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Milling cutter wear prediction method and state recognition method

A prediction method and milling cutter technology, applied in character and pattern recognition, instruments, calculation models, etc., can solve the problem of low modeling accuracy, achieve strong generalization ability, ensure search accuracy, optimal efficiency and modeling accuracy Effect

Inactive Publication Date: 2018-05-15
HUAZHONG UNIV OF SCI & TECH +1
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AI Technical Summary

Problems solved by technology

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides a milling cutter wear prediction method and a state identification method, the purpose of which is to solve the technical problem of low modeling accuracy of the existing method

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  • Milling cutter wear prediction method and state recognition method
  • Milling cutter wear prediction method and state recognition method
  • Milling cutter wear prediction method and state recognition method

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Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0045] In order to solve the problem in the present invention, set up the cuckoo search (Self-Adaptive Step Cuckoo Search, ASCS) based on adaptive step size and preference random walk behavior, obtain optimal penalty factor, optimal radial basis kernel function width coefficient In the cuckoo search, the adaptive function is the optimal eigenvector extracted from the vibration signal, the optima...

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Abstract

The invention discloses a milling cutter wear prediction method and a state recognition method. The wear prediction method comprises the following steps that firstly, wavelet noise reduction processing is performed on milling vibration data, feature extraction is performed on vibration signals from three aspects including time domain, frequency domain and time domain, after an initial feature vector set is obtained, a correlation coefficient method is used for calculating the correlation between feature vectors and wear amount, and an optimal feature vector set is obtained by screening; then,an average relative error predicted by a least squares support vector machine is defined as a fitness function of an adaptive step size cuckoo search algorithm, and by searching for a nest position, input parameters of the least squares support vector machine are optimized; finally, the wear amount is predicted by using the optimal least square support vector machine. Through comparison with two other hybrid intelligent algorithms, the superiority of an ASCS- LSSVR algorithm is verified.

Description

technical field [0001] The invention belongs to the technical field of mechanical processing, and more specifically relates to a wear prediction method and a state recognition method of a milling cutter. Background technique [0002] At present, the automatic monitoring schemes in the field of tool wear are mainly divided into two categories: direct method and indirect method. The direct method is generally applied to off-line monitoring in the non-processing process. The parameters such as the position and shape of the tool are directly obtained through the sensing device to determine the wear condition of the tool; the indirect method is to measure the indirect indicators such as tool vibration, force, current, and acoustic emission and Establish a correlation relationship with the cutting wear state, so as to obtain the wear degree of the tool. [0003] Although the direct method has high precision, it often has the disadvantages of not being able to guarantee real-time ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/00G06N99/00
CPCG06N20/00G06F2119/04G06F30/20G06F2218/08
Inventor 戴稳张超勇孟磊磊邵新宇马雷博詹欣隆李振国余俊洪辉
Owner HUAZHONG UNIV OF SCI & TECH
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