CN-121994929-A - Ultrasonic defect burial depth detection and imaging method for composite material

CN121994929ACN 121994929 ACN121994929 ACN 121994929ACN-121994929-A

Abstract

The invention discloses a composite material ultrasonic defect burial depth detection and imaging method, which belongs to the technical field of nondestructive detection and comprises the steps of collecting an A-scan signal of a composite material test piece through a water immersion type focusing probe, carrying out continuous wavelet transformation and dynamic threshold noise reduction on the signal, constructing a particle swarm optimization wavelet transformation mode maximum value (PSO-WTMM) model, searching optimal parameters through an adaptability function of envelope entropy fusion peak penalty, extracting echo time differences of the upper surface and the lower surface based on the optimal parameters, calculating defect burial depth by combining sound velocity, constructing a time difference and amplitude ratio matrix by point-by-point scanning, and outputting a C-scan image after interpolation enhancement. The ultrasonic defect burial depth detection and imaging method for the composite material overcomes the defects of experience dependence and poor robustness of the traditional method, improves the defect depth positioning precision and imaging quality, and is suitable for nondestructive detection and health monitoring of an aviation composite material structure.

Inventors

LIU YUAN
XU XINXIN
LU CHAO
HUANG LIUWEI
LIU CHEN
LI XIAOHAN
YUAN ZIGANG
YU JINGWEN
Fang Wangjun

Assignees

南昌航空大学

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (8)

