CN106932416B - Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial - Google Patents
Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial Download PDFInfo
- Publication number
- CN106932416B CN106932416B CN201710084115.7A CN201710084115A CN106932416B CN 106932416 B CN106932416 B CN 106932416B CN 201710084115 A CN201710084115 A CN 201710084115A CN 106932416 B CN106932416 B CN 106932416B
- Authority
- CN
- China
- Prior art keywords
- defect
- thickness
- dimensional
- digital
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000007547 defect Effects 0.000 claims abstract description 114
- 238000001514 detection method Methods 0.000 claims abstract description 66
- 238000003384 imaging method Methods 0.000 claims description 33
- 230000004044 response Effects 0.000 claims description 17
- 230000005855 radiation Effects 0.000 claims description 15
- 239000000463 material Substances 0.000 claims description 14
- 230000002950 deficient Effects 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 6
- 230000000877 morphologic effect Effects 0.000 claims description 5
- 238000013139 quantization Methods 0.000 claims description 4
- 230000010339 dilation Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 abstract description 8
- 230000007812 deficiency Effects 0.000 abstract 1
- 239000000284 extract Substances 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000009659 non-destructive testing Methods 0.000 description 3
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 229910000601 superalloy Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003760 hair shine Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000005495 investment casting Methods 0.000 description 1
- 238000000465 moulding Methods 0.000 description 1
- 229910052759 nickel Inorganic materials 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
- G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/40—Imaging
- G01N2223/401—Imaging image processing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/60—Specific applications or type of materials
- G01N2223/646—Specific applications or type of materials flaws, defects
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The two-dimensional detection image of defect is carried out a finite element according to pixel arrangement first and divided by the gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial that the invention discloses a kind of；Then discrete quantized is carried out according to gray value to the thickness at each pixel, determines the corresponding relationship of gray scale and thickness；Finally all pixels finite element is accumulated, extracts the three-dimensional parameter of defect area.The two-dimensional detection image of defect is carried out a finite element according to pixel arrangement and divided by thought of this method based on finite element, carries out discrete quantized according to gray value to the thickness at each pixel, and then determine the corresponding relationship of gray scale and thickness.By adding up to all pixels finite element region, extraction obtains the three-dimensional parameter of defect, deficiency of the traditional radiographic detection method in the extraction of defect three-dimensional parameter can be effectively made up, can realize the extraction to gas turbine blades internal flaw three-dimensional parameter with lower cost with higher efficiency.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the field of industrial ray nondestructive testing, and relates to a method for extracting three-dimensional parameters of internal defects of a gas turbine blade based on digital rays.
[ background of the invention ]
The gas turbine is a rotating impeller type power machine widely applied in the fields of electric power, navigation and the like. At present, the key technology of manufacturing core parts of the gas turbine is not completely mastered in China, and related products mainly depend on foreign imports. The blade is a core pneumatic part which interacts with a high-temperature, high-pressure and high-flow-rate working medium on a gas turbine and realizes energy conversion, is usually manufactured by a precision casting molding process, and needs to bear huge working load at extremely high temperature and pressure. Since the blade may form defects such as shrinkage cavity, shrinkage porosity, crack, inclusion and the like in the blade in the manufacturing and service stages, the working performance, the service life and the operational safety and reliability of the whole gas turbine are seriously affected. Therefore, the research on the detection technology of the defects of the gas turbine blade has great and profound strategic significance for improving the manufacturing level of the gas turbine in China and breaking through technical blockages in developed countries.
