A method for detecting the depth of internal defects in composite materials based on frequency-modulated infrared thermal imaging

Abstract

The present invention discloses a method for detecting the depth of internal defects in composite materials based on frequency-modulated infrared thermal imaging. The method comprises the following steps: Step 1: preparing a calibration specimen; Step 2: obtaining the AC component of the surface temperature response of the calibration specimen; Step 3: determining the phase difference variation curve with respect to frequency based on the thermal image sequence; Step 4: establishing the relationship between the sensitive frequency and the defect depth; Step 5: evaluating the defect depth based on the sensitive frequency measurement value. The method obtains a material surface temperature image through infrared thermal wave radar technology and extracts the temperature phase, thereby avoiding the influence of uneven heating and uneven emissivity of the material surface on the test results, and further extracts the sensitive frequency from the temperature phase to detect the defect depth. The sensitive frequency used by the present invention for detecting the defect depth is easier to obtain accurately than the traditional blind frequency, and the detection range is also larger than the delay time and cross-correlation phase, and has the characteristics of high measurement accuracy and wide detection range.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of nondestructive testing, and relates to a defect depth detection method based on frequency-modulated infrared thermal imaging, in particular to a method for evaluating defect depth by extracting sensitive frequency in phase difference curve. BACKGROUND

[0002] Infrared thermal wave radar (TWR) is a new type of infrared thermal imaging nondestructive testing technology. The technology heats the measured material by applying a frequency-modulated thermal excitation, and measures the surface temperature of the material using an infrared thermal imager to realize nondestructive testing of internal defects of the material. Traditional infrared thermal imaging methods mainly include pulse method and phase-locked method. The pulse method detects defects by exciting the measured material with a high-energy light pulse and analyzing the cooling curve, and the phase-locked method detects defects by exciting with a periodic sinusoidal wave and extracting the phase information of the thermal image. TWR uses frequency modulation to generate a wide-band thermal excitation signal, overcoming the limitations of traditional techniques in the distinguishability of defects at different depths and the detection range.

[0003] At present, the depth of internal defects of a material is detected based on infrared thermal imaging technology. The conventional method is to extract a characteristic parameter in an infrared thermal image sequence, establish a corresponding relationship between the characteristic parameter and the depth, and identify the defect depth by measuring the characteristic parameter. At present, it is common to extract a characteristic time from a temperature change curve or a blind frequency from a phase difference change curve as a characteristic parameter. However, the surface temperature is easily affected by factors such as non-uniform heating of the material surface and uneven distribution of the emissivity, and on the other hand, the extraction of the characteristic time from the temperature curve requires the calculation of the derivative, resulting in that the defect depth detection is greatly affected by noise. The phase in the frequency domain has higher anti-interference ability than the temperature in the time domain, because the phase is less sensitive to non-uniform heating and uneven distribution of surface emissivity. However, under the influence of measurement noise, it is also difficult to extract the blind frequency from the phase difference change curve, ultimately resulting in a decrease in the detection accuracy of the defect depth. For TWR, the excitation signal and the response signal can also be cross-correlated, and the delay time and the cross-correlation phase can be extracted to detect the defect depth. However, the correlation between the excitation and the response gradually weakens as the thermal wave diffusion depth increases, resulting in that the delay time and the cross-correlation phase can only detect defects within a limited depth range, which has great limitations. SUMMARY

[0004] In view of the above problems existing in the prior art, the present application provides a method for detecting the internal defect depth of a composite material based on frequency-modulated infrared thermal imaging. The method obtains a material surface temperature image through infrared thermal wave radar technology and extracts a temperature phase, thereby avoiding the influence of uneven heating and uneven emissivity of the material surface on the test results, and further extracting a sensitive frequency from the temperature phase to detect the defect depth. The sensitive frequency used by the present application for detecting the defect depth is more easily and accurately obtained than the traditional blind frequency, and the detection range is also larger than the delay time and the cross-correlation phase, and the present application has the characteristics of high measurement accuracy and wide detection range, and is suitable for detecting the internal delamination defect depth of a composite material.

