CN107356599B - Terahertz nondestructive testing method for ceramic matrix composite material - Google Patents
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Abstract
The invention relates to a terahertz nondestructive testing method for a ceramic matrix composite material, which comprises the following steps: s1, establishing a stable terahertz nondestructive testing imaging testing system; s2, applying the terahertz nondestructive testing imaging testing system established in the step S1 to carry out terahertz nondestructive testing imaging testing on the sample made of the ceramic matrix composite material; and S3, carrying out post-processing on the sample terahertz image obtained in the step S2 to identify the defects of the sample. The invention can effectively carry out nondestructive detection on the defects of different depths and widths in the ceramic matrix composite material and on the surface by adjusting the imaging mode and processing the terahertz image at the later stage.
Description
Technical Field
The invention relates to a terahertz nondestructive testing method for a ceramic matrix composite.
Background
In recent years, the unique performance and the huge research value of terahertz waves gradually draw high attention of scholars at home and abroad, new opportunities and challenges are brought to scientific research and technical innovation of colleges and universities, and the research on terahertz waves is increasing day by day. The THz time domain spectroscopy imaging technology (THz-TDS) mainly researched by the present disclosure gradually becomes the most promising direction in the research field of terahertz waves, and is widely applied in the fields of material identification, biomedicine, nondestructive inspection, safety inspection, military radar, and the like.
The terahertz spectral imaging technology is a very effective nondestructive detection technology. According to the technology, a terahertz time-domain waveform of a sample is measured through a terahertz spectral imaging system, amplitude and phase information is obtained, meanwhile, Fourier transformation is carried out on the time-domain waveform, and parameter information such as an absorption coefficient, a refractive index and energy of the sample is obtained through calculation. In the whole terahertz spectral imaging process, terahertz radiation with very low single photon energy can not cause optical damage to acted substances, and especially harmful ionization effect on living bodies of biological tissues can not be generated. And terahertz radiation has very strong penetrability to nonpolar materials such as cardboard, leather and plastics, has stronger absorption characteristic to the water molecule, consequently has very big help to fields such as safety inspection, quality control.
In 1995, Hu and Nuss et al in the bell laboratory in the united states successfully imaged samples such as an integrated chip and leaves by adding a two-dimensional scanning mobile platform in a terahertz time-domain spectroscopy system, so as to obtain metal wiring information inside the integrated chip and distinguish different water contents of the veins and the leaves. In 1996, the american college of lunsler theory of technology successfully developed a terahertz real-time imaging system based on a charge coupled camera and an electro-optic crystal, and the imaging speed of the terahertz imaging system was greatly improved. In 1997, the refractive indexes of samples at different depths are measured by a terahertz time-domain spectroscopy system in a reflection mode in the Bell laboratory in the United states, and the possible development direction of the terahertz wave auxiliary imaging technology is proved. In 2003, the terahertz research center of the university of Lorentzian Rayleigh in the United states adopts a point-by-point scanning imaging mode to successfully realize imaging of the insulating foam material of the fuel tank of the space shuttle, obtains high attention of the national space agency of the United states, and applies the technology to nondestructive testing of the insulating foam material.
In China, research institutions such as the national department of science and technology, the Chinese academy of sciences, the committee of fundamentals of natural science and the like also give high attention and great promotion to the research on terahertz waves. In 2015, Lewei and the like in a high-power semiconductor laser national key laboratory of Changchun university realize nondestructive detection of interlayer defects of the multilayer glass fiber composite material by utilizing a terahertz time-domain spectral imaging technology. Similarly, in 2015, people at the quality and safety engineering college of the national institute of metrology and technology, such as Dingling, utilize the imaging technology of the reflective terahertz time-domain spectroscopy system to realize nondestructive detection of defects such as hole defects, thermal damage, scratches and abrasion of the carbon fiber reinforced composite material, and have good effects.
