A microwave detection method for delamination defects in composite materials coupled with infrared thermography

Abstract

This invention discloses a microwave detection method for delamination defects in composite materials coupled with infrared thermography. It utilizes image enhancement algorithms to process initial infrared image sequence data, improving the contrast between defective and non-defective regions to obtain an enhanced and denoised infrared image sequence. From this sequence, a three-dimensional temperature matrix is ​​derived. Based on the new defect depth matrix obtained by the microwave detection method, a geometric model for three-dimensional heat transfer simulation is established. The heat flux density, ambient temperature, and other relevant parameters during infrared detection are used as boundary conditions for the three-dimensional heat transfer simulation, resulting in a simulated surface temperature matrix. The average error between the measured temperature matrix and the simulated temperature matrix within the defect edge range is compared to see if it is less than a given error, thus achieving defect detection. This invention uses infrared thermography temperature data to correct the defect edges detected by microwave detection, enabling the precise detection of the contour and location of internal defects in composite materials, and to some extent solving the problem of inaccurate defect contour identification in microwave detection methods.

Description

Technical Field

[0001] This invention relates to microwave and infrared thermography coupled detection technology, specifically to a microwave detection method for delamination defects in composite materials coupled with infrared thermography. Background Technology

[0002] Infrared thermography is a rapidly developing and promising non-destructive testing (NDT) technology in recent years. Based on the principle of infrared radiation, it uses an infrared thermal imager to collect infrared radiation from the surface of the object being inspected, records the temperature distribution changes on the surface, and extracts temporal and regional temperature anomalies to detect surface or internal defects. Microwave NDT of composite materials is an emerging technology. Its basic principle is to comprehensively utilize the interaction between microwaves and matter. On the one hand, microwaves will produce reflection, scattering, and transmission at discontinuous interfaces; on the other hand, microwaves can also interact with the material being inspected. The microwave field is affected by the electromagnetic and geometric parameters of the material. By measuring the changes in the basic parameters of the microwave signal, the echo coefficient is obtained, and then the internal damage of the material can be inverted.

[0003] Both detection methods have their own technical advantages and disadvantages. Firstly, infrared thermography is a non-contact method, causing no damage to the internal structure of the tested part and offering a high level of safety for the inspectors. It also boasts high sensitivity, a large detection area, and the ability to perform comprehensive surface and internal inspections. However, its detection depth is limited, and the deeper the defect, the larger the detectable defect size, requiring higher excitation power. Secondly, microwave detection can couple the antenna to the material through air, eliminating the need for a coupling agent like in ultrasonic testing. It can detect hidden internal defects down to the millimeter level, offering high sensitivity and the ability to pinpoint defect location. Furthermore, the entire microwave detection system is compact and robust, making it easy to mount on a vehicle. However, post-processing issues mean that the actual defect range in microwave detection results is affected by the waveguide antenna size, making it impossible to precisely locate the defect area.

[0004] Microwave nondestructive testing (NDT) technology offers good resolution for defect depth detection, but the complex internal structure of composite materials makes the echo coefficient highly susceptible to interference, leading to inaccurate defect location identification. Furthermore, the width of the waveguide itself can affect the identification of the defect range, causing discrepancies between the identified defect contour and the actual defect. Infrared detection, while offering low resolution for defect depth detection, cannot accurately determine the depth and contour information of defects. However, surface temperature measurement is precise and rapid, facilitating quick preliminary defect localization. Therefore, this invention provides an edge correction method for microwave inspection results of composite materials that combines infrared thermography data. By using temperature data from infrared thermography to correct the defect edges detected by microwave inspection, this method can accurately detect the contour and location of internal defects in composite materials, thus addressing to some extent the problem of inaccurate defect contour identification in microwave inspection methods. Summary of the Invention

[0005] Purpose of the invention: This invention proposes a microwave detection method for delamination defects in composite materials coupled with infrared thermography, which solves the problems of inaccurate defect location identification and deviation between defect contour identification and actual defects caused by the complex internal structure of composite materials and the susceptibility of echo coefficients to interference.

