CN111754563A - Method for automatically measuring percentage of section shearing area in drop weight tearing test - Google Patents
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Abstract
The invention provides a method for automatically measuring the percentage of a shear area of a section in a drop hammer tear test, which comprises the steps of firstly collecting fracture images, then measuring a standard gauge block to ensure the measurement traceability of pixels of an imaging picture and the actual physical length, then determining a net section selection area, and performing image enhancement by using gray level histogram regularization; secondly, carrying out characteristic value identification and segmentation on the image through maximum inter-class variance threshold segmentation, distinguishing a toughness section, binarizing the image, and finally calculating the area of the toughness section through counting pixel points in a characteristic region so as to calculate the shearing area of the sample. The method utilizes a digital image processing method to measure the shearing area of the drop hammer tear test, reduces the labor intensity of testers, improves the measurement accuracy, and strengthens the objectivity and traceability of test results. After the production is realized, the method can be widely used for quality inspection of petroleum pipes, the quality technical level of the whole experimental industry is improved, and the percentage of the shearing area is efficiently and accurately obtained.
Description
Technical Field
The invention relates to the field of quality inspection, in particular to a method for automatically measuring the percentage of a shear area of a section in a drop weight tear test.
Background
Petroleum and natural gas are 'blood' in modern industry, and material research and development related to energy transportation and pipe network engineering construction also occupy indispensable and important positions in national economic development. With the increasing exhaustion of oil and gas resources in regions with well-found reserves, it is urgent to extend exploration and mining to regions with severe geological conditions such as remote regions, polar regions and oceans and without well-found reserves. Under the influence of external conditions such as low temperature, frequent geological activities and the like and the process conditions of internal high pressure, thick wall and large caliber pipelines, high-strength and high-toughness pipeline steel products such as X70, X80 and the like need to have enough ductile fracture-stopping capability so as to prevent catastrophic accidents of long-distance fracture expansion of pipelines in accidents such as leakage, explosion and the like.
The toughness of the metal material is evaluated by the following method: (1) the small size uses the Charpy impact test. (2) Medium size Drop Weight Tear Test (DWTT). (3) Full-scale blasting tests simulating full-scale blasting of actual pipelines are adopted. The drop hammer tear test principle is that a heavy hammer with certain energy is adopted, a sample with a notch is broken at a certain speed at a certain temperature, and therefore the appearance of the sample fracture is evaluated.
The drop hammer tear test result is mainly the relationship between fracture morphology and temperature, so as to determine the ductile-brittle transition temperature of pipeline steel, and also can test the impact absorption energy in the fracture process of a test sample. The toughness of the pipeline steel pipe is tested by adopting a sample with full wall thickness or close to full wall thickness in the test, so that the test is closer to the stress and strain states of an actual pipeline, the size of the sample is larger, the complete crack initiation and expansion process of a crack in the test can be ensured, the test method is an economical and feasible test research method for evaluating characteristic parameters such as crack initiation, crack expansion, dynamic fracture toughness and the like of a material, and is an essential bridge and link between a small-size Charpy impact test and a physical blasting test.
The method used in the test process mainly comprises three standards of a ferritic steel drop weight tear test method (GB/T8363), a pipeline steel pipe drop weight tear test method (SY/T6476) and a pipeline steel drop weight tear test (API RP 5L 3). The experimenter mainly judges the toughness of the steel by the area percentage of the toughness section (namely the shearing section) in the effective area of the fracture of the sample. If the shearing area percentage is less than 50%, the steel is unsafe brittle fracture behavior, and if the shearing area percentage is more than 85%, the steel is tough fracture behavior with certain crack arrest capability, so that the shearing area percentage is calculated mainly by an experimenter through a manual measurement and map comparison method, and the measurement result is influenced by experience and human factors of the experimenter and is not beneficial to reproduction and objective evaluation of the experimental result. Therefore, a simple and objective evaluation method is needed to improve the experimental level at the present stage.
Disclosure of Invention
Aiming at the problem that the calculation of the percentage of the shearing area has errors in the prior art, the invention provides a method for automatically measuring the percentage of the shearing area of a section in a drop weight tear test. The detection level is improved, and the objectivity of experimental data and the traceability of experimental results are guaranteed.
The invention is realized by the following technical scheme:
a method for automatically measuring percentage of a shear area of a section in a drop weight tear test comprises the following steps:
step 2, measuring the standard gauge blocks, and determining the area unit of each pixel in the image according to the resolution of the image;
step 3, identifying the thickness of the sample according to the area of the pixel, and determining a net section selection area;
step 4, carrying out image enhancement on the image of the set area in the collected image by adopting a gray level histogram;
step 5, identifying and segmenting characteristic values of the set area image, and distinguishing sections of the toughness area;
and 6, counting pixel points in the characteristic region, calculating the area of the toughness section, and further calculating the shearing area of the sample.
