Composite material defect detection method based on acoustic resonance imaging

Abstract

The invention relates to the technical field of nondestructive testing of composite materials, discloses a composite material defect detection method based on acoustic resonance imaging, and provides a novel composite material defect detection method which effectively improves the defect detection capability of the composite materials, especially the detection of tiny defects. The resonant frequency can be dynamically adjusted through inductance switching, multi-frequency scanning and full-depth scanning are achieved, high, medium and low detection modes are adopted for layered detection, typical defects of air holes, debonding, layering and the like of the composite material can be distinguished at a time, and then high-stability and high-precision scanning detection is achieved and noise interference is overcome in combination with the probe frame. And a full-depth acoustic impedance distribution diagram is generated in combination with a back projection algorithm, so that the processing accuracy of a detection result is improved, the visualization degree of the detection result is improved, and detection personnel can quickly obtain detection information and analyze the detection information.

Description

Technical Field

[0001] The present invention relates to the technical field of non-destructive testing, and particularly to a method for detecting defects in composite materials based on acoustic resonance imaging. Background Art

[0002] With the in-depth application of composite materials in the aerospace field, the requirements for non-destructive testing of composite materials are constantly increasing. Especially for the detection of micro-defects in some new composite materials with severe sound absorption and increased thickness, traditional non-destructive testing methods all have their respective limitations. For example, although the ultrasonic pulse echo can perform one-sided detection and is easy to operate, when detecting thin-layer or multi-layer composite materials, the resolution is insufficient, making it difficult to accurately identify micro-defects and delamination defects, resulting in limited detection accuracy. The acoustic impedance detection relies on the acoustic impedance difference of materials to judge defects. However, the strong sound absorption characteristics of new materials seriously interfere with the detection signal, making it impossible for the detection result to accurately reflect the internal condition of the material, and it is difficult to guarantee the detection accuracy. The acoustic swept-frequency detection scans the material with sound waves of different frequencies. For composite materials with increased thickness or complex internal structures, the detection signal attenuates severely during propagation, making it difficult to meet the high-precision detection requirements. The mechanical impedance detection uses the mechanical impedance characteristics of materials to judge defects. However, the complex mechanical properties of new materials make the detection result unstable, and it is difficult to effectively detect micro-defects and delamination defects; and so on. Based on this, it is urgent to explore a new composite material detection technology, especially an in-situ detection technology suitable for composite materials such as in-service aircraft. Summary of the Invention

[0003] To solve the above problems, the present invention provides a method for detecting defects in composite materials based on acoustic resonance imaging, and the present invention is implemented as follows:

[0004] A method for detecting defects in composite materials based on acoustic resonance imaging uses an acoustic probe connected to an acoustic detection system to scan and detect the composite material to be inspected. The acoustic probe is a resonant piezoelectric probe, which is connected to the acoustic detection system to emit multi-frequency continuous resonant sound waves. The resonant piezoelectric probe includes:

[0005] A piezoelectric wafer, the polarization direction of which is perpendicular to the detection surface;

[0006] A switchable inductor group, including a plurality of discrete inductors, and the plurality of discrete inductors are connected in parallel with the piezoelectric wafer through a high-frequency relay;

[0007] The process of the scan detection further includes:

[0008] Setting up a micro-probe frame, and placing the resonant piezoelectric probe in the micro-probe frame for restricted space scanning;

[0009] Detection signal processing and imaging, including:

[0010] The amplitude and phase angle of the detection signal are synchronously measured by a lock-in amplifier, and the resonant frequency offset is recorded in real time by a frequency tracking module;

[0011] Classify the detection signals based on a preset defect determination rule:

[0012] When the phase angle of the detection signal lags, mark the current signal as a pore defect signal; when multiple peaks appear in the detection signal, mark the current signal as an adhesive failure defect signal; when the resonant frequency of the detection signal significantly deviates, mark the current signal as a delamination defect signal;

[0013] Generate a full-depth acoustic impedance distribution map through the back-projection algorithm.

[0014] Furthermore, the inductance values of the multiple discrete inductors L are distributed in a geometric progression, satisfying:

[0015] L max / L min ≥10,

[0016] and the inductance switching interval time ≤ 10 ms.

