CN112834457B - Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging - Google Patents
Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging Download PDFInfo
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Abstract
The invention relates to a metal surface microcrack characterization technology, in particular to a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method. The method solves the problem that the traditional metal surface microcrack characterization technology has incomplete characterization results. The metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected, a Fourier lens, a semiconductor laser, a computer, a signal generator and a thermal infrared imager; the exit end of the semiconductor laser is over against the incident end of the Fourier lens; the exit end of the Fourier lens is over against the front surface of the metal workpiece to be detected; the signal output end of the computer and the signal output end of the signal generator are connected with the signal input end of the semiconductor laser; and the detection end of the thermal infrared imager is obliquely opposite to the front surface of the metal workpiece to be detected. The method is suitable for characterization of the microcracks on the metal surface.
Description
Technical Field
The invention relates to a metal surface microcrack characterization technology, in particular to a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method.
Background
Metals are widely used in the manufacture of aircraft engines, automobiles, ships and the like because of their advantages of high strength, good heat resistance, strong corrosion resistance and the like. In the use process of the metal workpiece, surface microcracks are easy to appear due to fatigue aging or severe environment, so that the mechanical property, vibration noise and service life of the metal workpiece are influenced. Therefore, in order to ensure the mechanical property, vibration noise and service life of the metal workpiece, the metal workpiece needs to be subjected to surface microcrack characterization. However, the conventional metal surface microcrack characterization technology is limited by its principle, and can only perform two-dimensional characterization of the surface microcrack, but cannot perform three-dimensional characterization of the surface microcrack, thereby resulting in incomplete characterization results. Therefore, a metal microcrack three-dimensional characterization system and a metal microcrack three-dimensional characterization method based on reflective laser thermal imaging are needed to be invented to solve the problem that the characterization result of the traditional metal surface microcrack characterization technology is incomplete.
Disclosure of Invention
The invention provides a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method, aiming at solving the problem that the characterization result of the traditional metal surface microcrack characterization technology is incomplete.
The invention is realized by adopting the following technical scheme:
the metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected, a Fourier lens, a semiconductor laser, a computer, a signal generator and a thermal infrared imager; the exit end of the semiconductor laser is over against the incident end of the Fourier lens; the exit end of the Fourier lens is over against the front surface of the metal workpiece to be detected; the signal output end of the computer and the signal output end of the signal generator are connected with the signal input end of the semiconductor laser; and the detection end of the thermal infrared imager is obliquely opposite to the front surface of the metal workpiece to be detected.
The invention discloses a metal microcrack three-dimensional characterization method based on reflective laser thermal imaging (the method is realized based on a metal microcrack three-dimensional characterization system based on reflective laser thermal imaging), which is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece, selecting a laser heating point on the front surface of the detected metal workpiece, wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser through a computer; setting the output pulse width of the semiconductor laser through a signal generator; then, starting a semiconductor laser and a thermal infrared imager, wherein the semiconductor laser emits a pulse laser beam, and the pulse laser beam is focused by a Fourier lens and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, closing the thermal infrared imager; in the heating and radiating processes, the thermal infrared imager detects the temperature field change of the front surface of the detected metal workpiece in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar blocks of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and exponential transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting Fourier value change curves of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
In the second step, the output power of the semiconductor laser is set to 50W, and the output pulse width of the semiconductor laser is set to 1 s.
In the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is1Representing multiplicative noise of the thermal infrared image.
In the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents the noise variance of the thermal infrared image; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m is a group of2Representing the median of the second-level diagonal components in a two-level wavelet decomposition.
In the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; zxRepresents a search block; e represents the noise variance of the thermal infrared image.
And in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
In the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
In the step ten, the area of the contour of the microcrack is calculated by using the area function.
And in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
Compared with the traditional metal surface microcrack characterization technology, the reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method disclosed by the invention are based on the laser heating technology and the infrared temperature measurement technology, and are combined with a brand-new image processing flow, so that the outline area characterization of the microcracks is realized on one hand, and the depth characterization of the microcracks is realized on the other hand, and therefore, the three-dimensional characterization of the surface microcracks of the metal workpiece is realized, and the characterization result is more comprehensive.
