CN115343365B - Test piece perfection rate detection method based on ultrasonic C-scanning digital image processing - Google Patents
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Abstract
The invention relates to a test piece perfection rate detection method based on ultrasonic C scanning digital image processing, which comprises the following steps: reading and processing image data of a tested piece to obtain two-dimensional amplitude data corresponding to the tested piece; step two: preprocessing and correcting the two-dimensional amplitude data of the tested piece; step three: judging whether the structural features of the tested piece are detected or not to obtain the integrity rate of the tested piece, and scanning the tested piece by ultrasonic C to obtain defect detailed information, further determining a test piece integrity rate detection mode according to the structural features of the test piece, and completing calculation of the integrity rate. The test piece perfection rate analysis method provided by the invention can be used for carrying out defect analysis on the test piece with the structural characteristics or not, calculating the perfection rate of the tested test piece, has the characteristics of high accuracy and wide applicability, and provides an effective solving way for image defect analysis.
Description
Technical Field
The application relates to the field of nondestructive testing of materials, in particular to a test piece perfection rate detection method based on ultrasonic C-scanning digital image processing.
Background
The ultrasonic C scanning imaging detection technology is an important nondestructive detection means for ensuring the quality of products, and is widely applied to the key fields of aviation, aerospace, nuclear industry and the like. In the ultrasonic C-scan detection process, defect analysis based on digital images is a key process for judging whether a detected object meets acceptance conditions.
With the continuous emergence of new materials, new processes and new structures, the defects and acceptance conditions of the materials are new. Taking superplastic forming diffusion connection and composite material parts as examples, the inside of the superplastic forming diffusion connection is easy to generate welding defect area type defects due to the reasons of complex manufacturing process, multiple influencing factors and the like, the defect shapes are often irregular, and the sizes, the number and the positions of the defects are also irregular; because of complex manufacturing process, special materials and structures, and the like, the interior of the composite material part is easy to have area defects such as layering, debonding and the like. In marked contrast to conventional equivalent weight assessment methods, superplastic forming diffusion joints and composite articles are more concerned with defect areas or the proportion of defects in the overall article.
Most of the software parts of the existing ultrasonic C-scanning detection system have a length measurement function, are suitable for size measurement of regular shape features, and few software parts have a defect area analysis function based on an amplitude threshold. However, for a product with complex internal structural features, defects and structural features in the C-scan image are difficult to distinguish from each other in amplitude; during ultrasonic C-scanning detection, system software often cannot analyze and process defects of other detection images, and the use requirement cannot be met.
The reference CN201510290015.0 is that ultrasonic C-scan detection is performed by setting two different detection sensitivities, a simulated complete debonding defect scan image and an actual debonding defect scan image of a surface to be detected of a product to be detected are obtained respectively, total pixels of the surface to be detected of the product to be detected and pixels of the debonding region are counted by phtoshop software, and the ratio of the total pixels to the debonding region is used as the ratio of the area of the debonding region to the total area of the surface to be detected of the product to be detected. This method is time consuming because it requires two tests on the same test product. Reference 201810322575.3 is to obtain pixels contained in each defect by using a binarized ultrasonic C-scan of a composite material product, so as to calculate the defect area, and both references consider the influence of structural features on the defect area calculation.
Different from the method, the test piece perfection rate analysis method based on ultrasonic C scanning digital image processing provided by the invention can be used for carrying out defect analysis on the test piece with or without structural characteristics, and calculating the perfection rate of the tested test piece, and has the advantages of high accuracy, flexible operation and wide applicability. Meanwhile, the method is proved to be effective in defect analysis by analyzing and processing a large number of ultrasonic C-scan images of typical tested pieces.
Disclosure of Invention
In order to overcome the defects in the prior art, the defect information is obtained by scanning a tested piece through ultrasonic C, the test piece is divided into two states of undetected structural features and detected structural features through the amplitude data of the test piece, the test piece with the structural features is divided into a key area and a non-key area, and the test piece perfection rate detection mode is further determined according to the structural features of the test piece, so that the calculation of the perfection rate is completed; the method for calculating the integrity rate of the tested piece has the characteristics of high accuracy, flexible operation and wide applicability.