[0017] Furthermore, the inductance value configuration of the switchable inductor group further includes:

[0018] Enable the resonant piezoelectric probe to have a high-frequency detection mode, a medium-frequency detection mode, and a low-frequency detection mode. When implementing detection, perform detection on the composite material to be detected in the high-frequency detection mode, medium-frequency detection mode, and low-frequency detection mode respectively; divide the composite material to be detected into three layers from top to bottom, and comprehensively process the detection data of the high-frequency detection, medium-frequency detection, and low-frequency detection to obtain the detection results of the upper, middle, and lower layers of the composite material.

[0019] Furthermore, the back-projection algorithm performs the following steps:

[0020] (1) Discretize the movement path of the resonant piezoelectric probe in the micro-probe frame to generate a detection area grid point set {x, y};

[0021] (2) Calculate the acoustic impedance contribution value for each grid point:

[0022] Z(x,y) = ∑[W i ·Z i ·exp(-α(f i )·d i )]

[0023] Where:

[0024] w i is the frequency weight factor;

[0025] α(f i) is the frequency-dependent attenuation coefficient;

[0026] d i is the acoustic wave propagation path length;

[0027] (3) Optimize the minimum objective function through iteration:

[0028] ||Z obs -Z cal || 2

[0029] Furthermore, the frequency weight factor w i has the following calculation formula:

[0030] W i = Elayer(f i ) / Elayer(f j )

[0031] where Elayer is the energy proportion of the current detection layer at frequency f j and is obtained through pre-scan calibration;

[0032] Furthermore, the calculation formula of the attenuation coefficient α(f i ) includes an anisotropy correction term:

[0033] α(f i ) = α0(f i ) + β · cos 2 θ

[0034] where:

[0035] θ is the angle between the acoustic wave propagation direction and the orientation of the current detection layer;

[0036] β is the material anisotropy factor;

[0037] Furthermore, the iterative optimization process introduces total variation regularization constraints:

[0038] Min(||Z obs -Z cal || 2 +λ · TV(Z cal ))

[0039] where λ = 0.1 - 0.5.

[0040] Furthermore, the detection method further includes:

[0041] The acoustic detection system is provided with an automatic impedance matching circuit for adjusting the output power in real time to maintain the resonant state; and a dynamic noise suppression unit that uses an adaptive filtering algorithm to eliminate environmental noise interference.

[0042] Compared with the prior art, the present application can achieve the following technical effects:

[0043] The new method for detecting defects in composite materials proposed by the present invention effectively improves the ability to detect defects in composite materials, especially the detection of micro-defects. The present invention can dynamically adjust the resonant frequency by inductance switching to achieve multi-frequency scanning and full-depth scanning; it adopts high, medium, and low detection modes for hierarchical detection, and can distinguish typical defects in composite materials such as pores, debonding, and delamination at one time. Furthermore, combined with the probe frame, it realizes high-stability and high-precision scanning detection and overcomes noise interference. By combining the back-projection algorithm to generate a full-depth acoustic impedance distribution map, it improves the accuracy of detecting result processing, enhances the visualization degree of the detection result, and facilitates the detection personnel to quickly obtain detection information and analysis. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is the basic circuit schematic diagram of the piezoelectric acoustic probe during resonance.

[0046] Figure 2 It is a schematic diagram of the process of a probe scanning and detecting inside the micro-probe frame.

[0047] Figure 3 It is a schematic diagram of the process of another probe scanning and detecting inside the micro-probe frame.

[0048] Figure 4 It is the basic flowchart of the detection method of the present invention.

[0049] In the figure:

[0050] 10 - Acoustic probe, 11 - Piezoelectric wafer, 12 - Discrete inductor;

[0051] 20 - Micro-probe frame, 21 - Moving path, 22 - Grid. Detailed Embodiments

[0052] To make the purpose, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Therefore, the detailed description of the embodiments of the present invention provided below is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention.

[0053] Existing composite material detection generally uses acoustic detection sensors. With the development and application of new composite materials, it has brought great challenges to non-destructive testing, especially in-situ detection of composite materials such as in-service aircraft. Currently, the common method is to use ultrasonic sensors for detection. Due to problems such as strong sound absorption, large thickness, and tiny defects to be detected in composite materials, the detection resolution of conventional ultrasonic detection with a single resonant frequency is insufficient, and it is difficult to accurately identify tiny defects and delamination defects.