Since gas turbine blades belong to the class of complex free-form surface parts and are generally constructed of relatively dense nickel-base superalloy materials, radiation-based methods are typically employed for non-destructive testing thereof. The traditional method adopts industrial rays to transilluminate the leaves, and realizes the detection of the internal defects of the leaves by means of film imaging. The method has the advantages of high imaging resolution, high sensitivity, intuition, reliability and the like, and plays an important role in the field of industrial nondestructive testing. Since the method essentially projects and images the leaves on the film along the transillumination direction, only the two-dimensional outline of the defect can be clearly displayed, but the three-dimensional characteristic information of the defect in the transillumination direction cannot be displayed. Even experienced professionals have difficulty estimating information in this dimension accurately. The industrial CT technology can accurately, clearly and intuitively acquire the three-dimensional information of the internal structure composition and the defects of the measured object, so that the industrial CT technology has certain application in the detection of the internal defects of the blade. There are two limitations, however: on one hand, the nickel-based superalloy as the component material of the blade has a large attenuation coefficient for rays, and the CT with low power cannot completely and effectively penetrate the blade. Therefore, only industrial CT systems with large transillumination power can be used, and the high price of such systems directly drives up the cost of blade detection. On the other hand, in order to accurately detect the defects with smaller dimensions in the blade, the blade needs to be sliced in a large number at smaller intervals. Considering that gas turbine blades generally require full inspection, the requirement of acquiring a large number of slice data not only greatly reduces the inspection efficiency, but also brings a huge operation cost. Therefore, it is difficult for industrial CT to be widely applied to the detection of gas turbine blades in practical engineering due to its high detection cost and extremely low detection efficiency.
[ summary of the invention ]
The invention aims to solve the technical problem that the defects in the prior art are overcome, and discloses a method for extracting the internal defect three-dimensional parameters of the large-scale high-temperature blade based on digital rays.
The invention adopts the following technical scheme:
the method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays comprises the steps of firstly, carrying out primary finite element division on a two-dimensional detection image of the defect according to pixel arrangement; then, performing discrete quantization on the thickness of each pixel according to the gray value, and determining the corresponding relation between the gray G and the thickness T; and finally, accumulating finite elements of all pixels and extracting three-dimensional parameters of the defect area.
Further, the method comprises the following steps:
s1, determining the linear response range of the digital ray detection system;
s2, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image, and acquiring a two-dimensional outline of the defect;
s3, obtaining a relation curve of image gray scale and material thickness under specific transillumination parameters by transilluminating the test wedge block;
and S4, converting the simulated background gray scale of the defect and the defect gray scale of the original image into thicknesses, and subtracting the thicknesses to obtain the defect thickness.
And S5, accumulating the thickness information of each defective pixel to obtain the three-dimensional parameters of the defect.
Further, in step S1, the digital radiation detection system includes a radiation source, a support platform for placing the detected blade, a flat panel detector, and an imaging and control system, the radiation source is connected to the imaging and control system through a radiation source controller, the support platform is connected to the imaging and control system through a detection control system, and the flat panel detector is connected to the imaging and control system through a detector controller.
Further, in step S1, a lead plate with a central hole is disposed on the flat panel detector, the hole penetrates through the lead plate, the diameter of the hole is greater than 1mm and smaller than 10mm, the transillumination parameters of the digital ray detection system are increased to obtain different exposure amounts, an image formed by the flat panel detector at the collimation hole is changed from full black to high bright full white, the imaging gray levels of the digital ray detection system corresponding to the different exposure amounts in the hole area are obtained, a response curve of the flat panel detector is drawn, and a linear response area of the digital ray detection system is determined.
Further, the linear response relationship of the digital ray detection system is as follows:
G＝α·H+b
wherein: g-imaging gray scale value, H-exposure, alpha-linear response region slope and b-imaging gray scale linear offset.
Further, in step S2, the acquiring the two-dimensional profile of the defect specifically includes the following steps:
s21, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image with the gray level in the range of the linear region of the system;
s22, extracting a defect boundary based on the blade detection image, and preliminarily obtaining a two-dimensional outline of the defect;
s23, performing morphological dilation on the defect two-dimensional outline preliminarily acquired in the step S22, and enlarging a defect area to ensure that all defect outlines are completely included in the morphologically dilated area;
s24, carrying out bicubic interpolation on the expansion area, and calculating the simulated background of the defect area;
and S25, performing difference on the simulated background image and the original image, and performing binarization processing on the difference image to obtain a two-dimensional accurate outline of the defect.