In order to achieve the above object, the solution adopted by the present invention is:
a test piece perfection rate detection method based on ultrasonic C scanning digital image processing comprises the following steps:
step 1: reading and processing image data of a tested piece to obtain two-dimensional amplitude data corresponding to the tested piece;
placing the tested piece into the ultrasonic C for scanning to obtain a two-dimensional scanning image Pic (x, y) in an RGB format and a two-dimensional color band Col (m, n) thereof; converting RGB format two-dimensional scanning image Pic (x, y) into RGB mode three-dimensional image array Pic RGB (x, y, i), i=1, 2,3; converting the RGB format two-dimensional scanning color band Col (m, n) into a RGB mode three-dimensional color band array Col RGB (m,n,i)，i＝1,2,3；
Three-dimensional image array Pic of RGB mode RGB The xth row and yth column data Pic in each layer of (x, y, i) RGB (x,y,1)、Pic RGB (x, y, 2) and Pic RGB (x, y, 3) and the color bar data Col, respectively RGB (m, n, i) mth row and 1 st column data Col in each layer RGB (m,1,1)、Col RGB (m, 1, 2) and Col RGB (m, 1, 3) comparing and searching for Col RGB (m, 1, i) and Pic RGB Corresponding rows when the values of (x, y, i) are the sameNumber M i (i=1, 2, 3), the calculation procedure is as follows:
wherein: m is M 1 、M 2 And M 3 Respectively representing corresponding row numbers when the image array and the color bar array in the 1 st layer, the 2 nd layer and the 3 rd layer are the same in value, and each M i The number of the middle line numbers is at least 1; pic (Pic) RGB (x, y, i) represents the pixel values of the x-th row and the y-th column of the i-th layer in the three-dimensional image array; col RGB (m, 1, i) represents the pixel value of the mth layer in the three-dimensional color bar array; x and y represent the row number and column number of the image array, respectively; find represents a find function, assigning the row number in the array where the value of the condition listed in brackets is located to M on the left side of the equal number i The method comprises the steps of carrying out a first treatment on the surface of the i represents the number of layers in the three-dimensional image array; m represents the pixel number in the three-dimensional color bar array;
three-dimensional image data Pic RGB The two-dimensional amplitude data corresponding to the (x, y, i) x-th row and y-th column data are as follows:
a(x,y)＝Amp(M)；
wherein: a (x, y) represents two-dimensional amplitude data of three-dimensional image data at an x-th row and a y-th column; amp represents the color band array Col RGB Amplitude values corresponding to the line numbers in (m, 1, i) are in the range of [0,100]]The method comprises the steps of carrying out a first treatment on the surface of the M represents a three-dimensional color band array Col RGB (m, 1, i) RGB data and three-dimensional image array Pic RGB The row numbers corresponding to the same RGB data of the (x, y, i) x row and the y column are represented by M 1 、M 2 And M 3 Acquiring an intersection;
processing a three-dimensional image array Pic RGB Each of the xth row and the yth column data of each layer in (x, y, i) for a three-dimensional image array Pic RGB Performing amplitude conversion on the data in (x, y, i) to obtain corresponding two-dimensional amplitude data a (x, y);
step 2: preprocessing and correcting the two-dimensional amplitude data of the tested piece;
acquiring the two-dimensional amplitude data obtained in the step 1, wherein the two-dimensional amplitude data is obtained in the two dimensions of a 5 multiplied by 5 neighborhood blockThe value filtering processes the imported two-dimensional amplitude data a (x, y), so that speckle noise and salt and pepper noise in the image data are reduced, and preprocessing operation is realized; the image is corrected based on the rotated image to obtain corrected two-dimensional amplitude data A (x θ ,y θ ) The calculation process is as follows:
wherein: θ represents a correction angle of the image; x is x θ And y θ Respectively representing two-dimensional amplitude data A (x θ ,y θ ) Row and column numbers of (a); a (x) θ ,y θ ) Representing two-dimensional amplitude data at the x-th θ Line, y θ Amplitude of the columns;
step 3: judging whether the tested piece detects structural characteristics or not to obtain the integrity rate of the tested piece;
when no structural feature is detected, a non-structural feature integrity rate calculation is performed, and two-dimensional amplitude data A (x θ ,y θ ) The number of defective pixels and the number of regional integral pixels in the range of the defect amplitude threshold value are calculated, and the ratio of the number of defective pixels and the number of regional integral pixels is used as the perfection rate alpha of the tested piece without structural features 1 The method comprises the steps of carrying out a first treatment on the surface of the When the structural features exist in the detected image, the corrected image is overlapped with the design parameters of the tested piece, and the two-dimensional amplitude data A (x θ ,y θ ) Automatically acquiring the boundary of the related region, thereby calculating the perfection rate alpha of the key region c Integrity rate alpha of non-critical area nc Integrity rate alpha of a tested piece with structural characteristics 2 And finally obtaining the perfection rate of the tested piece.
Preferably, the two-dimensional scanned image Pic (x, y) and the two-dimensional color bar Col (m, n) in the step 1 are specifically:
the two-dimensional scanning image Pic (x, y) represents the pixel colors of the x-th row and the y-th column of the ultrasonic C-scanning image; the two-dimensional color bar Col (m, n) represents the pixel colors of the m-th row and the n-th column in the color bar, and the amplitude Amp range corresponding to different colors in the color bar is [0,100].
Preferably, the three-dimensional image array and the three-dimensional color bar array of the RGB mode in the step 1 are specifically obtained by the following ways:
reading in an ultrasonic C-scan image Pic (x, y), wherein the read-in data is a three-dimensional image array Pic in an RGB mode RGB (x,y,i)，i＝1,2,3；Pic RGB (x, y, i) represents the values of the x-th row and y-th column of the i-th layer; layer 1 data Pic RGB (x, y, 1) is the value of R, layer 2 data Pic RGB (x, y, 2) is the value of G, layer 3 data Pic RGB (x, y, 3) is a number of B;
color band Col (m, n) of ultrasonic C scanning image is read in, and the read-in data is a three-dimensional color band array Col of RGB mode RGB (m,n,i)，i＝1,2,3；Col RGB (m, n, i) represents the values of the m-th row and n-th column of the i-th layer; layer 1 data Col RGB (m, n, 1) is the value of R, layer 2 data Col RGB (m, n, 2) is the value of G, layer 3 data Col RGB (m, n, 3) is a number of B; the reason data Col RGB (m, n, i) the same column of values in all layers is the same, only column 1 values Col in each layer are taken RGB (m,1,i)。
Preferably, in the step 1, M is 1 、M 2 And M 3 Taking intersections, as follows:
M＝M 1 ∩M 2 ∩M 3 ；
wherein: m represents a three-dimensional color band array Col RGB (m, 1, i) RGB data and three-dimensional image array Pic RGB And (x, y, i) the row number corresponding to the same RGB data of the x-th row and the y-th column.
Preferably, in the step 3, when no structural feature is detected, the calculation of the structural feature-free integrity rate is performed, specifically:
two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) Counting the number N of the whole pixelsThe following formula is shown:
N＝∑A′(x θ ,y θ )；
wherein: n represents the number of overall pixels; a' (x θ ,y θ ) Representing the sum A (x θ ,y θ ) Two-dimensional data obtained after setting the value of '1';
threshold range phi corresponding to defect amplitude F Two-dimensional amplitude data A (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein:representing a defect binarization matrix; phi F Representing a threshold range corresponding to the defect amplitude;
counting the number N of defective pixels F The following is shown:
wherein: n (N) F Representing the number of defective pixels;
integrity rate alpha of test piece without structural feature 1 The expression of (2) is:
wherein: alpha 1 And the integrity rate of the tested piece without the structural characteristics is shown.
