled milling tool state of the first embodiment is verified based on the "PHM2010 tool wear data set" published by the International Association for Fault Diagnosis and Health Management.

[0111] Among them, each tool has a total of 315 sets of cutting signals, that is, m=315, and each set of signals gives a tool wear value (average wear width of the flank), and the average value and variance of each set of signals are extracted according to step S2. , standard deviation, root mean square, skewness, and kurtosis, a total of 6 signal characteristics, that is, n=6.

[0112] According to formula (18), the correlation vector ε of each signal feature and tool wear value can be obtained, Figure 4 The correlation calculation results of six signal features and the predicted target (i.e. tool wear value) are shown, from Figure 4 It can be seen that in this ...

Embodiment 3

[0116] This embodiment provides a feature enhancement system for online monitoring of the state of a milling tool for thin-walled parts, including:

[0117] The feature acquisition module is configured to: calculate the signal features of the sensing signal of each tool pass before strengthening, and obtain the tool wear amount after each pass;

[0118] The feature correlation value acquisition module is configured to: calculate the correlation between each sensing feature vector and the tool wear value according to the signal feature and the tool wear amount, and obtain the feature correlation value of the entire cutting process;

[0119] The feature weight coefficient vector acquisition module is configured to: calculate the feature weight coefficient for each feature correlation value, and obtain the feature weight coefficient vector of the entire cutting process;

[0120] The enhanced feature vector acquisition module is configured to: obtain the enhanced feature component...

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Abstract

The invention discloses a feature strengthening method and system for monitoring the state of a thin-walled workpiece milling cutter on line, and relates to the technical field of state monitoring, and the method comprises the steps: calculating the signal feature of a feeding sensing signal each time before strengthening, and obtaining the wear loss of the cutter after feeding each time; calculating the correlation between each sensing feature vector and a tool wear value according to the signal features and the tool wear amount to obtain a feature correlation value of the whole cutting process; calculating a feature weight coefficient for each feature correlation value to obtain a feature weight coefficient vector of the whole cutting process; and obtaining an enhanced feature component matrix based on the feature matrix and the weight coefficient matrix, performing summation processing on the enhanced feature component matrix, and calculating an average value to obtain an enhanced feature vector so as to improve the correlation between the feature vector and the tool wear value. According to the method, the robustness of a tool state monitoring algorithm is improved while the calculation efficiency is ensured.

Description

technical field [0001] The invention relates to the technical field of state monitoring, in particular to a feature enhancement method and system for online monitoring of the state of a milling tool for thin-walled parts. Background technique [0002] Impellers, blades and integral blisks are the core power components in aero-engines. In order to meet the service performance requirements, these parts are mostly made of difficult-to-machine materials such as titanium alloys and high-temperature alloys, which are typical difficult-to-machine thin-walled parts. . Due to the material and structural characteristics, the tool wears rapidly during the processing. If the tool is not changed in time, the surface quality of the workpiece will be affected, and the tool will be wasted easily if the tool is changed too early. Therefore, choosing the timing of tool change reasonably and accurately is the key guarantee for efficient and precise machining of aerospace thin-walled parts. I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 宋清华王润琼刘战强王兵蔡玉奎
Owner SHANDONG UNIV