Preferably, a CCD image sensor is used to capture the image in step 1.
Preferably, the method for determining the net cross-sectional selection area in step 3 is as follows:
when the thickness of the sample is smaller than or equal to the set thickness, the thickness of the sample is subtracted from the root of the pressing notch or the tip of the herringbone notch on the cross section of the sample, and the section obtained by subtracting the thickness of the sample from the hammering side is a net section selection area;
when the sample thickness is greater than the set thickness, the net section is the cross section of the sample cross section from the tip of the pressed notch or chevron notch and from the hammer side each minus the set thickness.
Preferably, the image enhancement method in step 4 is as follows:
step 3.1, histogram equalization processing is carried out on the original image;
where N is the total pixels in the image, L is the total number of gray levels, rkIs the value of the kth gray level, rkHas a number of pixels of nk,rkHas a probability p of pixel occurrencer(rk)，0≤r k1 or less, k is an integer from 0 to L-1, T (r)k) Representing a transformation function, skThe gray value of the original image after transformation;
step 3.2, histogram equalization processing is carried out on the image in the set area;
wherein p isz(rz) Representing the grey level r in the image of the set areazG (Z) of the probability of occurrence of the pixelk) Representing a transformation function, vkThe gray value of the set area image after transformation is obtained;
step 3.3, adopting the gray value v after the image transformation with the set areakTransformed gray value s of the closest original imagekReplacing the gray value v after the image transformation of the set regionkIn combination with G-1(s) obtaining an inversion function z 'by inverse transformation'k；
Step 3.4, according to the inversion function z'kObtaining the gray level probability density p of the corresponding set region imagez(zk) And finishing image enhancement.
Preferably, in step 5, a maximum inter-class variance threshold segmentation method is adopted to identify and segment the feature values of the image.
Preferably, the method for identifying and segmenting the feature value specifically comprises the following steps:
assuming that the gray scale range of an image is [1,2, …, m ], the number of pixels corresponding to gray level i is ni, and the total number N of the whole image is N1+ N2+ … + nm, the probability distribution of the pixels corresponding to gray level i is as follows:
if the whole image takes the gray level t as a threshold and is divided into two types, C0 and C1, wherein the gray level range [1, …, t ] of the C0 type, and the C1 type are [ t +1, …, m ], the two types of probabilities are as follows:
the mean values of C0 and C1 are:
the overall image gray level average value μ and the gray level average value μ (t) when the threshold value is t are as follows:
the average value of the gray levels of all samples is as follows
μ＝woμ0+w1μ1
The variance between C0 and C1 is:
varying t between 1 and m, solving for order max2T at (t)*And obtaining a threshold value, and binarizing the image through the threshold value.
Preferably, the calculation method of the shearing area in the step 6 is as follows:
and (3) setting pixel points with the number of 1 in the image as brittle surfaces, and calculating the fraction ratio of the shearing area through the pixel points with the statistical value of 1 and the area unit of each pixel obtained in the step (2).
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a method for automatically measuring the percentage of a shear area of a section in a drop hammer tear test, which comprises the steps of firstly collecting fracture images, then measuring a standard gauge block to ensure the measurement traceability of pixels of an imaging picture and the actual physical length, then determining a net section selection area, and performing image enhancement by using gray level histogram regularization; secondly, carrying out characteristic value identification and segmentation on the image through maximum inter-class variance threshold segmentation, distinguishing a toughness section, binarizing the image, and finally calculating the area of the toughness section through counting pixel points in a characteristic region so as to calculate the shearing area of the sample. The method utilizes a digital image processing method to measure the shearing area of the drop hammer tear test, reduces the labor intensity of testers, improves the measurement accuracy, and strengthens the objectivity and traceability of test results. After the production is realized, the method can be widely used for quality inspection of petroleum pipes, the quality technical level of the whole experimental industry is improved, and the percentage of the shearing area is efficiently and accurately obtained.
Drawings
FIG. 1 is a schematic view of a first exemplary fracture morphology of the present invention;
FIG. 2 is a schematic view of a second exemplary fracture morphology according to the present invention;
FIG. 3 is a schematic view of a third exemplary fracture morphology according to the present invention;
FIG. 4 is a diagram of an example of a fracture section in a drop-weight fracture experiment according to the present invention;
FIG. 5 is a diagram of the conversion of a digital image and a two-dimensional array according to the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the attached drawings, which are illustrative, but not limiting, of the present invention.