[0054] A method for detecting composite material defects based on acoustic resonance imaging according to the present invention, the specific method includes:

[0055] Step 1:

[0056] Design a special probe structure, and use an acoustic probe 10 connected to an acoustic detection system to scan and detect the composite material to be detected. The acoustic probe 10 is a resonant piezoelectric probe, which is connected to the acoustic detection system to emit multi-frequency continuous resonant sound waves. The resonant piezoelectric probe includes:

[0057] A piezoelectric wafer 11, the polarization direction of which is perpendicular to the detection surface;

[0058] A switchable inductor group, including a plurality of discrete inductors 12, and the plurality of discrete inductors are connected in parallel with the piezoelectric wafer through a high-frequency relay.

[0059] Dynamically adjust the frequency by connecting different inductance values in parallel to achieve multi-frequency detection with a single probe. Through a multi-frequency layer scanning mechanism, bind the detection depth to the frequency, with high frequency corresponding to shallow layer detection and low frequency corresponding to deep layer detection. Perform multi-frequency scanning and full-depth scanning, and automatically match the detection level through inductor switching, breaking through the traditional single-chip penetration depth limit. Compared with traditional multi-probe arrays or mechanical tuning structures, there is no need for double-sided contact or coupling agent, simplifying the complexity and usage conditions of the detection equipment, and having a lower cost.

[0060] Step 2

[0061] Design the process of probe scanning and detection, including:

[0062] Set up a micro-probe frame 20, and place the resonant piezoelectric probe in the micro-probe frame for restricted space scanning; achieve high-precision scanning in a limited space, improve the detection accuracy. Furthermore, a moving path 21 can be designed in the set probe frame, as shown in the appendix Figure 2As shown in Fig. 3 or Fig. 4, a lateral or longitudinal step track is designed, and the probe can walk and scan along the track within the probe frame to ensure full coverage of the area to be detected and prevent missed detection. The high stability and high-precision scanning density within the frame can effectively improve the detection sensitivity. In particular, for the detection of in-service aviation composite materials, scanning under high gain conditions can be achieved to adapt to external noise.

[0063] Step Three

[0064] Detection signal processing and imaging, including:

[0065] When there are defects inside the composite material, it will change the load characteristics of the probe. The amplitude and phase angle of the detection signal are synchronously measured through a lock-in amplifier, and the resonant frequency offset is recorded in real time through a frequency tracking module. Based on the acquisition of multiple detection parameters, the internal condition of the composite material is dynamically monitored.

[0066] Classify the detection signals based on a preset defect determination rule:

[0067] When the phase angle of the detection signal shows a lag, mark the current signal as a pore defect signal. When there are multiple peaks in the detection signal, mark the current signal as a debonding defect signal. When the resonant frequency of the detection signal is significantly offset, mark the current signal as a delamination defect signal;

[0068] Furthermore, a full-depth acoustic impedance distribution map is generated through a back-projection algorithm. The acoustic impedance back-projection algorithm is optimized for the delamination detection characteristics of composite materials to achieve better imaging effects.

[0069] Furthermore, the inductance values of the multiple discrete inductors are distributed in a geometric progression, satisfying:

[0070] L max / L min ≥10,

[0071] The discrete inductor realizes flexible adjustment of the probe resonant frequency. The inductance value distribution in the form of a geometric progression enables relatively uniform frequency adjustment in different frequency bands. For composite materials with severe sound absorption and large thickness, more accurate detection can be carried out from different depth levels.

[0072] And the inductance switching interval time ≤ 10 ms. In the multi-frequency delamination scanning mechanism, different inductance values are quickly switched to achieve detection at different frequencies, improving the detection efficiency, which is very important for the detection of large-scale in-service aviation composite materials. And the detection of aviation composite materials generally starts after the aircraft lands and enters the airport. The composite material is in a dynamically changing environment from high temperature to cooling. Quick switching is required to ensure the continuity of the detection process.

[0073] In this embodiment, the inductance value configuration of the switchable inductor group further includes:

[0074] Enable the resonant piezoelectric probe to have high-frequency detection mode, medium-frequency detection mode, and low-frequency detection mode. During detection, perform high-frequency detection mode, medium-frequency detection mode, and low-frequency detection mode on the composite material to be detected respectively. Divide the composite material to be detected into three layers from top to bottom, and comprehensively process the detection data of high-frequency detection, medium-frequency detection, and low-frequency detection to obtain the detection results of the upper, middle, and lower layers of the composite material.

