Tool abrasion monitoring method based on wavelet denoising and Hilbert-Huang transformation

A wavelet denoising and tool wear technology, applied in the field of signal processing, can solve problems such as energy leakage and insufficient self-adaptation, and achieve strong adaptability, high self-adaptability, and good application prospects

Inactive Publication Date: 2014-08-06
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The present invention proposes a method for processing vibration signals using Hilbert-Huang transform on the basis of wavelet denoising, which can overcome the problems of e

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  • Tool abrasion monitoring method based on wavelet denoising and Hilbert-Huang transformation
  • Tool abrasion monitoring method based on wavelet denoising and Hilbert-Huang transformation
  • Tool abrasion monitoring method based on wavelet denoising and Hilbert-Huang transformation

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[0039] The tool wear monitoring method based on wavelet denoising and Hilbert-Huang transform in this embodiment adopts the following steps:

[0040] Step 1: Collect several groups of vibration signals of the same type of tool in three periods of time during operation. The three periods of time correspond to the initial wear, normal wear and severe wear of the tool; the sampling time of the three time periods is the same; different groups are the same The cutting parameters of different types of tools are different during operation.

[0041] In this embodiment, the orthogonal test method is used to specify the three elements of cutting and tool wear, as shown in Tables 1 and 2, respectively. The cutting parameters of the milling machine are set, that is, the spindle speed, feed rate, and depth of cut. During the operation, the initial wear, normal wear and severe wear of the tool (the specified tool wear amount is 0-0.05 for its wear, 0.05-0.3mm is for normal wear, and when it is g...

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Abstract

The invention provides a tool abrasion monitoring method based on wavelet denoising and Hilbert-Huang transformation. According to the method, on the basis of wavelet denoising, the Hilbert-Huang transformation method is used for analyzing signals acquired in the machine tool operating process; according to an amplitude average value, obtained through EMD, of each IMF component and a time-frequency spectrum and a marginal spectrum which are obtained through Hilbert transformation, a normal state of a tool and an abrasion state of the tool are compared, and obvious characteristics are obtained. The self-adaptability of the method is much higher than that of other characteristic extraction methods; according to the method, the defects of high-frequency resolution are overcome, and meanwhile signal characteristics further retain all physical significances contained in original signal characteristics.

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Application Information

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Owner NORTHWESTERN POLYTECHNICAL UNIV
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