[0005] The object of the present application is achieved by the following technical solutions:

[0006] A method for detecting the internal defect depth of a composite material based on frequency-modulated infrared thermal imaging, comprising the following steps:

[0007] Step 1: preparing a calibration specimen;

[0008] Step 2: obtaining the surface temperature response alternating current component of the calibration specimen;

[0009] Step 3: determining the phase difference change curve about the frequency according to the thermal image sequence;

[0010] Step 4: establishing the relationship between the sensitive frequency and the defect depth;

[0011] Step 5: evaluating the defect depth according to the sensitive frequency measurement value.

[0012] Compared with the prior art, the present application has the following advantages:

[0013] 1. Compared with the currently commonly used characteristic time method, the present application does not need to derive the original temperature data, thereby reducing the influence of measurement noise on the detection results; the defect depth is identified by extracting the phase information, and is not easily affected by the non-uniform heating of the specimen surface and the uneven distribution of the emissivity.

[0014] 2. Compared with the commonly used method for detecting the defect depth based on the infrared thermal wave radar technology, the present application has a larger detection range, and there is a linear relationship between the sensitive frequency and the defect depth, which is easy to calibrate; the influence of the defect size on the sensitive frequency is considered, and the detection error caused by the inconsistency of the defect size between the calibration specimen and the specimen to be tested is corrected. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is an infrared thermal wave radar nondestructive testing experimental schematic diagram;

[0016] Figure 2 It is an experimental sample schematic diagram (unit: mm);

[0017] Figure 3 To smooth the phase difference change curve before and after processing;

[0018] Figure 4 To the phase difference about the change curve of frequency;

[0019] Figure 5 To the defect depth detection feasibility verification. DETAILED DESCRIPTION

[0020] The technical solutions of the present application are further described below in conjunction with the drawings, but are not limited thereto, and any modification or equivalent replacement of the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application shall be covered in the protection scope of the present application.

[0021] The present application provides a method for detecting the internal defect depth of composite materials based on frequency-modulated infrared thermal imaging, which measures the depth of internal defects of materials based on infrared thermal wave radar technology, and its main feature is to measure the phase change of the surface temperature of the test piece, obtain the phase information at the defect by using image processing technology, and determine the sensitive frequency. There is a one-to-one correspondence between the sensitive frequency and the defect depth. The specific steps for detecting the defect depth of the target test piece are as follows:

[0022] Step 1: Prepare a calibration test piece.

[0023] Step 11: Pre-detect the test piece to be tested by using infrared thermal wave radar technology, determine the position and shape of the defect according to the collected thermal image, and identify the defect size by using a general method.

[0024] Step 12: The calibration test piece is the same as the test piece to be tested, and contains multiple blind holes with different sizes and depths, wherein the blind hole shape is similar to (not required to be completely consistent with) the defect of the test piece to be tested, and the adjacent blind holes are kept at a sufficient distance to ensure that they do not interfere with each other during the measurement process.

[0025] Step 13: Uniformly spray the surface of the calibration test piece with appropriate black paint.

[0026] Step 2: Obtain the surface temperature response alternating current component of the calibration test piece.

[0027] Step 21: Apply a frequency-modulated thermal excitation to the surface of the calibration test piece, as shown in the following formula:

[0028] q(t)=A(1+sin(2πft)) (1)

[0029] In the formula, q is the heat flow value of the excitation, A is the heat flow amplitude, f is the frequency, and the following formula is satisfied:

[0030]

[0031] where f1 and f2 are the start frequency and end frequency respectively, T is the loading time s Increasing the sampling frequency of thermal image sequence (usually higher than 30 Hz) and prolonging the heating time (usually more than 10 seconds) can improve the detection accuracy.