[0075] In this embodiment, before detecting the in-service aircraft composite material, the approximate thickness of each layer of the composite material to be detected can be determined according to the design data of the aircraft component, the actual usage situation, and the common defect distribution law. For example, when detecting the composite skin of the wing of a certain type of aircraft, the thickness of the upper layer is about 1-2 mm, the thickness of the middle layer is about 2-5 mm, and the thickness of the lower layer is more than 5 mm. High-frequency detection covers the upper layer, medium-frequency detection covers the middle layer, and low-frequency detection covers the lower layer. Configure the inductance according to the thickness of the layered detection and the propagation characteristics of sound waves with different frequencies in the aircraft composite material. Then, through the algorithm model, fuse, compare, and analyze the detection data of different frequencies to accurately obtain the detection results of each layer of the composite material.

[0076] In this embodiment, the specific execution steps of the back-projection algorithm are also disclosed, including:

[0077] (1) Discretize the movement path 21 of the resonant piezoelectric probe in the micro-probe frame 20 to generate different grids 22 and the grid point set {x, y} of the detection area. Each grid point can be used as an independent calculation unit to accurately obtain the detection signal within the detection area;

[0078] (2) Calculate the acoustic impedance contribution value for each grid point:

[0079] Z(x,y) = ∑[W i ·Z i ·exp(-α(f i )·d i )]

[0080] Where:

[0081] w i is the frequency weight factor; the frequency weight factor is used to adjust the influence degree of sound waves with different frequencies on the acoustic impedance contribution value, and the weight is allocated according to the actual detection requirements and the characteristics of different frequencies. For example, when paying more attention to the detection of shallow defects, the frequency weight factor corresponding to high frequency can be appropriately increased.

[0082] α(f i) is the frequency-dependent attenuation coefficient; the attenuation characteristics of sound waves at different frequencies are different during propagation.

[0083] d i is the sound wave propagation path length; that is, the path length that the sound wave travels from the probe, passes through the grid point, and then returns to the probe. The path length will affect the propagation time and energy attenuation of the sound wave.

[0084] By calculating the acoustic impedance contribution value of each grid point, the influence degree of the grid point on the entire detection signal can be quantified. Due to the differences in the sound wave propagation path and the characteristics of the surrounding materials of the grid points at different positions, their acoustic impedance contribution values will also be different. These values are calculated through the calculation model to provide basic data for the subsequent generation of the acoustic impedance distribution map.

[0085] (3) By iteratively optimizing the minimum objective function:

[0086] ||Z obs -Z cal || 2

[0087] Through continuous iterative optimization and adjusting the acoustic impedance values of the grid points, the finally generated acoustic impedance distribution map can more accurately reflect the true structure and defect conditions inside the composite material.

[0088] Further, the frequency weight factor w i has the following calculation formula:

[0089] W i = Elayer(f i ) / Elayer(f j )

[0090] where Elayer is the energy proportion of the current detection layer at frequency f j , which is obtained through pre-scan calibration; comprehensively considering the energy distribution of sound waves at multiple frequencies in the current detection layer to avoid relying on information of a single frequency.

[0091] Further, the calculation formula of the attenuation coefficient α(f i ) includes an anisotropy correction term:

[0092] α(f i ) = α0(f i ) + β·cos 2 θ

[0093] Where:

[0094] θ is the angle between the acoustic wave propagation direction and the orientation of the current detection layer; when θ = 0, the acoustic wave propagation direction is consistent with the detection layer orientation; when θ = 90°, the acoustic wave propagation direction is perpendicular to the detection layer orientation. Different angles will cause different microstructures encountered by the acoustic wave in the material, thus affecting the attenuation degree of the acoustic wave.

[0095] β is the material anisotropy factor;

[0096] Through anisotropy correction, for aerospace composite materials with regular or irregular fiber arrangements, the algorithm can accurately calculate according to the actual anisotropy degree of the material (reflected by β) and the acoustic wave propagation direction (reflected by θ), improving the generality and reliability of the detection method.

[0097] Further, the iterative optimization process introduces total variation regularization constraint:

[0098] Min(||Z obs -Z cal || 2 +λ·TV(Z cal ))

[0099] where λ = 0.1 - 0.5.

[0100] Through the total variation regularization constraint, when detecting aerospace composite materials, the influence of noise and artifacts that may exist in the detection signal due to the complexity of the detection environment, the inhomogeneity of the material internal structure, etc. is overcome.