Further, in step S3, processing wedges for the same material test, each of which has a thickness variation range the same as that of a corresponding region, and each of which uses the same exposure parameters as the corresponding region, and uses a divisional transillumination mode to limit the thickness of each region within a certain range, and uses a set of specific exposure parameters to perform a transillumination, wherein the transillumination parameters include a tube voltage, a tube current, and an exposure time.
Further, in the one-time transillumination process, as the effective exposure amount reaching the flat panel detector is reduced along with the increase of the transillumination thickness, the functional relation between the imaging gray scale value G and the transillumination thickness T is determined according to the linear relation between the exposure amount H and the imaging gray scale value G as follows:
G＝f(T)。
further, in step S5, the volume of each pixel region in the defect two-dimensional contour region is obtained according to the thickness information as follows:
V＝a2×(T0-T1)
wherein: v-volume of single pixel region, a-side length of pixel, T0Theoretical thickness of single pixel area, T1-the actual thickness of the individual pixel areas.
Further, the three-dimensional parameters of the defect area are calculated as follows:
wherein: vall-total volume of defective area, n-total number of pixels of defective area, Ti0Theoretical thickness of ith pixel region of defect region, Ti1-actual thickness of the ith pixel region of the defective region.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a method for extracting three-dimensional parameters of internal defects of a gas turbine blade based on digital rays, which is based on the thought of finite elements, carries out one-time finite element division on a two-dimensional detection image of the defects according to pixel arrangement, carries out discrete quantization on the thickness of each pixel according to a gray value, and further determines the corresponding relation between the gray value and the thickness. The defect three-dimensional parameters are extracted by accumulating all pixel finite element regions, so that the defect three-dimensional parameter extraction defect of the traditional ray detection method can be effectively overcome, and compared with an industrial CT system, the defect three-dimensional parameters in the gas turbine blade can be extracted with higher efficiency and lower cost.
Further, the method comprises the steps of firstly determining the linear response range of a digital ray detection system, then carrying out transillumination on the blade by using the digital ray detection system to obtain a two-dimensional detection image, obtaining a two-dimensional outline of the defect, then simulating the background of the defect area by adopting a bicubic interpolation method, then carrying out transillumination on the test wedge block to obtain a relation curve between the image gray level and the material thickness under a specific transillumination parameter, then converting the simulated background gray level of the defect and the defect gray level of the original image into the thickness, subtracting the two thicknesses to obtain the thickness of the defect, and finally accumulating the thickness information of each defect pixel to obtain the three-dimensional parameter of the defect.
Furthermore, the digital ray detection system comprises a ray source, a supporting platform for placing the detected blade, a flat panel detector and an imaging and control system, wherein a lead plate with a millimeter-scale through hole in the center is arranged on the flat panel detector, the radiation scattering is reduced through the collimation effect of the lead plate on the radiation, and the radiation scattering phenomenon is effectively avoided when the radiation shines through an object.
Further, a defect boundary is extracted based on the blade detection image, a two-dimensional outline of the defect is preliminarily obtained, then the preliminarily obtained two-dimensional outline of the defect is subjected to morphological expansion, a defect area is expanded to ensure that all the defect outlines are completely included in the area subjected to the morphological expansion, bicubic interpolation is carried out on the expansion area, a simulated background of the defect area is calculated, finally the simulated background image is differed from the original image, binarization processing is carried out on the difference image to obtain a two-dimensional accurate outline of the defect, the two-dimensional outline of the defect can be accurately obtained, errors are reduced, and the original thickness can be obtained by simulating the defect background.