Ceramic matrix composites are increasingly becoming indispensable advanced materials in the fields of industry and aerospace due to their excellent high-temperature properties, especially their high melting point, wear resistance and light weight. The ceramic matrix composite is a composite material prepared by taking a ceramic material as a matrix and taking fibers, whiskers or particles of ceramic, carbon fibers or refractory metals as reinforcements through a proper compounding process. During the manufacturing and using processes of the ceramic and ceramic matrix composite, on one hand, some defects which affect the using performance, such as microcracks, pores, oxygen damage and the like, may appear, and on the other hand, the bonding condition of the fiber-matrix interface also affects the mechanical properties of the material. Therefore, research on corresponding non-destructive testing techniques is required. At present, the terahertz nondestructive testing technology for the ceramic matrix composite material is not used for processing different defect types, so that some defects cannot be detected or cannot be quantified.
Disclosure of Invention
The invention aims to provide a terahertz nondestructive testing method for a ceramic matrix composite material, and aims to solve the problem that some defects cannot be detected or cannot be quantified in the existing terahertz nondestructive testing technology for the ceramic matrix composite material. Therefore, the invention adopts the following specific technical scheme:
the terahertz nondestructive testing method for the ceramic matrix composite is characterized by comprising the following steps of:
s1, establishing a stable terahertz nondestructive testing imaging testing system;
s2, applying the terahertz nondestructive testing imaging testing system established in the step S1 to carry out terahertz nondestructive testing imaging testing on the sample made of the ceramic matrix composite material;
and S3, carrying out post-processing on the sample terahertz image obtained in the step S2 to identify the defects of the sample.
Further, the step S2 includes a terahertz spectral time-domain mode imaging step and a terahertz frequency-domain mode imaging step.
Further, the terahertz spectrum time-domain mode imaging step comprises the following steps:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission time-domain spectroscopy system in the terahertz nondestructive testing imaging test system, and storing data;
(2) reading data of all rows of a certain column of pixel points of a sample, and storing the data in a matrix, wherein the first column in the matrix is time in a time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) selecting corresponding parameter values as gray values of pixel points for different imaging methods;
(4) repeating the steps (2) and (3) until the gray values of all pixel points in the sample are obtained;
(5) the gray value of the pixel points calculated according to different methods is proportionally expanded to be between 0 and 255 according to the formula 1,
wherein m is the original grey scale value, mminAnd mmaxRespectively the minimum and maximum of the original gray values, M is the transformed gray value, MminAnd MmaxRespectively minimum and maximum of the transformed gray value, i.e. Mmin＝0，Mmax＝255；
(6) And calling an IMSHOW function in MATLAB in the terahertz nondestructive testing imaging test system for imaging.
Further, the terahertz spectrum time-domain mode imaging step comprises the following steps:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission time-domain spectroscopy system in the terahertz nondestructive testing imaging test system, and storing data;
(2) and reading the data of all rows of a certain column of pixel points of the sample, and accessing the data in the matrix. The first column in the matrix is time in the time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) carrying out Fourier transformation on data from the second column to the last column of the matrix;
(4) assuming that the terahertz time-domain signal of the sample is s (t), the energy is E, the spectrum density after fourier transform is s (f), and the method can be known from Parseval theorem:
therefore, | S (f) non-luminous2Integration in the frequency axis equals the signal energy, | S (f) | Y2Is the energy spectral density;
(5) repeating the steps (2), (3) and (4) until the energy spectrum density of all pixel points in the sample is obtained;
(6) selecting the energy spectrum density value of each pixel point under a certain frequency, expanding the energy spectrum density value to be between [0 and 255] according to a certain proportion, and calling an IMSHOW function in MATLAB in the terahertz nondestructive testing imaging test system for imaging;
(7) and (5) repeating the step (6) until imaging at all frequency values is completed.
Further, the step S3 may include an image fusion step and/or an image segmentation step.
Still further, the image fusion step includes a spatial domain image fusion algorithm and a frequency domain image fusion algorithm.
Still further, the spatial domain image fusion algorithm comprises the following steps:
(1) reading the gray value matrix of the n images to be fused, and recording the gray value of the image k to be fused at the (i, j) as Pk(i,j)；
(2) Carrying out weighted fusion on the images, and recording the gray value after fusion and update of the pixel points (i, j) as M (i, j);
wherein, wkIs the weight of the image to be fused.