[0101] Further, the detection method further includes:

[0102] The acoustic detection system is provided with an automatic impedance matching circuit and a dynamic noise suppression unit; the automatic impedance matching circuit is used to adjust the output power in real time to maintain the resonant state; the dynamic noise suppression unit uses an adaptive filtering algorithm to eliminate environmental noise interference. Thereby improving the sensitivity and accuracy of the detection.

[0103] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A composite material defect detection method based on acoustic resonance imaging, using an acoustic probe connected to an acoustic detection system to scan and detect the composite material to be inspected, characterized in that: The acoustic probe is a resonant piezoelectric probe, which is connected to the acoustic detection system to emit multi-frequency continuous resonant sound waves. The resonant piezoelectric probe includes: A piezoelectric chip, whose polarization direction is perpendicular to the detection surface; A switchable inductor group includes a plurality of discrete inductors, wherein the plurality of discrete inductors are connected in parallel with the piezoelectric wafer via a high-frequency relay; The scanning and detection process also includes: Setting a micro-probe frame, placing the resonant piezoelectric probe in the micro-probe frame to perform confined space scanning; Detection signal processing and imaging, including: The amplitude and phase angle of the detection signal are synchronously measured through a lock-in amplifier, and the resonant frequency offset is recorded in real time through a frequency tracking module; Classify the detection signals based on the preset defect judgment rules: When the phase angle of the detection signal lags, the current signal is marked as a pore defect signal. When the detection signal has multiple peaks, the current signal is marked as a debonding defect signal. When the resonant frequency of the detection signal is significantly offset, the current signal is marked as a delamination defect signal. The full-depth acoustic impedance distribution map is generated by the back-projection algorithm.

2. A composite material defect detection method based on acoustic resonance imaging according to claim 1, characterized in that: The inductance values ​​of the plurality of discrete inductors are distributed in a geometric progression, satisfying: L max / L min ≥10, And the inductor switching interval is ≤10ms.

3. A composite material defect detection method based on acoustic resonance imaging according to claim 2, characterized in that: The inductance value configuration of the switchable inductor group further includes: The resonant piezoelectric probe has a high-frequency detection mode, a medium-frequency detection mode and a low-frequency detection mode. When the detection is carried out, the high-frequency detection mode, the medium-frequency detection mode and the low-frequency detection mode are respectively implemented on the detected composite material for detection; the detected composite material is divided into three layers from top to bottom, and the detection data of the high-frequency detection, the medium-frequency detection and the low-frequency detection are comprehensively processed to obtain the detection results of the upper, middle and lower layers of the composite material.

4. The composite material defect detection method based on acoustic resonance imaging according to claim 3 is characterized in that: The back-projection algorithm performs the following steps: (1) discretizing the moving path of the resonant piezoelectric probe within the microprobe frame to generate a detection area grid point set {x, y}; (2) Calculate the acoustic impedance contribution value for each grid point: Z(x,y)=∑[W i ·Z i ·exp(-α(f i )·d i )] in: w i is the frequency weight factor; α(f i ) is the frequency-dependent attenuation coefficient; d i is the length of the sound wave propagation path; (3) Optimize the minimum objective function through iteration: ||From obs -WITH cal || 2 。 5. The composite material defect detection method based on acoustic resonance imaging according to claim 4, characterized in that: The frequency weight factor w i The calculation formula is: Wi=Elayer(fi) / Elayer(fj) Where Elayer is the current detection layer at frequency f j The energy proportion under the condition is obtained through pre-scan calibration.

6. The composite material defect detection method based on acoustic resonance imaging according to claim 4, characterized in that: The attenuation coefficient α(f i ) includes an anisotropy correction term: a(f i )=α0(f i )+β·cos 2 i in: θ is the angle between the direction of sound wave propagation and the orientation of the current detection layer; β is the material anisotropy factor.

7. The composite material defect detection method based on acoustic resonance imaging according to claim 4, characterized in that: The iterative optimization process introduces a total variation regularization constraint: Min(||Z obs -WITH cal || 2 +λ TV(Z cal )) Among them, λ=0.1~0.

5.

8. A composite material defect detection method based on acoustic resonance imaging according to any one of claims 5 to 7, characterized in that: Also includes: The acoustic detection system is provided with an automatic impedance matching circuit for adjusting the output power in real time to maintain a resonant state; and a dynamic noise suppression unit that uses an adaptive filtering algorithm to eliminate environmental noise interference.

Images