CN112487642B - Fatigue fracture morphology feature extraction method based on water-flooding filling algorithm - Google Patents
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Abstract
The invention discloses a fatigue fracture morphology feature extraction method based on a water-flooding filling algorithm, which comprises the following steps of: s1, preprocessing to obtain a preprocessed image; s2, performing water-flooding filling segmentation numbering on the preprocessed image to obtain a sequence region; s3, drawing an image contour of the sequence region to obtain an image with a closed contour; acquiring an image characteristic value of a sequence region; s4, calculating the area of the region of the image with the closed contour; s5, calculating the intensity factors of all the contours according to the area of the region; s6, performing linear fitting based on the image characteristic values and the intensity factors to obtain a fitting curve; s7, obtaining the appearance characteristics of the fatigue fracture according to the image with the closed contour and the fitting curve. The problems of difficulty and inaccuracy in visual analysis of gray image naked eye identification and complex calculation of area and intensity factors obtained by an acquisition system in the past are solved, and the analysis conclusion is quicker and more accurate.
Description
Technical Field
The invention relates to the field of fatigue port morphology analysis, in particular to a fatigue fracture morphology feature extraction method based on a water-flooding filling algorithm.
Background
The fatigue fracture analysis of the material is to conduct macroscopic and microscopic analysis after the corrosion cleaning of the fracture surface of the sample. The macroscopic analysis is to directly observe and analyze the broken parts by naked eyes, a magnifying glass and a solid microscope; the microscopic analysis is a method for imaging and analyzing the whole fracture, and mainly uses a transmission electron microscope and a scanning electron microscope. However, the magnification of macro analysis is usually 100 times or less, and it is difficult to judge and analyze the microscopic constitution of the crack with naked eyes; the fracture image obtained by the electron microscope is only amplified and gray-scale processed, the lowest amplification factor is 1000 times, the resolution ratio is up to 0.01um, and the fracture image is mostly observed and judged by people.
Fatigue cracks are divided into surface and internal initiation, and for some complex fracture morphologies such as the accumulation of plastic deformation, the difference between a crack source and a plastic accumulation area is difficult to distinguish, the existing pits are easy to be mistaken as the crack source, and various propagation mechanisms exist in one fatigue fracture morphology from the initiation area nucleation, so that the crack propagates from the source along all directions, the propagation trend of the crack is sometimes difficult to observe, and the like, which are difficult to visually observe and quickly determine through naked eyes.
The number of the cleavage faces of the fatigue fracture is determined, the outline of the cleavage face is required to be manually measured, the area size is required to be calculated, then the outline of the cleavage face is required to be extracted, the calculation is carried out through computer integration, the statistical distribution of the duty ratio is required to be extracted from the whole crack expansion low-speed area and the area size of the outline of each cleavage face is required to be calculated, and the duty ratio of the cleavage face is required to be obtained after the area size is calculated, and frequent steps and calculation are required in the processes.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for quickly acquiring the fatigue fracture morphology feature based on the fatigue fracture morphology feature extraction method of the water-flooding filling algorithm.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the fatigue fracture morphology feature extraction method based on the water-flooding filling algorithm comprises the following steps:
s1, acquiring a fatigue fracture image, and preprocessing by adopting matlab to obtain a preprocessed image;
s2, performing water-flooding filling segmentation numbering on the preprocessed image to obtain a sequence region;
s3, drawing an image contour of the sequence region to obtain an image with a closed contour; acquiring an image characteristic value of a sequence region, wherein the image characteristic value comprises entropy, a gray average value and a gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factors of all the contours according to the area of the region;
s6, performing linear fitting based on the image characteristic values and the intensity factors to obtain a fitting curve;
s7, obtaining the appearance characteristics of the fatigue fracture according to the image with the closed contour and the fitting curve.