A method for automatically measuring percentage of a shear area of a section in a drop weight tear test comprises the following steps:
1) acquiring a fracture image through a CCD image sensor;
and carrying out data acquisition on the welding seam section sample through a CCD camera, converting an optical information number into a digital image signal, and storing and displaying the digital image signal in a gray-scale image format.
Referring to fig. 5, a digital image is an array of dots. In the digital image processing, the computer stores and calculates the image through the two-dimensional array.
The rows of the two-dimensional array correspond to the height of the image and the columns of the two-dimensional array correspond to the width of the image. The elements of the two-dimensional array correspond to pixels of the image, and the values of the elements of the two-dimensional array are the gray values of the pixels.
2) And determining a basic calculation unit, and measuring the standard gauge blocks to ensure the measurement traceability of the pixels and the actual physical length of the imaging picture.
Referring to fig. 1-3, the area unit occupied by each pixel is calculated by collecting standard gauge blocks with known sizes and calculating the resolution of the image.
3) Identifying the thickness of the sample and determining a net section selection area;
referring to fig. 3, 1) a specimen having a specimen thickness of 19.0mm or less, a specimen thickness is subtracted from the root of the pressed notch or the tip of the chevron notch in the specimen cross section and a specimen thickness back section is deducted from the hammer side.
2) For test specimens with a thickness of more than 19.0mm, the clear section is the section of the test specimen cross section which is 19.0mm thick in each case starting from the tip of the pressed or chevron notch and starting from the hammer side.
4) Carrying out image enhancement on the image in the set area by adopting a gray level histogram;
1) histogram equalization processing is carried out on the original image
Where N is the total number of pixels in the digital image, the total number of gray levels is L, and the value of the kth gray level is rkHaving a grey level r in the imagekHas a number of pixels of nkGray level r in the imagekHas a probability p of pixel occurrencer(rk)，rkGreater than or equal to 0 and less than or equal to 1, and k is an integer between 0 and L-1. T (r)k) Representing a transformation function, skThe gray value of the original image after transformation.
2) Histogram equalization processing is performed on the set area image by the same method
The area image is set to be an image which is expected to be obtained, namely, the gray scale range of the effective area is highlighted, and the contrast of a certain gray scale range is enhanced. p is a radical ofz(rz) Representing the grey level r in the image of the set areazThe probability of occurrence of the pixel. G (Z)k) Representing a transformation function, vkThe gray value of the set area image after transformation is obtained.
3) Use with vkS of proximitykIn place of vkIn combination with G-1(s) inverse transformation to obtain zk
4) According to a series zkFinding the corresponding pz(zk)
5) Performing characteristic value identification and segmentation on the image by adopting maximum inter-class variance threshold segmentation, distinguishing sections with toughness and brittleness, and binarizing the image;
the maximum between-class variance threshold segmentation is derived on the basis of the discrimination and least square principle, a gray level histogram in an image is segmented into two groups at a certain threshold, and when the variance between the two groups is maximum, the threshold is determined. The specific mathematical representation is as follows:
the gray scale range of an image is [1,2, …, m ], the pixel corresponding to the gray level i is ni, the total number N of the whole image is N1+ N2+ … + nm, and the probability distribution of the pixel corresponding to the gray level i is
If the whole image is thresholded at the gray level t, the images are classified into two categories, C0 and C1. Wherein the grey scale range of C0 class [1, …, t ], and C1 class [ t +1, …, m ]. These two types of probabilities are
The mean values of these two classes are:
The average values of all sampled grays are respectively: mu-woμ0+w1μ1
The variance between the two groups was:
varying t between 1 and m, solving for t when the maximum of the above equation is taken, i.e., max2T at (t)*This value is the threshold. And binarizing the image through a threshold value.
6) And calculating the area of the toughness section by counting the pixel points in the characteristic region, and further calculating the shearing area of the sample.
And (3) identifying pixel points with the number of 1 in the image as brittle surfaces, and calculating the area fraction of the shearing area through the pixel points with the number of 1 and the basic quantity units obtained in the step (2).