(3) Performing small fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as N (i, j);
(4) performing large fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as O (i, j);
(5) and calling an IMSHOW function in MATLAB for imaging according to the gray value matrix M, N, O of the pixel points.
Still further, the frequency domain image fusion algorithm comprises the following steps:
(1) reading n frames to be fusedThe gray value matrix of the image, the gray value of the image k to be fused at (i, j) is recorded as Pk(i,j)；
(2) Setting a decomposition layer dim of wavelet fusion and a wavelet basis function wname, wherein the decomposition layer is 2 layers or 3 layers, and the wavelet basis function comprises haar, db2, db3, sym2 and sym 3;
(3) deleting part of data of the gray value matrix of the image to be fused so that the row and column values of the matrix can be 2dimTrimming;
(4) calling a wavedec2 function in MATLAB to perform wavelet decomposition on the image to be fused;
(5) fusing in a low frequency band, and taking the average value of the low frequency coefficients at corresponding positions in the source image as a new low frequency coefficient; fusing in a high frequency band, and taking the value of the maximum absolute value of the high frequency coefficient at the corresponding position of the source image as a new high frequency coefficient;
(6) calling a waverec2 function in MATLAB to reconstruct according to the processed high-frequency and low-frequency coefficients, and calling an IMSHOW function to re-image.
Still further, the image segmentation step includes an OTSU threshold segmentation method and an edge segmentation method.
Still further, the OTSU threshold segmentation method comprises the following steps:
(1) reading gray value information of each pixel point on an image to be segmented;
(2) setting L gray levels of an image, counting the number of pixel points corresponding to each gray level i, and recording as N (i); the total number of pixels in the image is N (1) + N (2) + … + N (L), where L is 256, the pixel with the gray level i has the actual gray value of (i-1);
(3) calculating the average value of the gray scales of all pixels of the image according to the formula 6, and recording as M;
(4) dividing the image into a foreground part and a background part according to any gray level t, and calculating the proportion of the pixel numbers of the foreground part and the background part to the total pixel number according to formula 7, and respectively recording as PA (t) and PB (t);
(5) calculating the gray average values of the foreground and background pixels according to the formula 8, and respectively recording the gray average values as MA (t) and MB (t);
(6) calculating the inter-class variance of the two parts according to the formula 9, and recording the inter-class variance as ICV (t);
ICV(t)＝PA(t)×[MA(t)-M]2+PB(t)×[MB(t)-M]2(9)
(7) returning to the step (4), traversing various values of T, and finding out the corresponding gray level T when the inter-class variance is maximum;
(8) and comparing the gray level of each pixel point in the image with the threshold value by taking the gray level T as the threshold value, setting the pixel point with the gray level smaller than the threshold value as 1, namely black, and otherwise, setting the pixel point as 0, namely white, and imaging again.