[0006] Technical solution: This invention provides a microwave detection method for delamination defects in composite materials coupled with infrared thermography, comprising the following steps:

[0007] Step 1: Obtain the echo signal s(x,y,f) by scanning the composite material using a standard waveguide antenna for microwave testing;

[0008] Step 2: Starting from the three-dimensional matrix s(x,y,f), use the frequency sweep signal result s at a single coordinate position (x,y) x,y (f) Using this as input, the time-domain reflectance coefficient curves D at various locations in the composite material are obtained through inverse Fourier transform. x,y (t);

[0009] Step 3: Obtain the defect depth h(x,y) inside the composite material;

[0010] Step 4: When the defect edge is located inside the waveguide port, based on the minimum defect size l x_min l y_min , will l x_min ×l y_min Set as the initial defect range l x ×l y All positions outside the range are marked as 0, resulting in a new matrix H(x,y);

[0011] Step 5: Heat the composite material specimen using a heat source and acquire the initial infrared image sequence using an infrared thermal imager;

[0012] Step 6: Process the initial infrared image sequence data using an image enhancement algorithm to improve the contrast between defective and non-defective areas, obtain the enhanced and denoised infrared image sequence, and derive the three-dimensional temperature matrix T from the sequence. h (x,y,t), where (x,y) are the pixel coordinates of the temperature change, and t represents the time series;

[0013] Step 7: Establish the geometric model for three-dimensional heat transfer simulation;

[0014] Step 8: Delineate the defect edge range described in Step 4 from the pixel coordinate positions in Step 6, i.e., l x_min <x<l x_max l y_min <y<l y_maxTo obtain the total number of coordinates n within this range. pixel Thus, the total number of temperature samples within this range, n = n t ×n pixel , where n t The number of time samples is used to compare the average error between the measured temperature matrix and the simulated temperature matrix within the defect edge range to see if it is less than a given error δ.

[0015] Step 9: If the average error is not less than the given error δ, return to step 4 to expand the defect range l x ×l y Correct the depth matrix H(x,y) and recalculate the comparison between steps 7 and 8.

[0016] Step 10: If the average error is less than the given error δ, the three-dimensional geometric model can be regarded as a three-dimensional image of the target defect.

[0017] Furthermore, the echo signal s(x,y,f) mentioned in step 1 is a complex signal, where x and y represent the coordinate values ​​of the horizontal and vertical scanning dimensions during microwave frequency sweeping, respectively. Its resolution is controlled by a stepper motor, and the step spacing does not exceed the short side dimension of the waveguide aperture. f represents the different frequencies of the frequency sweep.

[0018] Furthermore, the implementation process of step 3 is as follows:

[0019] Based on the principle of microwave reflection, the time-domain reflection coefficient D of a defect at a certain location... x,y (t) Compared to the defect-free case, its second reflection intensity peak point Compared to It will occur earlier, and at the same time, there will be an additional third peak point of reflection intensity. Furthermore, based on the propagation speed of microwaves in air and inside the composite material, the defect depth h(x,y) inside the composite material can be preliminarily obtained.

[0020] Furthermore, step 7 is implemented as follows:

[0021] A geometric model for three-dimensional heat transfer simulation is established using the new defect depth matrix H(x,y) from step 4. The heat flux density, ambient temperature, and other relevant parameters during infrared detection in step 5 are used as boundary conditions for the three-dimensional heat transfer simulation, resulting in the surface simulation temperature matrix T. m (x,y,t), where the coordinates (x,y) here correspond one-to-one with the pixel coordinates in step 6.

[0022] Furthermore, step 8 is achieved through the following formula:

[0023]

[0024] Where the subscript j represents the summation of data in the spatial dimension, and i represents the summation of data in the time dimension.

[0025] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes to couple infrared detection technology with microwave detection technology, and use the temperature data of infrared thermography to correct the defect edge of microwave detection. This can be used to detect the precise contour and location of internal defects in composite materials, and to a certain extent solves the problem of inaccurate defect contour identification when the waveguide itself crosses the defect contour edge. Attached Figure Description

[0026] Figure 1 This is a flowchart of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the principle of microwave reflection.