[0032] Step 22: The measured temperature generally contains both DC and AC components, but the DC component will interfere with the phase extraction. To remove the DC component, after the calibration sample is subjected to the excitation as shown in equation (1), it is cooled to the initial temperature, and then the following thermal excitation waveform with opposite phase is loaded:

[0033] q p (t) = A (1 + sin (2πft + π)) (3)

[0034] Step 23: If the temperature of a certain point on the surface of the calibration sample under the thermal excitation shown in equations (1) and (3) is T1 and T2 respectively, then the AC component of the point is:

[0035] T ac = (T1-T2) / 2 (4)

[0036] When applying the thermal excitation shown in equations (1) and (3), the infrared thermal imager needs to be synchronized to perform thermal image acquisition to ensure phase consistency.

[0037] Step 3: Determine the phase difference curve with respect to frequency based on the thermal image sequence.

[0038] Step 31: Substitute the temperature AC components calculated in step 2 into equations (5) and (6) respectively to obtain the in-phase correlation function and quadrature correlation function at a specific frequency f t :

[0039]

[0040]

[0041] where N is the total number of thermal images, f t ∈ [f1, f2], R(n) and Q(n) represent the in-phase reference signal and the quadrature reference signal respectively, and satisfy the following form:

[0042]

[0043]

[0044] where f samp is the sampling frequency.

[0045] Step 32: Calculate the phase of the AC component using the in-phase correlation function and the quadrature correlation function:

[0046]

[0047] Step 33: Extract the temperature AC component of several pixel points in the defect area and the reference area respectively, calculate the corresponding phase value according to formula (5)-(9), and calculate the phase difference:

[0048] ΔΦ = Φ s - Φ d (10)

[0049] In the formula, ΔΦ is the phase difference, Φ d and Φ s are the phase values calculated according to the pixel points in the defect area and the reference area respectively.

[0050] Step 34: Process the temperature data of each defect area on the surface of the calibration test piece according to steps 31-33, and obtain the change curve of the phase difference ΔΦ about the frequency f t corresponding to defects of different sizes and depths.

[0051] Step 35: The phase difference about the frequency change curve has oscillation phenomenon, so Loess formula is used to smooth the curve. Loess formula is a common non-parametric regression smoothing method, which principle is to weight each data point in the curve, and use the nearby data to locally fit, so as to obtain the smooth curve.

[0052] Step 4: Establish the relationship between sensitive frequency and defect depth.

[0053] Step 41: Define the frequency corresponding to the maximum phase difference ΔΦ as the sensitive frequency, denoted as f s , and the sensitive frequency f s and the defect depth satisfy the following relationship:

[0054]

[0055] In the formula, C1 and C2 are both undetermined coefficients, D is the defect size, and H is the defect depth (the distance between the defect plane and the measured surface of the test piece).

[0056] Step 42: According to the phase difference change curve of each defect area on the surface of the calibration test piece relative to the reference area, the corresponding sensitive frequency is obtained.

[0057] Step 43: According to the sensitive frequency f s , size D and depth H of the defect, draw the fitting curve of f s and 1 / (D·H 2 ), and determine the coefficients C1 and C2.

[0058] Step 5: Evaluate the defect depth according to the sensitive frequency measurement value.

[0059] Step 51: Apply the same thermal excitation as the calibration specimen to the test piece, extract the temperature AC components of the defect area of ​​the test piece according to step 2, and obtain a phase difference frequency curve based on the temperature AC components according to step 3 to determine the sensitive frequency of the defect area of ​​the test piece;

[0060] Step 52: Substitute the sensitive frequency and defect size into formula (11) to calculate the defect depth.

[0061] Example:

[0062] This embodiment uses infrared thermal wave radar technology to perform pre-test on the test piece, such as Figure 1 As shown, the experimental device mainly includes an infrared thermal image acquisition system and a light excitation system. A signal generator or a data acquisition card is used to control the light excitation system and the infrared thermal image acquisition system so that the light excitation signal output and the thermal image acquisition are synchronized to ensure the phase consistency between the excitation and the response in multiple experiments. Before applying the method described in the present invention, the test piece should be pre-tested to determine the location, shape and size of the internal defects of the test piece. According to the size and shape of the defect to be tested, the calibration test piece is designed. Taking the internal defect depth detection of alumina ceramics as an example, the specimen size is 150mm×40mm×5mm, and a circular flat-bottomed blind hole is used to approximate the internal defect. There are 14 flat-bottomed blind holes in the specimen, of which the diameter of defects #1 to #7 is 10mm, the diameter of defects #8 to #14 is 8mm, and the defect depth is as follows Figure 2 As shown in Figure 1, 10 flat-bottomed blind holes were randomly selected to calibrate the relationship between the sensitive frequency and the defect depth.