Furthermore, processing the same-material test wedge blocks with the thickness variation range the same as the partition thickness variation range, wherein each wedge block adopts the same exposure parameters as the corresponding area, and the partition transillumination mode is adopted, so that the transillumination thickness represented by each gray value of the transillumination image can be quantitatively quantified by transilluminating the test wedge blocks with the known sizes.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
[ description of the drawings ]
FIG. 1 is a schematic diagram of a digital radiation detection system of the present invention;
FIG. 2 is a schematic flow chart of the method of the present invention;
FIG. 3 is a schematic diagram of the digital ray detection system test of the present invention;
FIG. 4 is a graph showing the response law of the flat panel detector according to the present invention;
FIG. 5 is a schematic flow chart of a defect two-dimensional contour obtained by the method of the present invention;
FIG. 6 is a sectional perspective view of a blade according to the present invention;
FIG. 7 is a perspective view of a test wedge of the present invention;
FIG. 8 is a graph of the gray scale-thickness relationship of the present invention;
FIG. 9 is a schematic illustration of material thickness from image grayscale acquisition according to the present invention;
FIG. 10 is a schematic view of a defective pixel according to the present invention.
[ detailed description ] embodiments
Referring to fig. 1, the invention discloses a digital ray detection system and a method for quantitatively extracting three-dimensional parameters of internal defects of a gas turbine blade based on digital rays. The thickness of each pixel is discretized and quantized by the gray level of the image, so that each gray level corresponds to one thickness (the dynamic range of the flat panel detector reaches sixteen bits, and the obtained discrete thickness is three orders of magnitude smaller than the pixel size). Therefore, the thickness of each pixel is obtained, and the quantitative detection of the three-dimensional parameters of the defects can be realized based on the thickness and the size of the pixels.
Referring to fig. 2, the method for quantitatively extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital ray of the present invention includes the following six steps:
s1, determining the linear response range of the digital ray detection system;
referring to fig. 3, the digital ray detection system includes a ray source, a support platform for placing a detected blade, a flat panel detector and an imaging and control system, the ray source is connected to the imaging and control system through a ray source controller, the support platform is connected to the imaging and control system through a detection control system, the flat panel detector is connected to the imaging and control system through a detector controller, a millimeter-sized small through hole is processed in the center of a lead plate due to scattering of rays when the rays transilluminate an object, the diameter of the small hole is more than 1mm and less than 10mm, scattering of the rays is reduced through collimation of the small hole on the rays, and the lead plate is placed in close contact with the flat panel detector.
The digital ray detection system works as follows: setting various transillumination parameters (tube voltage, tube current and exposure time) through a radiation source controller to control a radiation source to emit X-rays; x-rays emitted by the ray source penetrate through the detected blade and are received by the flat panel detector; the flat panel detector converts the X-rays into electric signals, and finally displays a detection image on an imaging and control system through analog-to-digital conversion.
The functions of each system are as follows:
a ray source controller: setting transillumination parameters of a ray source and triggering the ray source;
the detection control system comprises: adjusting the position of the detected blade;
a detector controller: triggering the flat panel detector;
imaging and control system: and controlling each system to trigger and display images.
Referring to fig. 4, the transillumination parameters (tube voltage, tube current, exposure time) of the digital ray detection system are increased continuously to obtain different exposure amounts until the image formed by the flat panel detector at the collimating aperture changes from full black to high bright and full white. And acquiring the imaging gray scale of the digital ray detection system corresponding to different exposure quantities in the pinhole area so as to draw a response curve of the flat panel detector, and further determining the linear response area of the digital ray detection system. The two parameters alpha and b in the formula (1) are determined by combining the imaging gray scale and exposure data, and the linear response relation of the digital ray detection system is obtained as follows:
G＝α·H+b (1)
wherein:
g-imaging gray scale value
H-Exposure
Slope of alpha-linear response region
b-linear offset of imaging gray scale.
Aiming at the digital ray transillumination process of any workpiece, exposure parameters must be adjusted to enable the gray level of a once transillumination image to be in the range of a linear region, otherwise, the image cannot be quantitatively analyzed.
S2, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image;
referring to fig. 5, acquiring a two-dimensional defect outline and simulating a background of a defect region includes the following five steps:
s21, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image with the gray level in the range of the linear region of the system;
s22, extracting a defect boundary based on the blade detection image, and preliminarily obtaining a two-dimensional outline of the defect;
s23, performing morphological dilation on the defect two-dimensional outline preliminarily acquired in the step S22, and enlarging a defect area to ensure that all defect outlines are completely included in the morphologically dilated area;
s24, carrying out bicubic interpolation on the expansion area, and calculating the simulated background of the defect area;
and S25, performing difference on the simulated background image and the original image, and performing binarization processing on the difference image to obtain a two-dimensional accurate outline of the defect.