By adopting the technical scheme, the nondestructive detection method has the beneficial effects that through the adjustment of the imaging mode and the later-stage terahertz image processing, the nondestructive detection method can be used for effectively carrying out nondestructive detection on the defects of different depths and widths in the ceramic matrix composite material and on the surface of the ceramic matrix composite material.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a schematic diagram of a terahertz transmission-type time-domain spectroscopy system of the present invention;
FIG. 3 is a flow chart of a terahertz time-domain imaging algorithm of the present invention;
FIG. 4 is a flow chart of the terahertz frequency domain imaging algorithm of the present invention;
FIG. 5 is a flow chart of the spatial domain image fusion algorithm of the present invention;
FIG. 6 is a flow chart of the image fusion algorithm based on wavelet fusion of the present invention;
FIG. 7 is a flow chart of the OTSU threshold segmentation algorithm of the present invention;
FIG. 8 shows the results of non-destructive testing of a 96% alumina sample using the present invention, wherein (a)0.3375THz energy imaging, (b)0.7594THz energy imaging, (c) mean weighted fusion, (d) gray value selected small fusion, (e) gray value selected large fusion, (f) wavelet fusion (2, sym 2);
FIG. 9 shows the results of non-destructive testing of aluminum nitride square coupons using the present invention, wherein (a)1.0000THz energy imaging, (b)1.2406THz energy imaging, (c) mean weighted fusion, (d) Gray value select Small fusion, (e) Gray value select Large fusion, (f) wavelet fusion (3, sym 3);
FIG. 10 shows the results of image segmentation on a wavelet fused image of the 96% alumina sample in FIG. 8, where (a) OTSU thresholding and (b) edge thresholding;
fig. 11 shows the results of image segmentation for the wavelet fused image of the aluminum nitride tile specimen in fig. 9, where (a) OTSU thresholding and (b) edge thresholding.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
The invention will now be further described with reference to the accompanying drawings and detailed description. As shown in FIG. 1, a terahertz nondestructive testing method for a ceramic matrix composite material may include the following steps:
s1, establishing a stable terahertz nondestructive testing imaging testing system (as shown in figure 2);
s2, carrying out terahertz nondestructive testing imaging test on the sample made of the ceramic matrix composite material by applying the terahertz nondestructive testing imaging test system established in the step S1 to generate a multi-dimensional multi-angle terahertz image;
and S3, carrying out post-processing on the sample terahertz image obtained in the step S2 to identify the defects of the sample.
In step S1, the terahertz nondestructive imaging test system mainly includes a femtosecond laser, a terahertz pulse generating device, a detecting device, a time delay platform, a two-dimensional scanning platform, a host, and the like, and the sample to be detected is placed on the two-dimensional scanning platform.
In step S2, a step of terahertz spectral time-domain mode imaging and terahertz frequency-domain mode imaging of the sample to be detected is included. The terahertz spectral time-domain mode imaging step and the terahertz frequency-domain mode imaging step are described below with reference to fig. 3 and 4, respectively.
Terahertz spectrum time-domain mode imaging refers to imaging by using information in terahertz time-domain waveforms. Any change in the terahertz time-domain waveform of the sample is comprehensively reflected by the change of all frequency components in the frequency domain, and has an average effect. This feature determines that imaging using time domain information generally has a better imaging effect, and the difference in imaging quality between different imaging methods is relatively small. Time-domain mode imaging can be divided into amplitude information imaging and phase information imaging according to different time-domain imaging information. The former mainly reflects the thickness and absorption characteristics of the sample, and the latter mainly reflects the thickness and refractive index information of the sample. As shown in fig. 3, the terahertz spectral time-domain mode imaging step includes the following steps:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission time-domain spectroscopy system, and storing data;
(2) and reading the data of all rows of a certain column of pixel points of the sample, and accessing the data in the matrix. The first column in the matrix is time in the time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) for different imaging methods, selecting corresponding parameter values as gray values of pixel points according to table 1;
TABLE 1 time-domain mode imaging method and selection parameter comparison table
(4) Repeating the steps (2) and (3) until the gray values of all pixel points in the sample are obtained;
(5) proportionally expanding the gray values of the pixel points calculated according to different methods to be between [0,255] according to a formula 1;
wherein m is the original grey scale value, mminAnd mmaxRespectively the minimum and maximum of the original gray values, M is the transformed gray value, MminAnd MmaxRespectively minimum and maximum of the transformed gray value, i.e. Mmin＝0，Mmax＝255；
(6) And calling an IMSHOW function in MATLAB in a host of the terahertz nondestructive testing imaging testing system for imaging.