Preferably, in the step 3, when the structural feature is detected, structural feature integrity rate calculation is performed, specifically:
according to structural characteristics and key areas in design parameters of tested partsIn a coordinate system corresponding to a boundary of a non-critical region, the two-dimensional amplitude data A (x θ ,y θ ) Is used for extracting structural feature region (x As ,y As ) Critical area (x) Ac ,y Ac ) And non-critical areas (x Anc ,y Anc )；
To obtain (x) As ,y As )、(x Ac ,y Ac )、(x Anc ,y Anc ) The data are stored, and the stored data can be used for automatically importing the structural characteristics, the key areas and the non-key areas of the tested parts in the same batch; two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) The method comprises the steps of carrying out a first treatment on the surface of the Respectively counting the pixel quantity N of the critical area and the non-critical area c 、N nc The calculation method is as follows:
wherein: n (N) c Representing the number of pixels in the key region; n (N) nc Representing the number of non-critical area pixels; omega shape c Representing boundary (x) Ac ,y Ac ) An enclosed area; omega shape nc Representing boundary (x) Anc ,y Anc ) An enclosed area;
two-dimensional amplitude data A (x θ ,y θ ) Middle-to-middle boundary (x) As ,y As ) Surrounding an area omega s The value of "0" is set to obtain the dimension and A (x θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) Eliminating the influence of structural features on the defect area statistical result; on the basis, a threshold value range phi corresponding to the defect amplitude is set F Two-dimensional data A' (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein: a' (x) θ ,y θ ) Representing two-dimensional data after removing structural features;
counting the number N of defective pixels in the critical area and the non-critical area respectively Fc And N Fnc The calculation method is as follows:
wherein: n (N) Fc A number of pixels representing defects in the critical area; n (N) Fnc A number of pixels representing defects in the non-critical area;
calculating the perfection rate alpha of the tested piece in the critical area, the non-critical area and the structural characteristics c 、α nc And alpha 2 The following is shown:
wherein: alpha c And alpha nc The integrity rates of test pieces in the key area and the non-key area are respectively represented; alpha 2 Indicating the integrity of the part under test when there are structural features.
Preferably, the method comprises the step of generating the two-dimensional amplitude data a (x θ ,y θ ) Is used for extracting structural feature region (x As ,y As ) Critical area (x) Ac ,y Ac ) And non-critical areas (x Anc ,y Anc ) The method specifically comprises the following steps:
according to the structural characteristics, the coordinate system corresponding to the critical area and the non-critical area boundary in the design parameters of the tested piece, the two-dimensional wave amplitude data A (x θ ,y θ ) Extracting structural features, key areas and non-key areas;
the coordinate acquisition method corresponding to the boundary of the structural feature region is as follows:
wherein: x is x s And y s Respectively representing the corresponding abscissa and ordinate of the boundary of the structural feature region in the design parameters; x is x As And y As Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle structure characteristic region corresponds to an abscissa and an ordinate; l and W are the length and width of the design parameters, respectively; p and Q are two-dimensional amplitude data A (x θ ,y θ ) Total number of rows and total number of columns;
the coordinate acquisition method corresponding to the key region boundary is as follows:
wherein: x is x c And y c Respectively representing the corresponding abscissa and ordinate of the boundary of the key region in the design parameters; x is x Ac And y Ac Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle key region corresponds to an abscissa and an ordinate;
the coordinate acquisition method corresponding to the non-key region boundary is as follows:
wherein: x is x nc And y nc Respectively representing corresponding coordinates of non-key region boundaries in design parameters; x is x Anc And y Anc Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The non-critical region boundary corresponds to coordinates.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, the defect detailed information is obtained by scanning the tested piece through ultrasonic C, the test piece is divided into two states of undetected structural features and detected structural features through the amplitude data of the test piece, the test piece with the structural features is divided into a key region and a non-key region, a scientific test piece perfection rate detection mode is further determined according to the structural features of the test piece, and calculation of perfection rate is completed;
(2) The test piece perfection rate analysis method provided by the method can be used for carrying out defect analysis on the test piece with the structural characteristics or not, calculating the perfection rate of the tested test piece, has high accuracy, flexible operation and wide applicability, and provides an effective solution for defect analysis of the ultrasonic C scanning image.