S3, obtaining a relation curve of image gray scale and material thickness under specific transillumination parameters (tube voltage, tube current and exposure time) by transilluminating the test wedge block;
referring to fig. 6, due to the special structure of the blade, the detection of the blade adopts a zonal transillumination mode. The blade to be detected is divided into six areas, the thickness of each area is limited within a certain range, and a set of specific exposure parameters is adopted for carrying out transillumination once.
Referring to fig. 7, processing wedges made of the same material and having the same thickness variation range as the divisional thickness variation range, wherein each wedge employs the same exposure parameters as the corresponding area, limits the thickness of each area within a certain range by adopting a divisional transillumination mode, and performs a transillumination by employing a set of specific exposure parameters, wherein the transillumination parameters include tube voltage, tube current, and exposure time.
And processing a test wedge block of the same material with the thickness change range as that of a certain subarea, wherein each wedge block adopts the same exposure parameters as the corresponding area to perform one-time transillumination. In one transillumination process, the effective exposure amount reaching the detector is reduced along with the increase of the transillumination thickness (the two are in one-to-one correspondence). Since the exposure H and the imaging gray G have a linear relationship as described in equation (1), the functional relationship between the imaging gray G and the transillumination thickness T can be determined:
G＝f(T) (2)
referring to fig. 8, each wedge block performs a transillumination process using the same exposure parameters as the corresponding region, records the image gray level under continuous thickness variation, and determines the relationship between the image gray level and the material thickness.
And S4, converting the simulated background gray scale of the defect and the defect gray scale of the original image into thicknesses, and subtracting the thicknesses to obtain the defect thickness.
Referring to fig. 9, through a relationship curve between the image gray and the material thickness, the theoretical thickness and the actual thickness of the blade at each pixel point can be obtained from the simulated background image and the actual detection image, and the two values are subtracted to obtain the defect region of the blade and the thickness of each pixel point. For a single pixel point, G0For bi-cubic interpolation of background gray, G1For the actual pixel gray scale, the defect thickness can be obtained by the following equation:
T＝T0-T1 (3)
wherein,
t-defect thickness
T0-pixel area interpolation theoretical thickness
T1-the actual thickness of the pixel area.
And S5, accumulating the thickness information of each defective pixel to obtain the three-dimensional parameters of the defect.
Referring to fig. 10, based on the finite element concept, the volume of each pixel region in the two-dimensional defect contour region is obtained by combining the corresponding thickness information, and the volumes of all the pixel regions are accumulated to obtain the volume of the entire defect region.
The defect volume of a single pixel region can be calculated from equation (4):
V＝a2×(T0-T1) (4)
wherein:
V-Single Pixel region volume
a-pixel side length
T0Theoretical thickness of a single pixel area
T1-the actual thickness of the individual pixel areas.
And calculating to obtain the total volume of the defect area according to the formula (5):
wherein:
Valltotal volume of defect area
n-total number of pixels in defective area
Ti0Theoretical thickness of ith pixel region of defect region
Ti1-actual thickness of the ith pixel region of the defective region.
The invention relates to a digital ray-based three-dimensional parameter extraction method for internal defects of large high-temperature blades, which is based on the idea of finite elements and comprises the steps of firstly, carrying out one-time finite element division on a two-dimensional detection image of the defects according to pixel arrangement; then, performing discrete quantization on the thickness of each pixel according to the gray value, and determining the corresponding relation between the gray value and the thickness; finally, accumulating all pixel finite elements to extract the three-dimensional parameters of the defect, on one hand, the defect three-dimensional parameter extraction defect of the traditional ray detection method can be effectively overcome; on the other hand, compared with an industrial CT system, the method can realize the extraction of the three-dimensional parameters of the internal defects of the gas turbine blade with higher efficiency and lower cost.