[0028] Figure 3 This is a schematic diagram of defect edge detection;

[0029] Figure 4 This is a schematic diagram of three-dimensional heat conduction. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the accompanying drawings.

[0031] This invention provides a microwave detection method for delamination defects in composite materials coupled with infrared thermography, such as... Figure 1 As shown, it includes the following steps:

[0032] Step 1: Obtain the echo signal s(x,y,f) by scanning the composite material using a standard waveguide antenna for microwave testing. The s(x,y,f) is a complex signal, where x and y represent the coordinates of the horizontal and vertical scanning dimensions during microwave frequency sweeping, respectively. The resolution is controlled by a stepper motor, and the step spacing generally does not exceed the short side dimension of the waveguide aperture. f represents the different frequencies of the frequency sweep.

[0033] Step 2: Starting from the three-dimensional matrix s(x,y,f), use the frequency sweep signal result s at a single coordinate position (x,y) x,y (f) Using this as input, the time-domain reflectance coefficient curves D at various locations in the composite material are obtained using IFFT (Inverse Fourier Transform). x,y (t):

[0034]

[0035] Step 3: Based on the principle of microwave reflection, such as... Figure 2As shown in the figure, t represents the peak time of the reflected signal intensity; the upper right subscript d indicates the presence of a defect, and f indicates the absence of a defect; the lower right subscript 0 represents the signal transmission time, and the lower right subscripts 1, 2, and 3 represent the time points when the 1st, 2nd, and 3rd signal intensity peaks occur, respectively. When the signal is transmitted to the medium interface, reflection and refraction occur. Due to the presence of the defective layered interface, additional reflected signal peaks are generated. The time-domain reflection coefficient D of a defective location is shown. x,y (t) Compared to the defect-free case, its second reflection intensity peak point Compared to It will occur earlier, and at the same time, there will be an additional third peak point of reflection intensity. Furthermore, based on the propagation speed of microwaves in air and inside the composite material, the defect depth h(x,y) inside the composite material can be preliminarily obtained.

[0036] Step 4: As Figure 3 As shown, because the waveguide antenna used for microwave detection has its own dimensions, when the defect edge is located inside the waveguide port, the obtained echo signal s(x,y,f) will change with the movement of the waveguide port, making it impossible to accurately identify the defect edge. However, the minimum defect size l can be determined based on this. x_min l y_min . l x_min ×l y_min Set as the initial defect range l x ×l y All positions outside the range are marked as 0, resulting in a new matrix H(x,y).

[0037] Step 5: Heat the composite material specimen using a heat source and acquire the initial infrared image sequence using an infrared thermal imager.

[0038] Step 6: Process the initial infrared image sequence data using an image enhancement algorithm to improve the contrast between defective and non-defective areas, obtain the enhanced and denoised infrared image sequence, and derive the three-dimensional temperature matrix T from the sequence. h (x,y,t), where (x,y) are the pixel coordinates of the temperature change, and t represents the time series.

[0039] Step 7: Using the new defect depth matrix H(x,y) from Step 4, establish the geometric model for the 3D heat transfer simulation as a cuboid V(a,b,L), where a, b, and L represent the three sides of the model, respectively; the geometric model for the internal defects is also a cuboid V. d (l x ,l y ,D), l x l yLet D and E represent the three sides of the defect, respectively. Using the heat flux density q, ambient temperature T0, and other relevant parameters from the infrared detection in step 5 as boundary conditions for the three-dimensional heat transfer simulation, a transient numerical simulation of the heat transfer process is performed, such as... Figure 4 As shown, L is the height of the specimen material, h is the depth of the upper surface of the defect from the surface of the specimen, and D is the height of the defect itself, thus obtaining the surface simulation temperature matrix T. m (x,y,t), where the coordinates (x,y) here correspond one-to-one with the pixel coordinates in step 6.