The method effectively solves the problem that the traditional metal surface microcrack characterization technology has incomplete characterization results, and is suitable for metal surface microcrack characterization.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
In the figure: 1-a metal workpiece to be detected, 2-a Fourier lens, 3-a semiconductor laser, 4-a computer, 5-a signal generator and 6-a thermal infrared imager.
Detailed Description
The metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected 1, a Fourier lens 2, a semiconductor laser 3, a computer 4, a signal generator 5 and a thermal infrared imager 6; wherein, the exit end of the semiconductor laser 3 is over against the incident end of the Fourier lens 2; the exit end of the Fourier lens 2 is over against the front surface of the metal workpiece 1 to be detected; the signal output end of the computer 4 and the signal output end of the signal generator 5 are both connected with the signal input end of the semiconductor laser 3; the detection end of the thermal infrared imager 6 is obliquely opposite to the front surface of the metal workpiece 1 to be detected.
The invention discloses a reflective laser thermal imaging-based metal microcrack three-dimensional characterization method (which is realized based on a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system), which is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece 1, selecting a laser heating point on the front surface of the detected metal workpiece 1, wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser 3 by the computer 4; the output pulse width of the semiconductor laser 3 is set through the signal generator 5; then, starting the semiconductor laser 3 and the thermal infrared imager 6, wherein the semiconductor laser 3 emits a pulse laser beam, and the pulse laser beam is focused by the Fourier lens 2 and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser 3 is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, the thermal infrared imager 6 is closed; in the heating and radiating processes, the thermal infrared imager 6 detects the temperature field change of the front surface of the detected metal workpiece 1 in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar blocks of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and exponential transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting a Fourier value change curve of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
In the second step, the output power of the semiconductor laser 3 is set to 50W, and the output pulse width of the semiconductor laser 3 is set to 1 s.
In the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is1Representing multiplicative noise of the thermal infrared image.
In the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents the noise square of the thermal infrared imageA difference; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m2Representing the median of the second-level diagonal components in a two-level wavelet decomposition.
In the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; zxRepresents a search block; e represents the noise variance of the thermal infrared image.
And in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
In the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
In the step ten, the area of the contour of the microcrack is calculated by using the area function.
And in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (9)
1. A metal microcrack three-dimensional characterization method based on reflective laser thermal imaging is characterized by comprising the following steps: the method is realized based on a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system, and the system comprises a metal workpiece to be detected (1), a Fourier lens (2), a semiconductor laser (3), a computer (4), a signal generator (5) and an infrared thermal imager (6); wherein the emergent end of the semiconductor laser (3) is over against the incident end of the Fourier lens (2); the exit end of the Fourier lens (2) is just opposite to the front surface of the metal workpiece (1) to be detected; the signal output end of the computer (4) and the signal output end of the signal generator (5) are connected with the signal input end of the semiconductor laser (3); the detection end of the thermal infrared imager (6) is obliquely opposite to the front surface of the metal workpiece (1) to be detected;
the method is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece (1), selecting a laser heating point on the front surface of the detected metal workpiece (1), wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser (3) through a computer (4); setting the output pulse width of the semiconductor laser (3) through a signal generator (5); then, starting the semiconductor laser (3) and the thermal infrared imager (6), wherein the semiconductor laser (3) emits a pulse laser beam, and the pulse laser beam is focused by the Fourier lens (2) and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser (3) is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, the thermal infrared imager (6) is closed; in the heating and radiating processes, the thermal infrared imager (6) detects the temperature field change of the front surface of the detected metal workpiece (1) in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar block set of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and index transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting Fourier value change curves of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
2. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: in the second step, the output power of the semiconductor laser (3) is set to 50W, and the output pulse width of the semiconductor laser (3) is set to 1 s.
3. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: in the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is a radical of an alkyl radical1Representing multiplicative noise of the thermal infrared image.
4. The three-dimensional characterization method for the metal microcracks based on the reflective laser thermal imaging according to claim 1, characterized in that: in the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents a noise variance of the thermal infrared image; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m2Represents two layersThe median of the second layer diagonal components in the wavelet decomposition.
5. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: in the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; z is a linear or branched memberxRepresents a search block; e represents the noise variance of the thermal infrared image.
6. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: and in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
7. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: in the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
8. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: in the step ten, the area of the contour of the microcrack is calculated by using the area function.
9. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 1, wherein: and in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
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