Claims (7)
1. The method for extracting the three-dimensional parameters of the internal defects of the gas turbine blade based on the digital rays is characterized by comprising the following steps of firstly, carrying out primary finite element division on a two-dimensional detection image of the defects according to pixel arrangement; then, performing discrete quantization on the thickness of each pixel according to the gray value, and determining the corresponding relation between the gray G and the thickness T; and finally, accumulating finite elements of all pixels, and extracting three-dimensional parameters of the defect area, wherein the method comprises the following steps:
s1, determining a linear response range of a digital ray detection system, wherein the digital ray detection system comprises a ray source, a supporting platform for placing a detected blade, a flat panel detector and an imaging and control system, the ray source is connected to the imaging and control system through a ray source controller, the supporting platform is connected with the imaging and control system through a detection control system, and the flat panel detector is connected with the imaging and control system through a detector controller;
s2, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image, and acquiring a two-dimensional outline of the defect, wherein the acquiring of the two-dimensional outline of the defect specifically comprises the following steps:
s21, transilluminating the blade by using a digital ray detection system to obtain a two-dimensional detection image with the gray level in the range of the linear region of the system;
s22, extracting a defect boundary based on the blade detection image, and preliminarily obtaining a two-dimensional outline of the defect;
s23, performing morphological dilation on the defect two-dimensional outline preliminarily acquired in the step S22, and enlarging a defect area to ensure that all defect outlines are completely included in the morphologically dilated area;
s24, carrying out bicubic interpolation on the expansion area, and calculating the simulated background of the defect area;
s25, making a difference between the simulated background image and the original image, and performing binarization processing on the difference image to obtain a two-dimensional accurate outline of the defect;
s3, obtaining a relation curve of image gray scale and material thickness under specific transillumination parameters by transilluminating the test wedge block;
s4, converting the simulated background gray scale of the defect and the defect gray scale of the original image into thicknesses, and subtracting the thicknesses to obtain the defect thickness;
and S5, accumulating the thickness information of each defective pixel to obtain the three-dimensional parameters of the defect.
2. The method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 1, wherein the method comprises the following steps: in step S1, a lead plate with a central hole is disposed on the flat panel detector, the hole penetrates through the lead plate, the diameter of the hole is more than 1mm and less than 10mm, the transillumination parameters of the digital radiation detection system are increased to obtain different exposure amounts, an image formed by the flat panel detector at the collimation hole is changed from full black to high light and full white, the imaging gray levels of the digital radiation detection system corresponding to the different exposure amounts in the hole area are obtained, a response curve of the flat panel detector is drawn, and a linear response area of the digital radiation detection system is determined.
3. The method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 2, wherein the method comprises the following steps: the linear response relation of the digital ray detection system is as follows:
G＝α·H+b
wherein: g-imaging gray scale value, H-exposure, alpha-linear response region slope and b-imaging gray scale linear offset.
4. The method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 1, wherein the method comprises the following steps: in step S3, processing wedges for the same material test having the same thickness variation range as the divisional thickness variation range, each wedge using the same exposure parameters as the corresponding region, limiting the thickness of each region within a certain range by adopting a divisional transillumination mode, and performing a transillumination using a set of specific exposure parameters, wherein the transillumination parameters include tube voltage, tube current, and exposure time.
5. The method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 4, wherein the method comprises the following steps: in the one-time transillumination process, the effective exposure amount reaching the flat panel detector is reduced along with the increase of the transillumination thickness, and the functional relation between the imaging gray scale value G and the transillumination thickness T is determined according to the linear relation between the exposure amount H and the imaging gray scale value G as follows:
G＝f(T)。
6. the method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 1, wherein the method comprises the following steps: in step S5, the volume of each pixel region in the defect two-dimensional contour region is obtained according to the thickness information as follows:
V＝a2×(T0-T1)
wherein: v-volume of single pixel region, a-side length of pixel, T0Theoretical thickness of single pixel area, T1-the actual thickness of the individual pixel areas.