[0063] The specimen was mounted 25 cm from the infrared thermal imager, with the surface to be tested perpendicular to the infrared thermal imager lens and the specimen located in the center of the infrared thermal imager's field of view. Laser excitation was applied to the specimen with a wavelength of 808 nm, an angle of approximately 15° between the laser irradiation direction and the perpendicular direction of the specimen surface, and a heating time of 100 s. First, the optical excitation system was controlled to generate a frequency-modulated excitation signal as shown in Equation (1), where the frequency modulation starting frequency f1 and ending frequency f2 were 0.001 Hz and 1 Hz, respectively, and the amplitude A was 17.18 A (the laser energy was controlled by adjusting the current). A sequence of infrared thermal images of the specimen surface was recorded. After the specimen cooled to room temperature, an antiphase excitation signal as shown in Equation (3) was applied, and a sequence of infrared thermal images of the specimen surface was recorded. Background subtraction was first performed on the thermal image, i.e., the initial temperature was uniformly subtracted. The defect area and reference area in the thermal image were then determined. The temperature data of the pixels in the defect area and reference area were extracted, and the average temperature was calculated. The thermal image sequences obtained under the two previous excitations are processed in the same way, and the temperature AC component is calculated using formula (4).

[0064] After the temperature AC components of each defect region (a total of 10) and the reference region are sequentially obtained, the phase values of each AC component in the frequency range [0.001, 0.1] are calculated using formulas (5)-(9). The phase difference between each defect region and the reference region is calculated according to formula (10), and the Loess formula is used to smooth the curve. The original phase difference curve and the smoothed phase difference curve are shown in Figure 3 .

[0065] As shown in Figure 3 , the smoothed phase difference curve has a change trend of first increasing and then decreasing, and there is an extreme point, and the frequency value corresponding to the point is the sensitive frequency. Figure 4 The phase difference curves corresponding to defects of different depths are given. As shown in Figure 4 , the defect size and depth can change the sensitive frequency, and for defects of the same size, the phase difference extreme value and the sensitive frequency gradually decrease with the increase of the defect depth. In the experiment, the sensitive frequencies f s of defects #1, #4, #5, #6, #7, #8, #9, #12, #13 and #14 are extracted, and according to the size and depth of the defects, f s is plotted. 2 The linear fitting curve of 1 / (D·H Figure 5 ) is shown in . The R2 value obtained by linear fitting is 0.96209, indicating that the fitting curve has a high degree of coincidence with the actual data. According to the linear fitting curve, the undetermined coefficient in formula (11) can be obtained.

[0066] In addition to the defects used for calibration, two defects (#2 and #10) are selected from the remaining defects for actual measurement, and the calibration results in Figure 5 are verified. The temperature AC components of each defect region are extracted, and the phase difference-frequency change curves are sequentially obtained according to the temperature AC components, and the sensitive frequency corresponding to each defect is determined according to the extreme point. According to the fitting relationship in Figure 5 , the defect depth is calculated from the sensitive frequency and the defect size. The measured results of the defect depth are also plotted in Figure 5 , and are represented by blue triangles. The results show that the measured values of the defect depth are very close to the true values. The true depths of defects #2 and #10 are 1.1 mm and 1.2 mm, respectively, and the measured values according to the method of the present application are 1.142 mm and 1.23 mm, respectively, with relative errors of 3.8% and 2.5%, respectively, and the detection accuracy is high.