The terahertz frequency domain mode imaging is characterized in that the result of low-frequency component imaging is low in definition and low in noise, is usually used for presenting outline information of a sample, and is relatively flat; the result of high-frequency component imaging is high in definition and high in noise, and is often used for presenting detail information, image salient edges and texture information. In general, amplitude, energy, absorption coefficient, refractive index, and power spectral density of a specific frequency, etc. can be used as information for frequency-domain mode imaging. As shown in fig. 4, the terahertz spectral frequency domain mode imaging step includes the following steps:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission time-domain spectroscopy system, and storing data;
(2) and reading the data of all rows of a certain column of pixel points of the sample, and accessing the data in the matrix. The first column in the matrix is time in the time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) carrying out Fourier transformation on data from the second column to the last column of the matrix;
(4) assuming that the terahertz time-domain signal of the sample is s (t), the energy is E, the spectrum density after fourier transform is s (f), and the method can be known from Parseval theorem:
therefore, | S (f) non-luminous2Integration in the frequency axis equals the signal energy, | S (f) | Y2Is the energy spectral density;
(5) repeating the steps (2), (3) and (4) until the energy spectrum density of all pixel points in the sample is obtained;
(6) selecting the energy spectrum density value of each pixel point under a certain frequency, expanding the energy spectrum density value to be between [0 and 255] according to a certain proportion, and calling an IMSHOW function in MATLAB for imaging;
(7) and (5) repeating the step (6) until imaging at all frequency values is completed.
In step S3, since defects of the sample are not necessarily recognized in all the original terahertz images of the sample obtained in step S2, image fusion and/or image segmentation processing needs to be performed on the original terahertz images. The image fusion processing procedure and the image segmentation processing procedure are described below separately.
The definition of image fusion made by Pohl et al is: image fusion is a specific algorithm that can combine two or more images into a new image. People often divide image fusion into three levels in order from low to high: pixel level fusion, feature level fusion, and decision level fusion. The pixel level fusion method at the bottommost layer is a process of directly processing collected image data so as to obtain fused images, and has the advantages of keeping original data characteristics to the maximum extent and providing detail information for two high-level fusion methods. Pixel-level image fusion can be divided into spatial domain image fusion and frequency domain image fusion according to different processing domains.
The spatial domain image fusion algorithm is characterized in that image fusion is directly carried out by taking corresponding pixels of a plurality of images to be fused as a unit without carrying out image decomposition or transformation on an original image. As shown in fig. 5, the spatial domain image fusion algorithm adopted by the present invention includes the following steps:
(1) reading the gray value matrix of the n images to be fused, and recording the gray value of the image k to be fused at the (i, j) as Pk(i,j)；
(2) Carrying out weighted fusion on the images, and recording the gray value after fusion and update of the pixel points (i, j) as M (i, j);
wherein, wkIs the weight of the image to be fused.
(3) Performing small fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as N (i, j);
(4) performing large fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as O (i, j);
(5) and calling an IMSHOW function in MATLAB for imaging according to the gray value matrix M, N, O of the pixel points.
The frequency domain image fusion method needs to perform wavelet transformation or pyramid transformation on an original image, then perform fusion on coefficients of a plurality of images to be fused after transformation, and finally perform inverse transformation on the fusion coefficients to obtain a final fusion image. Therefore, the frequency domain image fusion method can carry out different processing on the image information of a plurality of images to be fused in different frequency bands, thereby obtaining better image fusion effect. As shown in fig. 6, the frequency domain image fusion algorithm adopted by the present invention includes the following steps:
(1) reading the gray value matrix of the n images to be fused, and recording the gray value of the image k to be fused at the (i, j) as Pk(i,j)；
(2) Setting the decomposition layer dim of wavelet fusion and a wavelet basis function wname, wherein the decomposition layer is 2 layers or 3 layers in general, and the wavelet basis function comprises haar, db2, db3, sym2, sym3 and the like;
(3) deleting part of data of the gray value matrix of the image to be fused so that the row and column values of the matrix can be 2dimTrimming;
(4) calling a wavedec2 function in MATLAB to perform wavelet decomposition on the image to be fused;
(5) fusing in a low frequency band, and taking the average value of the low frequency coefficients at corresponding positions in the source image as a new low frequency coefficient; fusing in a high frequency band, and taking the value of the maximum absolute value of the high frequency coefficient at the corresponding position of the source image as a new high frequency coefficient;
(6) calling a waverec2 function in MATLAB to reconstruct according to the processed high-frequency and low-frequency coefficients, and calling an IMSHOW function to re-image.
Fig. 8 and 9 show the terahertz amplitude imaging and image fusion results of the 96% alumina sample and the aluminum nitride sample, respectively, and the fusion effect is discussed and analyzed below from the perspective of subjective evaluation and objective data, respectively.