7. The method for extracting the internal defect three-dimensional parameters of the gas turbine blade based on the digital rays as claimed in claim 6, wherein the method comprises the following steps: the three-dimensional parameters of the defect region are calculated as follows:
wherein: vall-total volume of defective area, n-total number of pixels of defective area, Ti0Theoretical thickness of ith pixel region of defect region, Ti1-actual thickness of the ith pixel region of the defective region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710084115.7A CN106932416B (en) | 2017-02-16 | 2017-02-16 | Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710084115.7A CN106932416B (en) | 2017-02-16 | 2017-02-16 | Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106932416A CN106932416A (en) | 2017-07-07 |
| CN106932416B true CN106932416B (en) | 2019-10-11 |
Family
ID=59423607
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710084115.7A Expired - Fee Related CN106932416B (en) | 2017-02-16 | 2017-02-16 | Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106932416B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108072674B (en) * | 2018-02-07 | 2019-07-23 | 西安交通大学 | A kind of gas turbine blades defect three dimension location method based on digital radial |
| CN108229080B (en) * | 2018-03-26 | 2020-06-19 | 西安交通大学 | Optimization method for model unknown gas turbine blade digital ray subarea transillumination direction |
| CN109636800B (en) * | 2019-01-28 | 2022-10-14 | 中国科学院上海硅酸盐研究所 | Method for measuring size of internal defect of object |
| JP2021135125A (en) * | 2020-02-26 | 2021-09-13 | トヨタ自動車株式会社 | Inspection method and inspection device of membrane electrode assembly |
| CN111915569B (en) * | 2020-07-09 | 2022-04-22 | 西安交通大学 | Method, equipment and medium for screening digital radiographic image areas of free-form surface type parts |
| CN112730470B (en) * | 2020-12-24 | 2024-04-16 | 中国航发南方工业有限公司 | Method for determining X-ray detection blade trailing edge hole machining defect transillumination angle |
| CN113670958B (en) * | 2021-09-02 | 2022-12-06 | 西安交通大学 | Gas turbine blade defect identification method based on X-ray attenuation coefficient difference |
| CN114152637B (en) * | 2022-02-07 | 2022-04-26 | 东莞市志橙半导体材料有限公司 | Hard silicon carbide material punching detection device and method |
| CN117805123B (en) * | 2024-02-26 | 2024-07-09 | 西安交通大学 | Intelligent detection method for damage to surface of blade of gas turbine |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101556147A (en) * | 2009-05-19 | 2009-10-14 | 西北工业大学 | Method for measuring defect thickness in carbon/silicon carbide composite material |
| CN104655658A (en) * | 2015-02-10 | 2015-05-27 | 西安交通大学 | Large-sized high-temperature blade internal defect three-dimensional nondestructive detection method |
| CN104730091A (en) * | 2015-02-10 | 2015-06-24 | 西安交通大学 | Gas turbine blade defects extraction and analysis method based on region segmenting detection |
| CN105158280A (en) * | 2015-08-03 | 2015-12-16 | 赵建江 | Method for accurate detection of material defect and thickness by using digital radiography transillumination technology |
| CN205655808U (en) * | 2015-12-20 | 2016-10-19 | 赵建江 | Device of material thickness and defect height is measured with gasket and digital ray |
| CN106093080A (en) * | 2016-01-14 | 2016-11-09 | 南昌航空大学 | A kind of metal alloy compositions scattering ratio measuring method based on digital radial imaging technology explorer response curve |
| CN106353828A (en) * | 2015-07-22 | 2017-01-25 | 清华大学 | Method and device for estimating weight of inspected object in security inspection system |
| CN106370678A (en) * | 2016-08-23 | 2017-02-01 | 中国工程物理研究院激光聚变研究中心 | X-ray equivalence absorption method for measuring concentration of element doped in material |
-
2017
- 2017-02-16 CN CN201710084115.7A patent/CN106932416B/en not_active Expired - Fee Related
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101556147A (en) * | 2009-05-19 | 2009-10-14 | 西北工业大学 | Method for measuring defect thickness in carbon/silicon carbide composite material |
| CN104655658A (en) * | 2015-02-10 | 2015-05-27 | 西安交通大学 | Large-sized high-temperature blade internal defect three-dimensional nondestructive detection method |