Claims

1. A method for detecting the depth of internal defects in composite materials based on frequency-modulated infrared thermal imaging, characterized in that The method comprises the following steps: Step 1: Prepare the calibration specimen; Step 2: Obtain the AC component of the calibration specimen surface temperature response: Step 21: Apply frequency-modulated thermal excitation to the surface of the calibration specimen: （1） Where, is the excitation heat flow value, is the heat flow amplitude, f is the frequency; Step 22: After the calibration specimen is stimulated as shown in formula (1), it is cooled to the initial temperature and then loaded with the following thermal stimulation waveform with opposite phase: （2） Step 23: If the temperature at a point on the surface of the calibration specimen is and , then the AC component at this point is: （3） Step 3: Determine the phase difference curve with respect to frequency based on the thermal image sequence: Step 31: Substitute the temperature AC component calculated in step 2 into equations (4) and (5) to obtain the specific frequency The in-phase correlation function and orthogonal correlation function are as follows: （4） （5） Where N is the total number of thermal image frames, , and are the start frequency and the end frequency, and denote the in-phase reference signal and the quadrature reference signal respectively; Step 32: Calculate the phase of the AC component using the in-phase correlation function and the quadrature correlation function: （6） Step 33: Extract the temperature AC components of several pixels in the defect area and the reference area respectively, calculate the corresponding phase values ​​according to steps 31 and 32, and calculate the phase difference: （7） Where, is the phase difference, and are the phase values ​​calculated based on the pixels in the defect area and the reference area respectively; Step 34: Process the temperature data of each defect area on the surface of the calibration specimen according to steps 31 to 33 to obtain the phase difference corresponding to defects of different sizes and depths. About frequency The change curve of Step 35: Use the Loess formula to smooth the curve of phase difference with respect to frequency; Step 4: Establish the relationship between sensitive frequency and defect depth: Step 41: Define Phase Difference The frequency corresponding to the maximum value is the sensitive frequency, recorded as , sensitive frequency The following relationship is satisfied with the defect depth: （8） Where, and are all undetermined coefficients, D is the defect size, and H is the defect depth; Step 42: Calculate the corresponding sensitive frequency based on the phase difference curve of each defect area relative to the reference area on the calibration specimen surface; Step 43: According to the sensitive frequency of the defect , size D and depth H, draw and The fitting curve of and ; Step 5: Evaluate the defect depth based on the sensitive frequency measurement: Step 51: Apply the same thermal excitation as the calibration specimen to the test piece, extract the temperature AC components of the defect area of ​​the test piece according to step 2, and obtain a phase difference frequency curve based on the temperature AC components according to step 3 to determine the sensitive frequency of the defect area of ​​the test piece; Step 52: Substitute the sensitive frequency and defect size into formula (8) to calculate the defect depth.

2. The method for detecting the depth of internal defects of composite materials based on frequency-modulated infrared thermal imaging according to claim 1, characterized in that The specific steps of step 1 are as follows: Step 11: Use infrared thermal wave radar technology to pre-inspect the test piece, identify the location and shape of the defect based on the collected thermal image, and use a common method to identify the defect size; Step 12: The calibration specimen is identical to the specimen to be tested and contains multiple flat-bottomed blind holes of different sizes and depths; Step 13: Use black spray paint to evenly spray the surface of the calibration specimen.

3. The method for detecting the depth of internal defects of composite materials based on frequency-modulated infrared thermal imaging according to claim 2, characterized in that The shape of the blind holes is similar to the defects of the tested piece, and a sufficient distance is maintained between adjacent blind holes to ensure that they do not interfere with each other during the measurement process.

4. The method for detecting the depth of internal defects of composite materials based on frequency-modulated infrared thermal imaging according to claim 1, characterized in that The f satisfies the following form: （9） Where, and are the start frequency and the end frequency, The loading time.

5. The method for detecting the depth of internal defects of composite materials based on frequency modulation infrared thermal imaging according to claim 1, characterized in that described and Satisfy the following form: （10） （11） Where, is the sampling frequency.

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