Subjectively, the 0.3375THz amplitude imaging (a) can clearly see the strip convex defect on the right side, but the two point concave defects on the left side are blurred, and the image is dark as a whole; two point-like depressions on the left side of the 0.7594THz amplitude imaging (b) are clear, the edge is clear, but the right side image is bright, and the strip-like bulges are not obvious enough. The fusion effect is better by gray value large selection fusion (e) and wavelet fusion (f), the defects of two point-like depressions and strip-like bulges are completely presented, the edge of the sample is clear, and the contrast and the brightness are moderate.
From the objective evaluation index (table 2), the best fusion result is wavelet fusion. Compared with the index values of 0.3375THz and 0.7594THz amplitude imaging, the mean value of the wavelet fused image is centered, the standard deviation is reduced, and the defect that the original image is slightly bright or dark is overcome; the information entropy is slightly reduced, and the information loss is not large; the average gradient and the spatial frequency value become larger, the image is clearer, and the contrast is enhanced.
TABLE 296% Objective evaluation index for alumina
Fig. 9 shows terahertz amplitude imaging and image fusion results of an aluminum nitride square sample, and the fusion effect is discussed and analyzed in terms of subjective evaluation and objective data, respectively.
Subjectively, 1.0000THz amplitude imaging (a) can clearly present the edge information of the aluminum nitride square plate, but the internal texture of the sample is fuzzy; 1.2406THz amplitude imaging (b) the internal texture was clear, but the sample edges were blurry and the image was dark overall. The fusion effect is better in wavelet fusion (f), the edges and the internal grains of the sample are clear, and the brightness and the definition are moderate.
From the objective evaluation index (table 3), the fusion method with the lowest gray scale value is the best fusion result. Compared with the index values of 1.0000THz and 1.2406THz amplitude imaging, the mean value of the wavelet fused image is reduced, the standard deviation is increased, the overall brightness of the image is dark, and the gray value distribution is sparse; the value of the information entropy is increased, and the image information is obviously increased after fusion; the average gradient and spatial frequency values are greater than the original image, and the sharpness and contrast are optimized.
TABLE 3 Objective evaluation index of aluminum nitride square
The definition of image segmentation is: the process of dividing an image into regions that have some visually consistent characteristics and do not overlap each other. Therefore, the purpose of image segmentation is to divide an image into regions that are not related to each other, and to make the images in the same region show similarity of a certain feature, and at the same time, show the difference of the feature in different regions. The image segmentation adopted by the invention comprises an OTSU threshold segmentation method and an edge segmentation method. Specifically, as shown in fig. 7, the OTSU threshold segmentation method includes the following steps:
(1) reading gray value information of each pixel point on an image to be segmented;
(2) let L be 256 gray levels for the image. Counting the number of pixel points corresponding to each gray level i, and recording as N (i); the total number of pixels in the image is N (1) + N (2) + … + N (l). Specifically, in this experiment, the pixel having the gray level i has the actual gray level value (i-1);
(3) calculating the average value of the gray scales of all pixels of the image according to the formula 6, and recording as M;
(4) dividing the image into a foreground part and a background part according to any gray level t, and calculating the proportion of the pixel numbers of the foreground part and the background part to the total pixel number according to formula 7, and respectively recording as PA (t) and PB (t);
(5) calculating the gray average values of the foreground and background pixels according to the formula 8, and respectively recording the gray average values as MA (t) and MB (t);
(6) calculating the inter-class variance of the two parts according to the formula 9, and recording the inter-class variance as ICV (t);
ICV(t)＝PA(t)×[MA(t)-M]2+PB(t)×[MB(t)-M]2(9)
(7) returning to the step (4), traversing various values of T, and finding out the corresponding gray level T when the inter-class variance is maximum;
(8) comparing the gray level of each pixel point in the image with a threshold value by taking the gray level T as the threshold value, and setting the pixel point with the gray level less than the threshold value as 1, namely black; otherwise, it is set to 0, i.e., white, and imaging is performed again.