Drawings
FIG. 1 is a control block diagram of a test piece integrity analysis method based on ultrasonic C-scan digital image processing in an embodiment of the invention;
FIG. 2 is a flowchart of a test piece integrity analysis method based on ultrasonic C-scan digital image processing in accordance with an embodiment of the present invention;
FIG. 3 is an ultrasonic C-scan image of a defect-free superplastic forming diffusion joint test piece according to an embodiment of the present invention;
FIG. 4 is an ultrasonic C-scan image of a defective superplastic forming diffusion joint test piece according to an embodiment of the present invention;
FIG. 5 is a picture import of a flow of analysis of an ultrasonic C-scan image of a superplastic forming diffusion joint test piece according to an embodiment of the present invention;
FIG. 6 is a graph showing the results of selecting structural parameters, critical areas and non-critical areas after image correction in the process of analyzing ultrasonic C-scan images of a superplastic forming diffusion connecting test piece according to an embodiment of the present invention;
FIG. 7 is a defect-free superplastic forming diffusion joint test piece defect threshold input and technical results of a flow of analysis of ultrasonic C-scan images of superplastic forming diffusion joint test pieces according to an embodiment of the present invention;
FIG. 8 is a graph showing the defective superplastic forming diffusion joint test piece defect threshold input and technical results of a process of analyzing ultrasonic C-scan images of the superplastic forming diffusion joint test piece according to an embodiment of the present invention;
FIG. 9 is a C-scan image of a carbon fiber composite impact test piece according to an embodiment of the present invention;
FIG. 10 is an analysis result of a carbon fiber composite impact test piece according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
According to the embodiment of the invention, the typical superplastic forming diffusion connection test piece and the carbon fiber composite material impact test piece are scanned by the ultrasonic C to obtain detailed information of the test piece, the test piece is divided into two states of undetected structural features and detected structural features by the amplitude data of the test piece, the test piece with the structural features is divided into a key area and a non-key area, the integrity rate detection mode of the tested test piece is further determined according to the structural features of the test piece, and calculation of the integrity rate is completed. In the invention, the integrity of the tested piece refers to the proportion of the non-defective area in the specific area of the tested piece, wherein the specific area is a part of an integrity calculation formula and is different for different objects. For example, a first type of part under test, the specific area including a critical area, a non-critical area, and an entire area of the part under test; the second type of test piece, the specific area is the whole area of the test piece. The specific region may be a clear region defined according to the specific detection requirements. The test piece integrity analysis method provided by the embodiment of the invention can be used for carrying out defect analysis on the test piece with the structural characteristics, and the integrity of the tested piece calculated by the method can be proved by comparing and analyzing the data of the settlement result, so that the test piece integrity analysis method has the characteristic of high accuracy; the method is flexible in operation and high in applicability, and an effective solving way is provided for defect analysis of the ultrasonic C scanning image. Fig. 1 is a control block diagram of a test piece integrity analysis method based on ultrasonic C-scan digital image processing according to an embodiment of the present invention.
The embodiment of the invention provides a test piece perfection rate detection method based on ultrasonic C scanning digital image processing, and a flow chart of the test piece perfection rate analysis method in the embodiment of the invention is shown in fig. 2; to demonstrate the applicability of the invention, it is applied to examples, comprising in particular the following steps:
s1: reading and processing image data of a tested piece to obtain two-dimensional amplitude data corresponding to the tested piece;
to-be-tested piecePlacing the image into an ultrasonic C for scanning to obtain a two-dimensional scanning image Pic (x, y) in an RGB format and a two-dimensional color band Col (m, n) thereof; converting RGB format two-dimensional scanning image Pic (x, y) into RGB mode three-dimensional image array Pic RGB (x, y, i), i=1, 2,3; converting the RGB format two-dimensional scanning color band Col (m, n) into a RGB mode three-dimensional color band array Col RGB (m,n,i)，i＝1,2,3；
The two-dimensional scanning image Pic (x, y) represents the pixel colors of the x-th row and the y-th column of the ultrasonic C-scanning image; the two-dimensional color bar Col (m, n) represents the pixel colors of the m-th row and the n-th column in the color bar, and the amplitude Amp range corresponding to different colors in the color bar is [0,100]. FIG. 3 is an ultrasonic C-scan image of a defect-free superplastic forming diffusion joint test piece according to an embodiment of the present invention; an ultrasonic C-scan image of a defective superplastic forming diffusion joint test piece according to an embodiment of the invention is shown in FIG. 4.
Reading in an ultrasonic C-scan image Pic (x, y), wherein the read-in data is a three-dimensional image array Pic in an RGB mode RGB (x,y,i)，i＝1,2,3；Pic RGB (x, y, i) represents the values of the x-th row and y-th column of the i-th layer; layer 1 data Pic RGB (x, y, 1) is the value of R, layer 2 data Pic RGB (x, y, 2) is the value of G, layer 3 data Pic RGB (x, y, 3) is a number of B;
color band Col (m, n) of ultrasonic C scanning image is read in, and the read-in data is a three-dimensional color band array Col of RGB mode RGB (m,n,i)，i＝1,2,3；Col RGB (m, n, i) represents the values of the m-th row and n-th column of the i-th layer; layer 1 data Col RGB (m, n, 1) is the value of R, layer 2 data Col RGB (m, n, 2) is the value of G, layer 3 data Col RGB (m, n, 3) is a number of B; the reason data Col RGB (m, n, i) the same column of values in all layers is the same, only column 1 values Col in each layer are taken RGB (m, 1, i). FIG. 5 is a graphical illustration of a process for analyzing ultrasonic C-scan images of a superplastic forming diffusion joint test piece according to an embodiment of the present invention.
Three-dimensional image array Pic of RGB mode RGB The xth row and yth column data Pic in each layer of (x, y, i) RGB (x,y,1)、Pic RGB (x, y, 2) and Pic RGB (x, y, 3) and the color bar data C respectivelyol RGB (m, n, i) mth row and 1 st column data Col in each layer RGB (m,1,1)、Col RGB (m, 1, 2) and Col RGB (m, 1, 3) comparing and searching for Col RGB (m, 1, i) and Pic RGB Corresponding line number M when the values of (x, y, i) are the same i (i=1, 2, 3), the calculation procedure is as follows:
wherein: m is M 1 、M 2 And M 3 Respectively representing corresponding row numbers when the image array and the color bar array in the 1 st layer, the 2 nd layer and the 3 rd layer are the same in value, and each M i The number of the middle line numbers is at least 1; pic (Pic) RGB (x, y, i) represents the pixel values of the x-th row and the y-th column of the i-th layer in the three-dimensional image array; col RGB (m, 1, i) represents the pixel value of the mth layer in the three-dimensional color bar array; x and y represent the row number and column number of the image array, respectively; find represents a find function, assigning the row number in the array where the value of the condition listed in brackets is located to M on the left side of the equal number i The method comprises the steps of carrying out a first treatment on the surface of the i represents the number of layers in the three-dimensional image array; m represents the pixel number in the three-dimensional color bar array;
for M 1 、M 2 And M 3 Taking intersections, as follows:
M＝M 1 ∩M 2 ∩M 3 ；
wherein: m represents a three-dimensional color band array Col RGB (m, 1, i) RGB data and three-dimensional image array Pic RGB And (x, y, i) the row number corresponding to the same RGB data of the x-th row and the y-th column.