1. The ultrasonic defect burial depth detection and imaging method for the composite material is characterized by comprising the following steps of: S1, collecting an A-scan ultrasonic signal of a composite material test piece through an ultrasonic probe; S2, carrying out continuous wavelet transformation on the A-scan ultrasonic signal, carrying out dynamic threshold noise reduction treatment, and extracting a wavelet transformation mode maximum curve of the signal; s3, constructing a particle swarm optimization wavelet transformation mode maximum model based on a wavelet transformation mode maximum curve, and taking a wavelet scale range, a peak detection threshold value and a minimum peak interval as optimization variables; S4, searching an optimal parameter combination of an optimization variable through a particle swarm algorithm based on a fitness function of a fusion envelope entropy and a dynamic peak value quantity penalty mechanism; S5, performing wavelet transformation mode maximum analysis based on the optimal parameter combination, extracting the time difference between the upper surface echo and the defect or the lower surface echo, and calculating the defect burial depth by combining the sound velocity of the material; S6, executing steps S2 to S5 on each scanning point in the detection area, constructing a time difference matrix and an amplitude ratio matrix, and improving the image resolution through interpolation processing to obtain a C-scanning image of the defect.
2. The ultrasonic defect burial depth detection and imaging method for the composite material according to claim 1, wherein in the step S1, the ultrasonic probe is a water immersion type focusing probe, and the test piece is placed in a liquid immersion tank with the defect side facing downwards, and the ultrasonic probe is aligned to a defect area from the upper surface for signal acquisition.
3. The method for detecting and imaging ultrasonic defect burial depth for composite materials according to claim 2, wherein the signal acquisition step comprises the following steps: Horizontally placing a composite material test piece in a liquid immersion tank with one side of a prefabricated defect facing downwards, and enabling the sound beam axis of a probe to be aligned to the central area of the defect; According to the detection requirement, configuring the center frequency of the probe and the sampling frequency of signal acquisition; and the control unit is cooperated with the two-dimensional scanning frame to drive the water immersion type focusing probe to scan the test piece detection area point by point.
4. The method for detecting and imaging ultrasonic defect burial depth for composite material according to claim 1, wherein step S2 specifically comprises: s21, taking a Mexico cap wavelet as a mother wavelet, wherein the time domain expression of the Mexico cap wavelet is as follows: ; Wherein, the Representing time; The frequency domain expression is: ; Wherein, the Representing the frequency; performing continuous wavelet transformation on the A-scan ultrasonic signal to obtain a time-frequency domain wavelet coefficient matrix; S22, estimating a noise standard deviation based on the median absolute deviation of the wavelet coefficient, wherein the calculation formula is as follows: ; Wherein, the In the form of a matrix of wavelet coefficients, As a scale factor of the dimensions of the device, In order for the translation factor to be a factor, Representing a median function; S23, introducing a scale adjustment factor Constructing adaptive threshold values dynamically changing along with scales The calculation formula is as follows: ; Wherein, the For the length of the signal, For the segment weight adjustment factor, the calculation formula is: ; Wherein, the For the scale-weighted sensitivity coefficient, To analyze the geometric mean of a range of scales, , The minimum dimension is indicated as such, Representing the maximum scale; s24, filtering and denoising the wavelet coefficients by adopting a soft threshold function The expression is: ; Wherein, the Is a sign function; S25, reconstructing a time domain signal by adopting an energy weighting mode, and weighting coefficients The calculation formula is as follows: ; The echo amplitude is adaptively enhanced by adopting an enhancement factor at the position corresponding to the significant peak value of the original signal in the reconstructed signal, and the preprocessed signal is obtained through the sliding average smoothing treatment of the local window; s26, carrying out continuous wavelet transformation on the preprocessed signals again, extracting a wavelet transformation mode maximum curve, and identifying signal singular points.
5. The composite material-oriented ultrasonic defect burial depth detection and imaging method according to claim 4, wherein in step S3, a particle swarm optimization wavelet transform mode maximum model is constructed, and the method specifically comprises the following steps: S31, encoding the wavelet scale range, the peak detection threshold value and the minimum peak interval into a particle position vector A parameter search boundary is set according to signal characteristics, wherein, The minimum dimension is indicated as such, Representing the maximum dimension of the display, Representing the peak detection threshold value, Representing the minimum peak spacing; s32, initializing particle swarm parameters, and setting the number of the adapted particles, the iteration times, the inertia weight, the learning factors and the inertia weight attenuation coefficients according to the optimization requirements; s33, designing fitness function : ; Wherein, the For the number of iterations, For the purpose of the envelope entropy, In order for the number of peaks to deviate, In order to have a very large penalty constant, For the dynamic penalty factor(s), In order to actually detect the number of peaks, Is the maximum value of a preset peak value range; S34, performing iterative optimization according to a particle speed and position updating formula: ; Wherein, the Is the first The individual particles are cut off to the first The optimal position is historic for the next iteration, Cut off to the first for the whole population The historical optimal position found by the next iteration, And Respectively represent particles In the first place The speed and position at the time of the iteration, And Respectively represent particles In the first place The speed and position at the time of the iteration, The weight of the inertia is represented by the weight of the inertia, And In order for the learning factor to be a function of, The step length of the flight of the particles to the historical optimal position of the particles is adjusted, The step length of the flight of the particles to the global historical optimal position is adjusted, And Is a random number between [0,1 ]; And S35, outputting a global optimal parameter combination when iteration is performed to the maximum iteration times.
6. The method for detecting and imaging ultrasonic defect burial depth for composite material according to claim 5, wherein in step S4, the specific calculation step of the fitness function comprises: s41, performing Hilbert transformation on a wavelet transformation mode maximum curve corresponding to the current parameter combination to obtain an envelope curve of the wavelet transformation mode maximum curve; s42, calculating shannon entropy of the envelope, and taking the shannon entropy as the envelope entropy : ; Wherein, the Is the envelope line The energy duty cycle of the individual sampling points, The total number of the envelope sampling points is; S43, detecting the actual peak value number on the envelope line Setting a reasonable interval of effective peak value quantity , In order to preset the lower limit of the peak value, To preset the peak upper limit, calculating the peak quantity deviation : ; S44, constructing dynamic penalty coefficients The calculation formula is as follows: ; Wherein, the For the number of iterations, For the total number of iterations, And Respectively an initial penalty coefficient and a termination penalty coefficient; S45, combining envelope entropy Dynamic penalty coefficient Peak number bias Obtaining the fitness function value 。
7. The method for detecting and imaging ultrasonic defect burial depth for composite material according to claim 6, wherein step S5 specifically comprises: s51, extracting a wavelet transformation mode maximum curve of the preprocessed signals based on the optimal parameter combination, identifying mode maximum peak points of the echo of the upper surface and the echo of the defect or the echo of the lower surface of the corresponding material, and recording time of the two peak points And Calculating a time difference: ; s52, calculating the defect burial depth The calculation formula is as follows: ; Wherein, the Is the propagation speed of ultrasonic waves in the composite material.
8. The method for detecting and imaging ultrasonic defect burial depth for composite materials according to claim 7, wherein step S6 specifically comprises: S61, defining a detection area, setting a scanning step length according to the detection resolution requirement, scanning the detection area point by driving a probe through a two-dimensional scanning frame, repeating the steps S2 to S4, and obtaining the time difference of each scanning point Sum-to-amplitude ratio The amplitude ratio is the amplitude corresponding to the second peak point of the mode maximum curve divided by the amplitude corresponding to the first peak point; s62, constructing a time difference matrix Sum-to-amplitude ratio matrix ; S63, adopting a natural neighborhood interpolation method to perform time difference matrix Sum-to-amplitude ratio matrix And performing resolution enhancement processing.