The image edge segmentation method is realized by utilizing the difference of a foreground object and a background on certain image characteristics, wherein the image characteristics comprise gray scale, color, texture characteristics and the like, and the image characteristics are the positions of the detected image characteristics. The edge segmentation operator is selected to dominate the local maxima of the first derivative and the zero values of the second derivative. The invention adopts Canny operator.
Fig. 10 shows the results of image segmentation on a 96% alumina sample wavelet fused image. Through OTSU image segmentation, the lower right convex defect and two smaller concave defects can be clearly seen, but the upper left concave defect is not present. The edge division can preferably present the edge of the lower right convex portion and the edge of the middle concave region, and likewise, the upper left concave edge is not conspicuous.
Fig. 11 shows the results of image segmentation on an aluminum nitride square sample wavelet fused image. The result of OTSU segmentation shows the grain information in the sample clearly, and the foreground and the background are well separated. The edge of the sample can be clearly shown by the result of the edge segmentation, but the communication effect of the internal texture information is poor.
According to specific detection results, the method can effectively detect different defects of the ceramic-based material.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. The terahertz nondestructive testing method for the ceramic matrix composite is characterized by comprising the following steps of:
s1, establishing a stable terahertz nondestructive testing imaging testing system;
s2, applying the terahertz nondestructive testing imaging testing system established in the step S1 to carry out terahertz nondestructive testing imaging testing on the sample made of the ceramic matrix composite material;
s3, carrying out post-processing on the sample terahertz image obtained in the step S2, wherein the post-processing comprises an image fusion step and/or an image segmentation step so as to identify the defects of the sample;
the image segmentation step comprises an OTSU threshold segmentation method and an edge segmentation method;
the OTSU threshold segmentation method comprises the following steps:
(1) reading gray value information of each pixel point on an image to be segmented;
(2) setting L gray levels of an image, counting the number of pixel points corresponding to each gray level i, and recording as N (i); the total number of pixels in the image is N (1) + N (2) + … + N (L), where L is 256, the pixel with the gray level i has the actual gray value of (i-1);
(3) calculating the average value of the gray scales of all pixels of the image according to the formula 6, and recording as M;
(4) dividing the image into a foreground part and a background part according to any gray level t, and calculating the proportion of the pixel numbers of the foreground part and the background part to the total pixel number according to formula 7, and respectively recording as PA (t) and PB (t);
(5) calculating the gray average values of the foreground and background pixels according to the formula 8, and respectively recording the gray average values as MA (t) and MB (t);
(6) calculating the inter-class variance of the two parts according to the formula 9, and recording the inter-class variance as ICV (t);
ICV(t)＝PA(t)×[MA(t)-M]2+PB(t)×[MB(t)-M]2(9)
(7) returning to the step (4), traversing various values of T, and finding out the corresponding gray level T when the inter-class variance is maximum;
(8) and comparing the gray level of each pixel point in the image with the threshold value by taking the gray level T as the threshold value, setting the pixel point with the gray level smaller than the threshold value as 1, namely black, and otherwise, setting the pixel point as 0, namely white, and imaging again.
2. The method for terahertz nondestructive testing of ceramic matrix composite material according to claim 1, wherein said step S2 includes a terahertz time-domain mode imaging step and a terahertz frequency-domain mode imaging step.
3. The method for terahertz nondestructive testing of ceramic matrix composite material according to claim 2, wherein the terahertz time-domain mode imaging step comprises the steps of:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission type time-domain spectroscopy system in the terahertz nondestructive testing imaging test system, and storing data;
(2) reading data of all rows of a certain column of pixel points of a sample, and storing the data in a matrix, wherein the first column in the matrix is time in a time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) selecting corresponding parameter values as gray values of pixel points for different imaging methods;
(4) repeating the steps (2) and (3) until the gray values of all pixel points in the sample are obtained;
(5) the gray value of the pixel points calculated according to different methods is proportionally expanded to be between 0 and 255 according to the formula 1,
wherein m is the original grey scale value, mminAnd mmaxRespectively the minimum and maximum of the original gray values, M is the transformed gray value, MminAnd MmaxRespectively minimum and maximum of the transformed gray value, i.e. Mmin＝0，Mmax＝255；
(6) And calling an IMSHOW function in MATLAB in the terahertz nondestructive testing imaging test system for imaging.