Three-dimensional image data Pic RGB The two-dimensional amplitude data corresponding to the (x, y, i) x-th row and y-th column data are as follows:
a(x,y)＝Amp(M)；
wherein: a (x, y) represents two-dimensional amplitude data of three-dimensional image data at an x-th row and a y-th column; amp represents the color band array Col RGB Amplitude values corresponding to the line numbers in (m, 1, i) are in the range of [0,100]]The method comprises the steps of carrying out a first treatment on the surface of the M represents a three-dimensional color band array Col RGB (m,1RGB data of i) and three-dimensional image array Pic RGB (x, y, i) a row number corresponding to the same RGB data of the x-th row and the y-th column;
processing a three-dimensional image array Pic RGB Each of the xth row and the yth column data of each layer in (x, y, i) for a three-dimensional image array Pic RGB Performing amplitude conversion on the data in (x, y, i) to obtain corresponding two-dimensional amplitude data a (x, y);
s2: preprocessing and correcting the two-dimensional amplitude data of the tested piece;
acquiring two-dimensional amplitude data obtained in the step S1, and processing the imported two-dimensional amplitude data a (x, y) by adopting two-dimensional median filtering of a 5X 5 neighborhood block, so as to reduce speckle noise and salt and pepper noise in the image data and realize preprocessing operation; the image is corrected based on the rotated image to obtain corrected two-dimensional amplitude data A (x θ ,y θ ) The calculation process is as follows:
wherein: θ represents a correction angle of the image; x is x θ And y θ Respectively representing two-dimensional amplitude data A (x θ ,y θ ) Row and column numbers of (a); a (x) θ ,y θ ) Representing two-dimensional amplitude data at the x-th θ Line, y θ Amplitude of the columns;
s3: judging whether the tested piece detects structural characteristics or not to obtain the integrity rate of the tested piece;
the structural characteristics of the invention are that the amplitude and the depth in the ultrasonic C-scan image are displayed in the same way as the defects, and the rest defects cannot be distinguished through the adjustment of an amplitude threshold value and a depth gate; the structural characteristics comprise that the amplitude and the depth in the ultrasonic C-scan image are the same as those of the defect; when no structural feature is detected, a non-structural feature integrity rate calculation is performed, and two-dimensional amplitude data A (x θ ,y θ ) The number of defective pixels and the number of regional whole pixels in the range of the defect amplitude threshold value are calculated, and the ratio of the number of defective pixels and the number of regional whole pixels is taken as a non-structureIntegrity rate alpha of tested piece during characteristic 1 ；
Two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) The number of overall pixels N is counted as shown in the following formula:
N＝∑A′(x θ ,y θ )；
wherein: n represents the number of overall pixels; a' (x θ ,y θ ) Representing the sum A (x θ ,y θ ) Two-dimensional data obtained after setting the value of '1';
threshold range phi corresponding to defect amplitude F Two-dimensional amplitude data A (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein:representing a defect binarization matrix; phi F Representing a threshold range corresponding to the defect amplitude;
counting the number N of defective pixels F The following is shown:
wherein: n (N) F Representing the number of defective pixels;
integrity rate alpha of test piece without structural feature 1 The expression of (2) is:
wherein:α 1 and the integrity rate of the tested piece without the structural characteristics is shown.
When the structural features exist in the detected image, the corrected image is overlapped with the design parameters of the tested piece, and the two-dimensional amplitude data A (x θ ,y θ ) Automatically acquiring the boundary of the related region, thereby calculating the perfection rate alpha of the key region c Integrity rate alpha of non-critical area nc Integrity rate alpha of a tested piece with structural characteristics 2 And finally obtaining the perfection rate of the tested piece. Fig. 6 shows the result of selecting the structural parameters, the critical areas and the non-critical areas after image correction in the process of analyzing the ultrasonic C-scan image of the superplastic forming diffusion test piece according to the embodiment of the invention.
According to the structural characteristics, the coordinate system corresponding to the critical area and the non-critical area boundary in the design parameters of the tested piece, the two-dimensional wave amplitude data A (x θ ,y θ ) Is used for extracting structural feature region (x As ,y As ) Critical area (x) Ac ,y Ac ) And non-critical areas (x Anc ,y Anc )；
The coordinate acquisition method corresponding to the boundary of the structural feature region is as follows:
wherein: x is x s And y s Respectively representing the corresponding abscissa and ordinate of the boundary of the structural feature region in the design parameters; x is x As And y As Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle structure characteristic region corresponds to an abscissa and an ordinate; l and W are the length and width of the design parameters, respectively; p and Q are two-dimensional amplitude data A (x θ ,y θ ) Total number of rows and total number of columns;
the coordinate acquisition method corresponding to the boundary of the key area is as follows:
wherein: x is x c And y c Respectively representing the corresponding abscissa and ordinate of the boundary of the key region in the design parameters; x is x Ac And y Ac Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle key region corresponds to an abscissa and an ordinate;
the coordinate acquisition method corresponding to the non-critical area boundary is as follows:
wherein: x is x nc And y nc Respectively representing corresponding coordinates of non-key region boundaries in design parameters; x is x Anc And y Anc Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The non-critical region boundary corresponds to coordinates.