Description

Ultrasonic defect burial depth detection and imaging method for composite material Technical Field The invention relates to the technical field of nondestructive testing, in particular to a composite material-oriented ultrasonic defect burial depth detection and imaging method. Background The composite material has become a key structural material in the core fields of aerospace (aircraft fuselage, wing parts), rail transit (high-speed train body), new energy equipment (wind power blades) and the like by virtue of excellent characteristics of light weight, high strength, fatigue resistance, strong designability and the like. However, the materials are easily affected by process fluctuation, uneven resin distribution and the like in the manufacturing process, and can face the effects of assembly stress, complex load, environmental corrosion and the like in the service process, thereby causing microscopic and macroscopic defects such as layering, pores, fiber breakage and the like. Wherein, layering damage is as the foremost failure mode of lamination structure, can show the rigidity, the intensity and the fatigue life of reduction material, directly threatens the safe operation of equipment. Therefore, developing high-efficiency and accurate internal defect detection and quantitative evaluation technology has important significance for guaranteeing the safety and reliability of the whole life cycle of the composite material structure. In the existing nondestructive detection technology system, ultrasonic detection is widely applied due to high sensitivity to lamellar structure defects and moderate detection cost. Traditional contact ultrasonic detection relies on the couplant between the probe and the test piece to realize acoustic energy transmission, but the problems of uneven coupling pressure, ageing of the couplant and the like easily cause signal fluctuation, and the detection consistency and reliability are affected. The water immersion ultrasonic detection can realize stable acoustic energy transmission in a non-contact mode (such as water immersion coupling), effectively overcomes the defect of contact coupling, has high acoustic energy transmission efficiency, and can adapt to the detection requirement of complex structural members by adjusting the acoustic beam path. However, the ultrasonic detection signal has non-stationarity, is easily interfered by environmental noise and multi-interface reflection, and is difficult to effectively extract defect characteristics by the traditional spectrum analysis method. The wavelet transformation mode maximum value (WTMM) method can accurately identify signal singular points (corresponding to defect reflection echoes) by means of time-frequency localization characteristics, and becomes one of core technologies for defect positioning. However, the performance of the method is highly dependent on the selection of parameters such as wavelet scale range, peak detection threshold value, minimum peak interval and the like, the prior art is mostly provided with parameters through experience, and systematic optimization is lacked, so that the robustness of the method is insufficient when facing different materials and different defect types, the defect depth measurement error is larger, and the imaging resolution and defect contour recognition capability are required to be improved. Aiming at the problems, a composite material ultrasonic detection method capable of realizing parameter self-adaptive optimization and improving defect positioning accuracy and imaging quality is needed to solve the core defects of high parameter dependence experience, high detection error, weak robustness and the like in the prior art. Disclosure of Invention The invention aims to provide a composite material-oriented ultrasonic defect burial depth detection and imaging method, which is characterized in that core parameters of wavelet transformation mode maximum values (WTMM) are optimized in a self-adaptive mode through a Particle Swarm Optimization (PSO), and an adaptation function of an envelope entropy and a peak penalty mechanism is combined, so that accurate positioning of defect depth and high-quality C-scan imaging are realized, detection errors are reduced, and robustness and engineering applicability of the method are improved. In order to achieve the above purpose, the invention provides a composite material-oriented ultrasonic defect burial depth detection and imaging method, which comprises the following steps: S1, collecting an A-scan ultrasonic signal of a composite material test piece through an ultrasonic probe; S2, carrying out continuous wavelet transformation on the A-scan ultrasonic signal, carrying out dynamic threshold noise reduction treatment, and extracting a wavelet transformation mode maximum curve of the signal; s3, constructing a particle swarm optimization wavelet transformation mode maximum model based on a wavelet transformation mode maximum curve, and t