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Subsurface crack size measurement method based on surface wave and BP neural network

A BP neural network, crack size technology, applied in measuring devices, material analysis by optical means, instruments, etc., to achieve the effect of high precision and fast measurement speed

Active Publication Date: 2021-10-08
HANGZHOU DIANZI UNIV
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Problems solved by technology

But these studies only evaluated the depth of surface cracks

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  • Subsurface crack size measurement method based on surface wave and BP neural network
  • Subsurface crack size measurement method based on surface wave and BP neural network
  • Subsurface crack size measurement method based on surface wave and BP neural network

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with accompanying drawing.

[0041] The present invention is to detect the depth and height of the subsurface cracks produced in the processing of precision machining materials, so as to facilitate subsequent processing to remove the defect layer, and proposes a quantitative measurement method for subsurface cracks based on ultrasonic surface wave frequency domain parameters. The specific plan is as follows:

[0042] The quantitative measurement method adopts a subsurface crack quantitative measurement device based on ultrasonic surface wave frequency domain parameters, including a pulsed laser probe 3 , a first ultrasonic probe 4 and a second ultrasonic probe 5 . Both the first ultrasonic probe 4 and the second ultrasonic probe 5 are attached to the workpiece 1 and are respectively located on both sides of the subsurface crack 2 . The pulsed laser probe 3 is arranged on the side of the first ultrason...

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Abstract

The invention discloses a subsurface crack size measurement method based on a surface wave and a BP neural network. The method comprises the following steps of 1, carrying out ultrasonic reflection and transmission signal detection on a subsurface crack with known buried depth and length; 2, extracting the time domain features and frequency domain features of a reflected surface wave signal and a transmitted surface wave signal; and 3, performing k-weight wavelet transformation on the time domain features and the frequency domain features obtained in the step 2, further calculating to obtain 2k+2 wavelet entropies, and taking the 2k+2 wavelet entropies as time-frequency domain feature parameters; and 4, training the BP neural network which takes the time-frequency domain characteristic parameters as input and takes the buried depth and length of the subsurface crack as output by utilizing the time-frequency domain characteristic parameters obtained in the step 3. According to the method, the damage-free and contact-free laser ultrasonic technology is adopted, the waveform data characteristic parameters generated by the interaction of the crack and the surface wave are analyzed in combination with the wavelet entropy theory and the neural network, and the burial depth and length of the crack can be obtained at the same time.

Description

technical field [0001] The invention relates to the field of non-destructive testing and machine learning, in particular to a quantitative detection method for subsurface crack burial depth and length based on laser ultrasonic surface wave and BP neural network. Background technique [0002] Ultra-precision machining technology is an important processing method, which has been widely used in various aerospace, national defense, optics, machinery, electronics and other fields. However, an inevitable problem of ultra-precision machining technology is subsurface cracks. Once a subsurface defect occurs, it will grow under operating loads and threaten the health of the structure, resulting in catastrophic consequences. Therefore, techniques for detecting subsurface cracks using ultrasonic techniques have also attracted increasing attention. [0003] In previous studies, J.T. Zeng et al. detected amorphous carbon films with low-frequency scanning probe acoustic microscopy, demon...

Claims

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Application Information

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IPC IPC(8): G01N21/956G01B11/02G01B11/22
CPCG01N21/956G01B11/02G01B11/22
Inventor 王传勇林江王文卢科青陈占锋居冰峰
Owner HANGZHOU DIANZI UNIV
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