Further, the specific method in step S1 is as follows:
collecting fatigue fracture images through a digital camera, and carrying out graying, denoising and binarization processing by adopting matlab to obtain preprocessed images;
or, image acquisition and gray scale processing are carried out through a scanning electron microscope or a transmission electron microscope, and matlab is adopted for denoising and binarization processing, so that a preprocessed image is obtained.
Further, the specific method of step S2 comprises the following sub-steps:
s2-1, calling a flood fill operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, a four-connected-domain range with an operation identifier of eight bits, and filling the eight-connected-domain range with water to obtain a connected region image of the preprocessed image;
s2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of gray scale by taking an initial pixel point of a connected region image as a starting point, so as to obtain a sequence region.
Further, in step S3, the specific method for drawing the image contour of the sequence region to obtain the image with the closed contour is as follows:
and searching a gray value at the boundary of the sequence region by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
Further, the specific method for obtaining the entropy of the sequence region in step S3 is as follows:
and loading an OpenCV library function in the VS series software, searching all pixel points in each sequence region by using a global threshold method on the sequence region, and calling a double entropy function to calculate to obtain the entropy of each sequence region image.
Further, the specific method in step S5 is as follows:
according to the formula:
calculating an intensity factor delta K; where n is a constant, which is 0.6 when cracks are initiated from the surface and 0.5 when cracks are initiated from the inside; pi is the circumference ratio; areα is the area of the region.
Further, the specific method of step S6 is as follows:
comparing the image characteristic values of the images with the closed outlines to obtain the contrast of the images with the closed outlines; and calling image characteristic values by using Oringin software, respectively taking entropy, a gray average value, gray variance and the contrast of each image with a closed contour as independent variables, and taking intensity factors as dependent variables, and performing primary function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray average value, a fitting curve corresponding to the gray variance and a fitting curve corresponding to the contrast of each image with a closed contour.
Further, the fatigue fracture morphology features in step S7 include image features and numerical features; wherein the image features include crack initiation source location, plastic accumulation region, crack progression and degree of stress deformation; the numerical features include magnitude relation and statistical distribution of image feature values and intensity factors obtained by linear fitting.
The beneficial effects of the invention are as follows:
1. the sensitivity of the gray value of the image pixel is utilized, the image acquisition system is combined, the fracture morphology of the crack under different fatigue lives can be quantitatively obtained by utilizing the flooding filling operator and origin data fitting, and the crack source can be reversely deduced from the crack trend in profile extraction; the statistical duty ratio of different crack morphologies can be obtained through area calculation; the crack strength factor corresponding to the area can be obtained from different area. The problems of difficulty and inaccuracy in visual analysis of gray image naked eye identification and complex calculation of area and intensity factors obtained by an acquisition system in the past are solved, and the analysis conclusion is quicker and more accurate.
2. The linear fitting part in the invention can characterize the tissue stress strain response condition of each connected region of the appearance after fracture, and has universal applicability.
3. The method has the advantages of simpler and more convenient program design function calling and operation and strong applicability.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is an original image in an embodiment;
FIG. 3 is a diagram of a region extraction effect of eight-low octets of an operation identifier;
fig. 4 is a schematic drawing of an image profile of a region of a rendering sequence.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the fatigue fracture morphology feature extraction method based on the water-flooding filling algorithm comprises the following steps:
s1, acquiring a fatigue fracture image, and preprocessing by adopting matlab to obtain a preprocessed image;
s2, performing water-flooding filling segmentation numbering on the preprocessed image to obtain a sequence region;
s3, drawing an image contour of the sequence region to obtain an image with a closed contour; acquiring an image characteristic value of a sequence region, wherein the image characteristic value comprises entropy, a gray average value and a gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factors of all the contours according to the area of the region;
s6, performing linear fitting based on the image characteristic values and the intensity factors to obtain a fitting curve;
s7, obtaining the appearance characteristics of the fatigue fracture according to the image with the closed contour and the fitting curve.