To obtain (x) As ,y As )、(x Ac ,y Ac )、(x Anc ,y Anc ) The data are stored, and the stored data can be used for automatically importing the structural characteristics, the key areas and the non-key areas of the tested parts in the same batch; two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) The method comprises the steps of carrying out a first treatment on the surface of the Respectively counting the pixel quantity N of the critical area and the non-critical area c 、N nc The calculation method is as follows:
wherein: n (N) c Representing the number of pixels in the key region; n (N) nc Representing the number of non-critical area pixels; omega shape c Representing boundary (x) Ac ,y Ac ) An enclosed area; omega shape nc Representing boundary (x) Anc ,y Anc ) An enclosed area;
two-dimensional amplitude data A (x θ ,y θ ) Middle-to-middle boundary (x) As ,y As ) Surrounding an area omega s The value of "0" is set to obtain the dimension and A (x θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) Eliminating the influence of structural features on the defect area statistical result; on the basis, a threshold value range phi corresponding to the defect amplitude is set F Two-dimensional data A' (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein: a' (x) θ ,y θ ) Representing two-dimensional data after removing structural features;
counting the number N of defective pixels in the critical area and the non-critical area respectively Fc And N Fnc The calculation method is as follows:
wherein: n (N) Fc A number of pixels representing defects in the critical area; n (N) Fnc A number of pixels representing defects in the non-critical area;
calculating the perfection rate alpha of the tested piece in the critical area, the non-critical area and the structural characteristics c 、α nc And alpha 2 The following is shown:
wherein: alpha c And alpha nc The integrity rates of test pieces in the key area and the non-key area are respectively represented; alpha 2 Indicating the integrity of the part under test when there are structural features.
And finally obtaining the perfection rate of the tested piece. FIG. 7 is a graph showing defect threshold inputs and technical results of a defect-free superplastic forming diffusion joint test piece according to an embodiment of the present invention; FIG. 8 shows the defective threshold inputs and technical results of a defective superplastic forming diffusion joint test piece according to an embodiment of the invention.
And setting the defect threshold range as [90,100], respectively counting the number of pixels of the critical area, the non-critical area, the whole tested piece and defects therein, and respectively calculating the perfection rates of the critical area, the non-critical area and the whole. The results of the superplastic forming diffusion joint test piece calculation with no defects are shown in fig. 7 and 8, respectively. The critical area, the non-critical area and the overall integrity of the tested test piece of the non-defective superplastic forming diffusion connection test piece are respectively 99.83%, 99.22% and 99.36%; the critical area, the non-critical area and the overall integrity of the tested test piece of the defective superplastic forming diffusion connection test piece are respectively 99.73%, 96.20% and 96.90%.
The calculation procedure for the second test piece is as follows; FIG. 9 is a C-scan image of a carbon fiber composite impact test piece according to an embodiment of the present invention; FIG. 10 shows the results of the carbon fiber composite impact test piece according to the embodiment of the present invention. Setting the defect threshold range to [0,30]And respectively counting the whole tested piece and the number of pixels of defects in the tested piece, and calculating the perfection rate. The integrity rate of the carbon fiber composite impact test piece is 99.26%. When the area of the area to be analyzed of the test piece is known, the defect area can be calculated according to the perfection rate, for example, the area of the carbon fiber composite material impacting the test piece is 15000.00mm 2 From this, it was found that the impact damage area was 111.00mm 2 。
Tables 1 and 2 show the relevant data for calculating the integrity rates of the first test piece and the second test piece, respectively, using the present application. In contrast, the first test piece and the second test piece were measured for their integrity using the length measurement function of the software portion of the commercial ultrasonic C-scan test system, and the calculation results are shown in table 3. Comparing the table 1, the table 2 and the table 3 respectively, because the defect shape is irregular, the defect area measured by the length measuring function of the commercial software is larger, the integrity rate of the tested piece is lower, and the calculation is inaccurate; in addition, for the first test piece, the influence of the structural features cannot be removed by using commercial software, and the integrity rates of the critical area and the non-critical area cannot be calculated respectively.
TABLE 1 calculation results of the first defective test piece of the present invention application
TABLE 2 calculation results of the second test piece of the present invention application
| Number of overall pixels, N | Number of defective pixels, N F | Integrity rate of the test piece, alpha | |
| Second test piece | 207074 | 1528 | 99.26％ |
Table 3 results of the first and second test piece commercial software measurements
In summary, the calculation result of the test piece perfection rate detection method based on ultrasonic C scanning digital image processing proves that the method has a good effect.