The invention provides a method for automatically measuring the percentage of a shear area of a section in a drop hammer tear test, which comprises the steps of collecting a fracture image through a CCD (charge coupled device) image sensor, measuring a standard gauge block, ensuring the measurement traceability of pixels of an imaging picture and the actual physical length, identifying the thickness of a sample, determining a net section selection area, and performing image enhancement by using gray level histogram regularization; and then, carrying out characteristic value identification and segmentation on the image through maximum inter-class variance threshold segmentation, distinguishing sections with toughness and brittleness, binarizing the image, and finally calculating the area of the toughness section through counting pixel points in a characteristic region so as to calculate the shearing area of the sample. The method utilizes a digital image processing method to measure the shearing area of the drop hammer tear test, reduces the labor intensity of testers, improves the measurement accuracy, and strengthens the objectivity and traceability of test results. After the production is realized, the method can be widely used for quality inspection of petroleum pipes, the quality technical level of the whole experimental industry is improved, and the percentage of the shearing area is efficiently and accurately obtained.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (7)
1. A method for automatically measuring percentage of a shear area of a section in a drop weight tear test is characterized by comprising the following steps:
step 1, collecting an image of a fracture;
step 2, measuring the standard gauge blocks, and determining the area unit of each pixel in the image according to the resolution of the image;
step 3, identifying the thickness of the sample according to the area of the pixel, and determining a net section selection area;
step 4, carrying out image enhancement on the image of the set area in the collected image by adopting a gray level histogram;
step 5, identifying and segmenting characteristic values of the set area image, and distinguishing sections of the toughness area;
and 6, counting pixel points in the characteristic region, calculating the area of the toughness section, and further calculating the shearing area of the sample.
2. The method for automatically measuring the percentage of the shear area of the drop weight tear test section according to claim 1, wherein a CCD image sensor is used for acquiring the image in the step 1.
3. The method for automatically measuring percentage of area sheared by drop weight tear test according to claim 1, wherein the method for determining the net cross section selected area in step 3 is as follows:
when the thickness of the sample is smaller than or equal to the set thickness, the thickness of the sample is subtracted from the root of the pressing notch or the tip of the herringbone notch on the cross section of the sample, and the section obtained by subtracting the thickness of the sample from the hammering side is a net section selection area;
when the sample thickness is greater than the set thickness, the net section is the cross section of the sample cross section from the tip of the pressed notch or chevron notch and from the hammer side each minus the set thickness.
4. The method for automatically measuring the percentage of the shear area of the drop weight tear test section according to claim 1, wherein the image enhancement method in the step 4 is as follows:
step 3.1, histogram equalization processing is carried out on the original image;
where N is the total pixels in the image, L is the total number of gray levels, rkIs the value of the kth gray level, rkHas a number of pixels of nk，rkHas a probability p of pixel occurrencer(rk)，0≤rk1 or less, k is an integer from 0 to L-1, T (r)k) Representing a transformation function, skThe gray value of the original image after transformation;
step 3.2, histogram equalization processing is carried out on the image in the set area;
wherein p isz(rz) Representing the grey level r in the image of the set areazG (Z) of the probability of occurrence of the pixelk) Representing a transformation function, vkThe gray value of the set area image after transformation is obtained;
step 3.3, adopting the gray value v after the image transformation with the set areakTransformed gray value s of the closest original imagekReplacing the gray value v after the image transformation of the set regionkIn combination with G-1(s) obtaining an inversion function z 'by inverse transformation'k；
Step 3.4, according to the inversion function z'kObtaining the gray level probability density p of the corresponding set region imagez(zk) And finishing image enhancement.
5. The method for automatically measuring percentage of shear area of a drop hammer tear test section according to claim 1, wherein in the step 5, a maximum between-class variance threshold segmentation method is adopted to perform characteristic value identification and segmentation on the image.
6. The method for automatically measuring percentage of shear area of a drop weight tear test section according to claim 1, wherein the method for identifying and segmenting the characteristic value is specifically as follows:
assuming that the gray scale range of an image is [1, 2., m ], the number of pixels corresponding to the gray level i is ni, and the total number N of the whole image is N1+ N2+.. + nm, the probability distribution of the pixels corresponding to the gray level i is as follows:
if the whole image is thresholded at a gray level t and is divided into two types, C0 and C1, wherein the gray level range [1,.. multidot.t ] of C0 type, and the gray level range [ t +1,. multidot.m ] of C1 type are as follows:
the mean values of C0 and C1 are:
the overall image gray level average value μ and the gray level average value μ (t) when the threshold value is t are as follows:
the average value of the gray levels of all samples is as follows
μ＝woμ0+w1μ1
The variance between C0 and C1 is:
varying t between 1 and m, solving for order max2T at (t)*Obtaining a threshold value, andand (5) carrying out binarization on the image by passing a threshold value.
7. The method for automatically measuring the percentage of the shear area of the drop weight tear test section according to claim 1, wherein the shear area in the step 6 is calculated by the following method:
and (3) setting pixel points with the number of 1 in the image as brittle surfaces, and calculating the fraction ratio of the shearing area through the pixel points with the statistical value of 1 and the area unit of each pixel obtained in the step (2).
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| CN114544632A (en) * | 2022-02-28 | 2022-05-27 | 陕西正通煤业有限责任公司 | Coal rock impact energy calculation method based on block image recognition |
| CN114544632B (en) * | 2022-02-28 | 2024-05-24 | 陕西正通煤业有限责任公司 | Coal rock impact energy calculation method based on block image recognition |
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