[0040] Step 8: Delineate the defect edge range described in Step 4 (i.e., l) from the pixel coordinate positions in Step 6. x_min <x<l x_max l y_min <y<l y_max ), thus obtaining the total number of coordinates n within this range. pixel Thus, the total number of temperature samples within this range, n = n t ×n pixel , where n t The number of time samples. Compare the average error between the measured temperature matrix and the simulated temperature matrix within the defect edge region to see if it is less than a given error δ:

[0041]

[0042] Where the subscript j represents the summation of data in the spatial dimension, and i represents the summation of data in the time dimension.

[0043] Step 9: If the average error is not less than the given error δ, return to step 4 and increase the defect range l. x ×l y Correct the depth matrix H(x,y) and repeat the comparison calculation of steps 7 and 8.

[0044] Step 10: If the average error is less than the given error δ, the three-dimensional geometric model can be regarded as a three-dimensional image of the target defect.

[0045] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A microwave detection method for delamination defects in composite materials coupled with infrared thermography, characterized in that, Includes the following steps: Step 1: Obtain the echo signal by scanning the composite material using a standard waveguide antenna for microwave testing. ; Step 2: From the three-dimensional matrix Starting from a single coordinate position (x s , y s The results of the frequency sweep signal. As input, the time-domain reflectance coefficient curves at various locations in the composite material are obtained using inverse Fourier transform. ; Step 3: Obtain the depth of defects inside the composite material ; Step 4: When the defect edge is located inside the waveguide port, based on the minimum defect size... , ,Will Set as initial defect range All locations outside the range Marked as 0, a new defect depth matrix is ​​obtained. ; Step 5: Heat the composite material specimen using a heat source and acquire the initial infrared image sequence using an infrared thermal imager; Step 6: Process the initial infrared image sequence data using an image enhancement algorithm to improve the contrast between defective and non-defective areas, obtain the enhanced and denoised infrared image sequence, and derive the three-dimensional temperature matrix from this sequence. , where (x T ,y T () represents the pixel coordinates of the temperature change, and t represents the time series; Step 7: Establish the geometric model for three-dimensional heat transfer simulation; Step 8: Delineate the defect edge range described in Step 4 from the pixel coordinate positions in Step 6, i.e. , To obtain the total number of coordinates within this range. This allows us to obtain the total number of temperature samples within that range. ,in The time sample size is used to compare the average error between the measured temperature matrix and the simulated temperature matrix within the defect edge region to see if it is less than a given error. ; Step 9: If the average error is not less than the given error Return to step 4 to expand the defect area. Correct the depth matrix Then, repeat the comparison calculations in steps 7 and 8. Step 10: If the average error is less than the given error At this point, the three-dimensional geometric model can be regarded as a three-dimensional image of the target defect; The process for step 3 is as follows: Based on the principle of microwave reflection, the time-domain reflection coefficient of a defect at a certain location... Compared to the defect-free case, its second reflection intensity peak point Compared to It will occur earlier, and at the same time, there will be an additional third peak point of reflection intensity. Furthermore, based on the propagation speed of microwaves in air and inside the composite material, the depth of defects inside the composite material can be preliminarily determined. ; The process for step 7 is as follows: Using the new defect depth matrix in step 4 A geometric model for three-dimensional heat transfer simulation is established. The heat flux density, ambient temperature, and other relevant parameters during infrared detection in step 5 are used as boundary conditions for the three-dimensional heat transfer simulation to obtain the surface simulation temperature matrix. The coordinates here (x) T , y T The pixel coordinates in step 6 correspond one-to-one with the pixel coordinates in step 6. Step 8 is achieved through the following formula: ； Where the subscript j represents the summation of data in the spatial dimension, and i represents the summation of data in the time dimension.

2. The microwave detection method for delamination defects in composite materials coupled with infrared thermography according to claim 1, characterized in that, The echo signal described in step 1 For a complex signal, x s and y s These represent the coordinate values ​​of the horizontal and vertical scanning dimensions during microwave frequency sweeping. The resolution is controlled by a stepper motor, and the step spacing does not exceed the short side dimension of the waveguide aperture. f represents the different frequencies of the frequency sweep.

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