The specific method of the step S1 is as follows:
collecting fatigue fracture images through a digital camera, and carrying out graying, denoising and binarization processing by adopting matlab to obtain preprocessed images;
or, image acquisition and gray scale processing are carried out through a scanning electron microscope or a transmission electron microscope, and matlab is adopted for denoising and binarization processing, so that a preprocessed image is obtained.
The specific method of the step S2 comprises the following substeps:
s2-1, calling a flood fill operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, a four-connected-domain range with an operation identifier of eight bits, and filling the eight-connected-domain range with water to obtain a connected region image of the preprocessed image;
s2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of gray scale by taking an initial pixel point of a connected region image as a starting point, so as to obtain a sequence region.
In step S3, drawing an image contour of the sequence region, and the specific method for obtaining the image with the closed contour is as follows: and searching a gray value at the boundary of the sequence region by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
The specific method for obtaining the entropy of the sequence region in the step S3 is as follows: and loading an OpenCV library function in the VS series software, searching all pixel points in each sequence region by using a global threshold method on the sequence region, and calling a double entropy function to calculate to obtain the entropy of each sequence region image.
The specific method of step S5 is as follows: according to the formula:
calculating an intensity factor delta K; where n is a constant, which is 0.6 when cracks are initiated from the surface and 0.5 when cracks are initiated from the inside; pi is the circumference ratio; areα is the area of the region.
The specific method of step S6 is as follows: comparing the image characteristic values of the images with the closed outlines to obtain the contrast of the images with the closed outlines; and calling image characteristic values by using Oringin software, respectively taking entropy, a gray average value, gray variance and the contrast of each image with a closed contour as independent variables, and taking intensity factors as dependent variables, and performing primary function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray average value, a fitting curve corresponding to the gray variance and a fitting curve corresponding to the contrast of each image with a closed contour.
The fatigue fracture morphology features in the step S7 comprise image features and numerical features; wherein the image features include crack initiation source location, plastic accumulation region, crack progression and degree of stress deformation; the numerical features include magnitude relation and statistical distribution of image feature values and intensity factors obtained by linear fitting.
In one embodiment of the present invention, after the original image is preprocessed as shown in fig. 2, a connected region image extracted by using a region with eight lower bits and eight connected of the operation identifier is shown in fig. 3, and a sequence region obtained by dividing the connected region image and sequence numbering is subjected to contour drawing, and the obtained image with a closed contour is shown in fig. 4. And the follow-up operation is carried out on the image with the closed contour, so that the fatigue breaking morphological characteristics can be rapidly extracted.
In the specific implementation process, fracture morphology analysis: mainly comprises two parts of image characteristics and numerical characteristics. The image features are: the functions of crack initiation source position determination, plastic accumulation area determination, crack trend determination and the like for helping eyes identify various special morphologies such as 'facets', 'fish eyes', 'dark regions', 'light regions', 'initiation regions, expansion regions, instantaneous break regions' and the like. Sequence numbering can be carried out on the extracted characteristic regions according to different gray values through analysis, the regions under the same number are similar in reaction composition by combining characteristic value data, and the stress deformation degree is equivalent; the profile is extracted to analyze crack trend, crack accumulation position and crack initiation source; the numerical feature mainly comprises the following steps of interpreting the magnitude relation and statistics subsection of the image feature value and the intensity factor by linear fitting: 1. the magnitude relation of the image gray level confusion degree, gray level average value, variance, image area contrast and intensity factor of the entropy reaction can be determined by the statistical distribution of the fitting curve. 2. The position and the size distribution of the intensity factors on the whole section can be further realized according to the distribution of the sequence region where the intensity factor value is located on the whole image by the size relation of the contrast of the image region and the intensity factors.