(1) According to the embodiment of the invention, the typical superplastic forming diffusion connection test piece and the carbon fiber composite material impact test piece are scanned by ultrasonic C to obtain detailed information of the test piece, the test piece is divided into two states of undetected structural features and detected structural features by the amplitude data of the test piece, the test piece with the structural features is divided into a key area and a non-key area, a scientific test piece perfection rate detection mode is further determined according to the structural features of the test piece, and calculation of perfection rate is completed;
(2) The test piece integrity analysis method provided by the embodiment of the invention can be used for carrying out defect analysis on the test piece with the structural characteristics, and the integrity of the tested piece calculated by the method can be proved by comparing and analyzing the data of the settlement result, so that the test piece integrity analysis method has the characteristic of high accuracy; the method is flexible in operation and high in applicability, and an effective solving way is provided for defect analysis of the ultrasonic C scanning image.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (5)
1. The test piece perfection rate detection method based on ultrasonic C-scan digital image processing is characterized by comprising the following steps:
step 1: reading and processing image data of a tested piece to obtain two-dimensional amplitude data corresponding to the tested piece;
placing the tested piece into the ultrasonic C for scanning to obtain a two-dimensional scanning image Pic (x, y) in an RGB format and a two-dimensional color band Col (m, n) thereof; two-dimensional scanning image in RGB formatPic (x, y) is converted into RGB mode three-dimensional image array Pic RGB (x, y, i), i=1, 2,3; converting the RGB format two-dimensional scanning color band Col (m, n) into a RGB mode three-dimensional color band array Col RGB (m,n,i)，i＝1,2,3；
Three-dimensional image array Pic of RGB mode RGB The xth row and yth column data Pic in each layer of (x, y, i) RGB (x,y,1)、Pic RGB (x, y, 2) and Pic RGB (x, y, 3) and the color bar data Col, respectively RGB (m, n, i) mth row and 1 st column data Col in each layer RGB (m,1,1)、Col RGB (m, 1, 2) and Col RGB (m, 1, 3) comparing and searching for Col RGB (m, 1, i) and Pic RGB Corresponding line number M when the values of (x, y, i) are the same i I=1, 2,3, the calculation procedure is as follows:
wherein: m is M 1 、M 2 And M 3 Respectively representing corresponding row numbers when the image array and the color bar array in the 1 st layer, the 2 nd layer and the 3 rd layer are the same in value, and each M i The number of the middle line numbers is at least 1; pic (Pic) RGB (x, y, i) represents the pixel values of the x-th row and the y-th column of the i-th layer in the three-dimensional image array; col RGB (m, 1, i) represents the pixel value of the mth layer in the three-dimensional color bar array; x and y represent the row number and column number of the image array, respectively; find represents a find function, assigning the row number in the array where the value of the condition listed in brackets is located to M on the left side of the equal number i The method comprises the steps of carrying out a first treatment on the surface of the i represents the number of layers in the three-dimensional image array; m represents the pixel number in the three-dimensional color bar array;
three-dimensional image data Pic RGB The two-dimensional amplitude data corresponding to the (x, y, i) x-th row and y-th column data are as follows:
a(x,y)＝Amp(M)；
wherein: a (x, y) represents two-dimensional amplitude data of three-dimensional image data at an x-th row and a y-th column; amp represents the color band array Col RGB Amplitude values corresponding to the line numbers in (m, 1, i) are in the range of [0,100]]The method comprises the steps of carrying out a first treatment on the surface of the M represents a three-dimensional color band array Col RGB (m, 1, i) RGB data and three-dimensional image array Pic RGB The row numbers corresponding to the same RGB data of the (x, y, i) x row and the y column are represented by M 1 、M 2 And M 3 Acquiring an intersection;
processing a three-dimensional image array Pic RGB Each of the xth row and the yth column data of each layer in (x, y, i) for a three-dimensional image array Pic RGB Performing amplitude conversion on the data in (x, y, i) to obtain corresponding two-dimensional amplitude data a (x, y);
step 2: preprocessing and correcting the two-dimensional amplitude data of the tested piece;
acquiring the two-dimensional amplitude data obtained in the step 1, and processing the imported two-dimensional amplitude data a (x, y) by adopting two-dimensional median filtering of a 5X 5 neighborhood block, so as to reduce speckle noise and salt and pepper noise in the image data and realize preprocessing operation; the image is corrected based on the rotated image to obtain corrected two-dimensional amplitude data A (x θ ,y θ ) The calculation process is as follows:
wherein: θ represents a correction angle of the image; x is x θ And y θ Respectively representing two-dimensional amplitude data A (x θ ,y θ ) Row and column numbers of (a); a (x) θ ,y θ ) Representing two-dimensional amplitude data at the x-th θ Line, y θ Amplitude of the columns;
step 3: judging whether the tested piece detects structural characteristics or not to obtain the integrity rate of the tested piece;
when no structural feature is detected, a non-structural feature integrity rate calculation is performed, and two-dimensional amplitude data A (x θ ,y θ ) The number of defective pixels and the number of regional integral pixels in the range of the defect amplitude threshold value are calculated, and the ratio of the number of defective pixels and the number of regional integral pixels is used as the perfection rate alpha of the tested piece without structural features 1 The method comprises the steps of carrying out a first treatment on the surface of the When the structural features exist in the detected image, as the corrected image coincides with the design parameters of the tested piece,according to the structural characteristics of the design parameters of the tested piece, the coordinates in the critical area and the non-critical area, the two-dimensional wave amplitude data A (x θ ,y θ ) Automatically acquiring the boundary of the related region, thereby calculating the perfection rate alpha of the key region c Integrity rate alpha of non-critical area nc Integrity rate alpha of a tested piece with structural characteristics 2 Finally obtaining the perfection rate of the tested piece;
and when the structural features are not detected in the step 3, executing the calculation of the structural feature-free integrity rate, wherein the calculation specifically comprises the following steps:
two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) The number of overall pixels N is counted as shown in the following formula:
N＝∑A′(x θ ,y θ )；
wherein: n represents the number of overall pixels; a' (x θ ,y θ ) Representing the sum A (x θ ,y θ ) Two-dimensional data obtained after setting the value of '1';
threshold range phi corresponding to defect amplitude F Two-dimensional amplitude data A (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein:representing a defect binarization matrix; phi F Representing a threshold range corresponding to the defect amplitude;