4. The method for terahertz nondestructive testing of ceramic matrix composite material according to claim 2, wherein the step of terahertz frequency domain mode imaging comprises the steps of:
(1) acquiring a terahertz time-domain waveform on each pixel point of a sample by using a terahertz transmission type time-domain spectroscopy system in the terahertz nondestructive testing imaging test system, and storing data;
(2) reading data of all rows of a certain column of pixel points of a sample, and storing the data in a matrix, wherein the first column in the matrix is time in a time domain, and the unit is ps; sequentially accessing the amplitude of the pixel points of the row of the sample in each row from the second row to the last row;
(3) carrying out Fourier transformation on data from the second column to the last column of the matrix;
(4) supposing that the terahertz time-domain signal of the sample is s (t), the energy is E, the spectrum density after fourier transform is s (f), and the method can be known from Parseval theorem:
wherein | s (f) |2 is the energy spectral density;
(5) repeating the steps (2), (3) and (4) until the energy spectrum density of all pixel points in the sample is obtained;
(6) selecting the energy spectrum density value of each pixel point under a certain frequency, expanding the energy spectrum density value to be between [0 and 255] according to a certain proportion, and calling an IMSHOW function in MATLAB in the terahertz nondestructive testing imaging test system for imaging;
(7) and (5) repeating the step (6) until imaging at all frequency values is completed.
5. The method for terahertz nondestructive testing of ceramic matrix composite material according to claim 1, wherein said image fusion step comprises a spatial domain image fusion algorithm and a frequency domain image fusion algorithm.
6. The terahertz nondestructive testing method for the ceramic matrix composite material according to claim 5, wherein the spatial domain image fusion algorithm comprises the following steps:
(1) reading a gray value matrix of n images to be fused, and recording the gray value of the image k to be fused at (i, j) as Pk (i, j);
(2) carrying out weighted fusion on the images, and recording the gray value after fusion and update of the pixel points (i, j) as M (i, j);
wherein wk is the weight of the image to be fused;
(3) performing small fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as N (i, j);
(4) performing large fusion on the gray value of the image, and recording the gray value after fusion update of the pixel point (i, j) as O (i, j);
(5) and calling an IMSHOW function in MATLAB in the terahertz nondestructive testing imaging test system for imaging according to the gray value matrix M, N, O of the pixel points.
7. The method for terahertz nondestructive testing of ceramic matrix composite material according to claim 5, wherein said frequency domain image fusion algorithm comprises the steps of:
(1) reading a gray value matrix of n images to be fused, and recording the gray value of the image k to be fused at (i, j) as Pk (i, j);
(2) setting a decomposition layer dim of wavelet fusion and a wavelet basis function wname, wherein the decomposition layer dim is 2 layers or 3 layers, and the wavelet basis function wname comprises haar, db2, db3, sym2 and sym 3;
(3) deleting part of data of the gray value matrix of the image to be fused, so that the row and column values of the matrix can be divided by 2 dim;
(4) calling a wavedec2 function in MATLAB in the terahertz nondestructive testing imaging test system to perform wavelet decomposition on the image to be fused;
(5) fusing in a low frequency band, and taking the average value of the low frequency coefficients at corresponding positions in the source image as a new low frequency coefficient; fusing in a high frequency band, and taking the value of the maximum absolute value of the high frequency coefficient at the corresponding position of the source image as a new high frequency coefficient;
(6) calling a waverec2 function in MATLAB to reconstruct according to the processed high-frequency and low-frequency coefficients, and calling an IMSHOW function to re-image.
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| CN114414577B (en) * | 2021-12-24 | 2023-12-22 | 华南理工大学 | Method and system for detecting plastic products based on terahertz technology |
| CN116818704B (en) * | 2023-03-09 | 2024-02-02 | 苏州荣视软件技术有限公司 | High-precision full-automatic detection method, equipment and medium for semiconductor flaw AI |
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