In summary, the sensitivity of the gray value of the image pixel is utilized, the image acquisition system is combined, the fracture morphology of the crack under different fatigue lives can be quantitatively obtained by utilizing the flooding filling operator and oridin data fitting, and the crack source can be reversely deduced from the crack trend in the profile extraction; the statistical duty ratio of different crack morphologies can be obtained through area calculation; the crack strength factor corresponding to the area can be obtained from different area. The problems of difficulty and inaccuracy in visual analysis of gray image naked eye identification and complex calculation of area and intensity factors obtained by an acquisition system in the past are solved, and the analysis conclusion is quicker and more accurate.
Claims (7)
1. The fatigue fracture morphology feature extraction method based on the water-flooding filling algorithm is characterized by comprising the following steps of:
s1, acquiring a fatigue fracture image, and preprocessing by adopting matlab to obtain a preprocessed image;
s2, performing water-flooding filling segmentation numbering on the preprocessed image to obtain a sequence region;
s3, drawing an image contour of the sequence region to obtain an image with a closed contour; acquiring an image characteristic value of a sequence region, wherein the image characteristic value comprises entropy, a gray average value and a gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factors of all the contours according to the area of the region;
s6, performing linear fitting based on the image characteristic values and the intensity factors to obtain a fitting curve;
s7, obtaining the appearance characteristics of the fatigue fracture according to the image with the closed contour and the fitting curve;
the specific method of the step S6 is as follows:
comparing the image characteristic values of the images with the closed outlines to obtain the contrast of the images with the closed outlines; and calling image characteristic values by using Oringin software, respectively taking entropy, a gray average value, gray variance and the contrast of each image with a closed contour as independent variables, and taking intensity factors as dependent variables, and performing primary function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray average value, a fitting curve corresponding to the gray variance and a fitting curve corresponding to the contrast of each image with a closed contour.
2. The method for extracting the fatigue fracture morphology feature based on the water-flooding filling algorithm according to claim 1, wherein the specific method in the step S1 is as follows:
collecting fatigue fracture images through a digital camera, and carrying out graying, denoising and binarization processing by adopting matlab to obtain preprocessed images;
or, image acquisition and gray scale processing are carried out through a scanning electron microscope or a transmission electron microscope, and matlab is adopted for denoising and binarization processing, so that a preprocessed image is obtained.
3. The method for extracting the fatigue fracture morphology feature based on the water-flooding filling algorithm according to claim 1, wherein the specific method of the step S2 comprises the following sub-steps:
s2-1, calling a flood fill operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, a four-connected-domain range with an operation identifier of eight bits, and filling the eight-connected-domain range with water to obtain a connected region image of the preprocessed image;
s2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of gray scale by taking an initial pixel point of a connected region image as a starting point, so as to obtain a sequence region.
4. The method for extracting the fatigue fracture morphology feature based on the water-flooding algorithm according to claim 1, wherein the specific method for drawing the image contour of the sequence region in the step S3 to obtain the image with the closed contour comprises the following steps:
and searching a gray value at the boundary of the sequence region by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
5. The method for extracting the fatigue fracture morphology feature based on the water-flooding filling algorithm according to claim 1, wherein the specific method for obtaining the entropy of the sequence region in the step S3 is as follows:
and loading an OpenCV library function in the VS series software, searching all pixel points in each sequence region by using a global threshold method on the sequence region, and calling a double entropy function to calculate to obtain the entropy of each sequence region image.
6. The method for extracting the fatigue fracture morphology feature based on the water-flooding filling algorithm according to claim 1, wherein the specific method in the step S5 is as follows:
according to the formula:
calculating an intensity factor delta K; where n is a constant, which is 0.6 when cracks are initiated from the surface and 0.5 when cracks are initiated from the inside; pi is the circumference ratio; areα is the area of the region.
7. The method for extracting fatigue fracture morphology features based on the flooding filling algorithm according to claim 1, wherein the fatigue fracture morphology features in the step S7 include image features and numerical features; wherein the image features include crack initiation source location, plastic accumulation region, crack progression and degree of stress deformation; the numerical features include magnitude relation and statistical distribution of image feature values and intensity factors obtained by linear fitting.
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