counting the number N of defective pixels F The following is shown:
wherein: n (N) F Representing the number of defective pixels;
integrity rate alpha of test piece without structural feature 1 The expression of (2) is:
wherein: alpha 1 The integrity rate of the tested piece without structural characteristics is shown;
and when the structural feature is detected in the step 3, executing structural feature perfection rate calculation, specifically:
according to the structural characteristics, the coordinate system corresponding to the critical area and the non-critical area boundary in the design parameters of the tested piece, the two-dimensional wave amplitude data A (x θ ,y θ ) Is used for extracting structural feature region (x As ,y As ) Critical area (x) Ac ,y Ac ) And non-critical areas (x Anc ,y Anc )；
To obtain (x) As ,y As )、(x Ac ,y Ac )、(x Anc ,y Anc ) Storing data, wherein the stored data are used for automatically importing structural features, key areas and non-key areas of tested pieces in the same batch; two-dimensional amplitude data A (x θ ,y θ ) The value of "1" is set to obtain values of "1", dimension and A (x) θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) The method comprises the steps of carrying out a first treatment on the surface of the Respectively counting the pixel quantity N of the critical area and the non-critical area c 、N nc The calculation method is as follows:
wherein: n (N) c Representing the number of pixels in the key region; n (N) nc Representing the number of non-critical area pixels; omega shape c Representing boundary (x) Ac ,y Ac ) An enclosed area; omega shape nc Representing boundary (x) Anc ,y Anc ) An enclosed area;
two-dimensional amplitude data A (x θ ,y θ ) Middle-to-middle boundary (x) As ,y As ) Surrounding an area omega s The value of "0" is set to obtain the dimension and A (x θ ,y θ ) Identical two-dimensional data A' (x θ ,y θ ) Eliminating the influence of structural features on the defect area statistical result; on the basis, a threshold value range phi corresponding to the defect amplitude is set F Two-dimensional data A' (x θ ,y θ ) The medium amplitude lies in the threshold range phi F Built-in "1", amplitude lies in the threshold range Φ F Externally, the "0" is shown as follows:
wherein: a' (x) θ ,y θ ) Representing two-dimensional data after removing structural features;
counting the number N of defective pixels in the critical area and the non-critical area respectively Fc And N Fnc The calculation method is as follows:
wherein: n (N) Fc A number of pixels representing defects in the critical area; n (N) Fnc A number of pixels representing defects in the non-critical area;
calculating the perfection rate alpha of the tested piece in the critical area, the non-critical area and the structural characteristics c 、α nc And alpha 2 The following is shown:
wherein: alpha c And alpha nc Respectively are provided withThe integrity rates of test pieces in the key area and the non-key area are represented; alpha 2 Indicating the integrity of the part under test when there are structural features.
2. The test piece integrity detection method based on ultrasonic C-scan digital image processing according to claim 1, wherein the two-dimensional scan image Pic (x, y) and the two-dimensional color band Col (m, n) thereof in the step 1 are specifically:
the two-dimensional scanning image Pic (x, y) represents the pixel colors of the x-th row and the y-th column of the ultrasonic C-scanning image; the two-dimensional color bar Col (m, n) represents the pixel colors of the m-th row and the n-th column in the color bar, and the amplitude Amp range corresponding to different colors in the color bar is [0,100].
3. The test piece perfection rate detection method based on ultrasonic C-scan digital image processing according to claim 1, wherein the three-dimensional image array and the three-dimensional color band array of the RGB mode in step 1 are specifically obtained by:
reading in an ultrasonic C-scan image Pic (x, y), wherein the read-in data is a three-dimensional image array Pic in an RGB mode RGB (x,y,i)，i＝1,2,3；Pic RGB (x, y, i) represents the values of the x-th row and y-th column of the i-th layer; layer 1 data Pic RGB (x, y, 1) is the value of R, layer 2 data Pic RGB (x, y, 2) is the value of G, layer 3 data Pic RGB (x, y, 3) is a number of B;
color band Col (m, n) of ultrasonic C scanning image is read in, and the read-in data is a three-dimensional color band array Col of RGB mode RGB (m,n,i)，i＝1,2,3；Col RGB (m, n, i) represents the values of the m-th row and n-th column of the i-th layer; layer 1 data Col RGB (m, n, 1) is the value of R, layer 2 data Col RGB (m, n, 2) is the value of G, layer 3 data Col RGB (m, n, 3) is a number of B; the reason data Col RGB (m, n, i) the same column of values in all layers is the same, only column 1 values Col in each layer are taken RGB (m,1,i)。
4. The ultrasonic C-scan digital image processing based test piece completion of claim 1A method for detecting the yield is characterized in that in the step 1, M is as follows 1 、M 2 And M 3 Taking intersections, as follows:
M＝M 1 ∩M 2 ∩M 3 ；
wherein: m represents a three-dimensional color band array Col RGB (m, 1, i) RGB data and three-dimensional image array Pic RGB And (x, y, i) the row number corresponding to the same RGB data of the x-th row and the y-th column.
5. The method for detecting the integrity of a test piece based on ultrasonic C-scan digital image processing according to claim 1, wherein the data obtained in two-dimensional amplitude data a (x θ ,y θ ) Is used for extracting structural feature region (x As ,y As ) Critical area (x) Ac ,y Ac ) And non-critical areas (x Anc ,y Anc ) The method specifically comprises the following steps:
according to the structural characteristics, the coordinate system corresponding to the critical area and the non-critical area boundary in the design parameters of the tested piece, the two-dimensional wave amplitude data A (x θ ,y θ ) Extracting structural features, key areas and non-key areas;
the coordinate acquisition method corresponding to the boundary of the structural feature region is as follows:
wherein: x is x s And y s Respectively representing the corresponding abscissa and ordinate of the boundary of the structural feature region in the design parameters; x is x As And y As Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle structure characteristic region corresponds to an abscissa and an ordinate; l and W are the length and width of the design parameters, respectively; p and Q are two-dimensional amplitude data A (x θ ,y θ ) Total number of rows and total number of columns;
the coordinate acquisition method corresponding to the key region boundary is as follows:
wherein: x is x c And y c Respectively representing the corresponding abscissa and ordinate of the boundary of the key region in the design parameters; x is x Ac And y Ac Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The boundary of the middle key region corresponds to an abscissa and an ordinate;
the coordinate acquisition method corresponding to the non-key region boundary is as follows:
wherein: x is x nc And y nc Respectively representing corresponding coordinates of non-key region boundaries in design parameters; x is x Anc And y Anc Respectively representing two-dimensional amplitude data A (x θ ,y θ ) The non-critical region boundary corresponds to coordinates.
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