| CN104730091A (en) * | 2015-02-10 | 2015-06-24 | 西安交通大学 | Gas turbine blade defects extraction and analysis method based on region segmenting detection |
| CN106353828A (en) * | 2015-07-22 | 2017-01-25 | 清华大学 | Method and device for estimating weight of inspected object in security inspection system |
| CN105158280A (en) * | 2015-08-03 | 2015-12-16 | 赵建江 | Method for accurate detection of material defect and thickness by using digital radiography transillumination technology |
| CN205655808U (en) * | 2015-12-20 | 2016-10-19 | 赵建江 | Device of material thickness and defect height is measured with gasket and digital ray |
| CN106093080A (en) * | 2016-01-14 | 2016-11-09 | 南昌航空大学 | A kind of metal alloy compositions scattering ratio measuring method based on digital radial imaging technology explorer response curve |
| CN106370678A (en) * | 2016-08-23 | 2017-02-01 | 中国工程物理研究院激光聚变研究中心 | X-ray equivalence absorption method for measuring concentration of element doped in material |
Non-Patent Citations (1)
| Title |
|---|
| Ｘ射线图像灰度值与透照厚度的定量关系;郭文明等;《无损检测》;20161231;摘要，1.3节，第3节，16页公式7 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106932416A (en) | 2017-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106932416B (en) | Gas turbine blades internal flaw three-dimensional parameter extracting method based on digital radial | |
| CN106845011B (en) | Large-scale gas turbine blade digital ray partition detection method | |
| CN1603836B (en) | Method for internal feature reconstruction | |
| US8184767B2 (en) | Imaging system and method with scatter correction | |
| CN105004611A (en) | Method for studying material mechanics behaviors of concrete crack process zone | |
| CN108388713B (en) | Self-adaptive segmentation and exposure parameter optimization method for gas turbine blade detection area | |
| US11249039B2 (en) | Method of measuring depth of defects in large-scale wind turbine blade using infrared thermography | |
| CN112991319B (en) | Carbon fiber laminated board infrared thermal imaging defect size detection method | |
| US20190172191A1 (en) | System and method for determining part damage | |
| CN113222937A (en) | Detection method and detection device for appearance peripheral surface defects of nuclear fuel pellet | |
| CN110060293A (en) | A kind of defect detection performance boundary appraisal procedure of CT detection system | |
| CN108229080A (en) | The optimization method in unknown-model gas turbine blades digital radial subregion transillumination direction | |
| CN104266900A (en) | CTOD (crack-tip opening displacement) measurement method based on high-temperature image processing | |
| CN112858351A (en) | X-ray nondestructive inspection method based on machine vision | |
| CN113884538B (en) | Infrared thermal image detection method for micro defects in large wind turbine blade | |
| CN111351452A (en) | Method for measuring wall thickness of leading edge of hollow turbine blade | |
| CN116223224B (en) | Method for detecting influence of curing agent on mechanical properties of product based on image processing | |
| Maurer et al. | Damage characterisation of short glass fibre reinforced polyamide with different fibre content by an interrupted in-situ X-Ray computed tomography test | |
| Li et al. | 3D detection of internal defects for gas turbine blades | |
| Högner et al. | Quantification of X-ray measurement uncertainty based on optical measurement data of turbine blades | |
| KR20140027911A (en) | Method and evaluation device for determining the position of a structure located in an object to be examined by means of x-ray computer tomography | |
| Brunke | Fully-automated 3D metrology and defect analysis with high-resolution 300 kV microfocus computed tomography | |
| CN110196026B (en) | Method for rapidly measuring and calculating relation between surface roughness and measurement scale | |
| Losano et al. | Computed tomography in the automotive field. Development of a new engine head case study | |
| CN114674252B (en) | Flatness detection system and method for protective middle frame of tablet personal computer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20191011 Termination date: 20220216 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |