Method for constructing thickness and gray scale function

Abstract

The application provides a method for constructing a thickness and gray scale function, which solidifies a ray distribution rule in position information to obtain a function relationship between gray scales and thicknesses at different positions, avoids measurement errors or even mistakes in large-size samples caused by a single gray scale and thickness relationship, and effectively solves the attenuation problem of the thickness of a large-size casting blank sample by using ray detection, thereby ensuring the reliability of thickness measurement.

Description

Technical Field

[0001] This invention relates to the field of continuous casting processing technology, and more specifically, to a method for constructing a thickness and grayscale function. Background Technology

[0002] Invention patent CA113129259A discloses a method for density measurement. The most important part of this method is to obtain a function relating grayscale and thickness by comparing the grayscale value and thickness of the sample. Based on this function, the thickness at different locations (different pixels) of the test sample can be determined from the grayscale value, thus enabling subsequent density and process evaluation. The functional relationship between grayscale and thickness is crucial for obtaining the thickness of the test sample. (See attached...) Figure 3 The image shows a schematic diagram of an X-ray transmission through a workpiece. The article "Study on the Thickness and Gray Scale Model of X-ray Digital Imaging Transmission" in Acta Optica Sinica V41 provides a basic method. By transmitting X-rays through a dense, stepped-thickness workpiece sample, the corresponding grayscale image is obtained. Then, by combining physical formula derivation and fitting methods, the functional relationship between grayscale and thickness is obtained. The constant parameters include tube current, tube voltage, and the distance from the X-ray source to the workpiece, which obviously have certain physical significance.

[0003] However, the above method is only suitable for small-sized test samples, generally in the range of 10-50 mm. For large-sized samples of continuously cast billets, this method has a large error. That is, the grayscale image obtained by transmission from any dense, stepped-thickness workpiece is not uniform. As the distance from the vertical position of the X-ray source increases, the grayscale value will gradually change, and the gradual change trend is not consistent in the two vertical directions of the grayscale image plane. Even the grayscale extreme values ​​of the grayscale image do not appear at the center of the vertical position of the X-ray source. This problem is because... Figure 3 From I0 to I1 and then to I2, the radiation intensity is already different in I1, and even more different in I2. There is a distribution law of radiation intensity. When the workpiece is small enough, this radiation intensity distribution law can be ignored. However, when the workpiece size is large, this radiation intensity distribution law will affect or even determine the relationship between grayscale and thickness, and ultimately affect the measurement of the thickness of the test workpiece.

[0004] Practice has also found that when Figure 3 Once F is fixed, the distribution pattern of I1 on the surface of the test sample is basically fixed, while the distribution pattern of I2 on the imaging plate is also related to the thickness. Under the same I1 distribution, the gray distribution of I2 corresponding to different thicknesses is different.

[0005] Therefore, for large-sized samples, determining the ray distribution pattern and ultimately obtaining a precise functional relationship between grayscale and thickness is the foundation of patent CA113129259A. However, no relevant solution has been found to date. Summary of the Invention

[0006] In view of the above problems, the purpose of this invention is to provide a method for constructing a thickness and grayscale function, so as to solve the problem that the grayscale and thickness function relationship obtained by combining physical formula derivation and fitting method in the prior art is only suitable for small-sized test samples, and has a large error for large-sized samples.

[0007] This invention provides a method for constructing thickness and grayscale functions, comprising the following steps:

[0008] According to the preset thickness difference, a preset number of samples of different thicknesses are made, and the samples of different thicknesses are arranged in a stepped manner to form a stepped sample.

[0009] X-ray scanning was performed on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of the samples of different thicknesses.

[0010] The grayscale images in the grayscale image set are subjected to localization processing to obtain the position coordinates of each pixel in the grayscale image in the plane of the grayscale image, forming pixel comprehensive information; wherein, the pixel comprehensive information includes the position of the pixel and the corresponding grayscale value;

[0011] Based on the pixel information, regression processing is performed on the thickness and gray value corresponding to the pixel at a specific position of the stepped sample to form a position-related thickness and gray value function.

[0012] Furthermore, a preferred embodiment, after performing regression processing on the thickness corresponding to a pixel at a specific location on the stepped sample and the grayscale value corresponding to the thickness based on the pixel comprehensive information to form a position-dependent function of thickness and grayscale, further includes:

[0013] X-ray scanning is performed on the test sample to obtain a grayscale image of the test sample;

[0014] The grayscale image of the test sample is subjected to positioning processing. Based on the grayscale values ​​at each position of the grayscale image of the test sample, the thickness corresponding to the grayscale values ​​at each position is calculated using the thickness and grayscale function, thereby obtaining the thickness distribution on the entire surface of the test sample.

[0015] Furthermore, in a preferred embodiment, the material of the sample is the same as that of the test sample; and the thickness of the test sample includes the thickness of its internal defects; the thickness of the test sample is within the thickness range of the stepped sample; the width of the sample is greater than or equal to the width of the test sample; and the length of the sample is greater than or equal to the length of the test sample.

[0016] Furthermore, a preferred embodiment is that the sample does not contain dense defects.

[0017] Furthermore, a preferred embodiment is that the preset thickness difference is 1mm to 5mm.

[0018] Furthermore, a preferred embodiment is that the preset quantity is at least 3.

[0019] In addition, a preferred embodiment is that, in the process of preparing a preset number of samples of different thicknesses according to a preset thickness difference and arranging the samples of different thicknesses in a stepped manner to form a stepped sample, the samples of different thicknesses are arranged in a stepped manner in order of increasing thickness or decreasing thickness, thereby forming a stepped sample.

[0020] Furthermore, a preferred embodiment is that, in the process of performing X-ray scanning on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of samples of different thicknesses, X-rays are used to perform X-ray scanning on samples of different thicknesses in the stepped sample.

[0021] Furthermore, a preferred embodiment is that the step of performing localization processing on the grayscale images in the grayscale image set to obtain the position coordinates of each pixel in the grayscale image on the plane of the grayscale image, forming comprehensive pixel information, includes:

[0022] Using the position perpendicular to the ray source as the origin of the coordinate system, the position of the pixel in the x-direction is labeled as i, and the position in the y-direction is labeled as j, resulting in the position label of the pixel as P(i,j);

[0023] The position P(i,j) of the pixel and the gray value G corresponding to the position P(i,j) are used as the pixel comprehensive information.

[0024] Furthermore, a preferred embodiment is that, based on the pixel comprehensive information, regression processing is performed on the thickness corresponding to a pixel at a specific location on the stepped sample and the gray value corresponding to the thickness to form a position-related thickness-gray value function, including:

[0025] The specimens of different thicknesses in the stepped specimen are numbered, and the thickness of the specimen corresponding to each number is marked as T;

[0026] For the pixel position P(i,j) of the stepped sample, establish a data correspondence between the thickness T(i,j) and gray value G(i,j) corresponding to the position P(i,j);

[0027] Based on the data correspondence, regression processing is performed on the thickness T(i,j) and the gray value G(i,j) to obtain the functional relationship between the thickness T(i,j) and the gray value G(i,j) related to the position P(i,j): G(i,j) = f(T(i,j)).

[0028] As can be seen from the above technical solution, the method for constructing the thickness and grayscale function provided by the present invention obtains the functional relationship between grayscale and thickness at different locations by solidifying the ray distribution law in the position information, thus avoiding the measurement error or even mistake caused by a single grayscale and thickness relationship in large-size samples; the present invention can effectively solve the problem of thickness attenuation when using ray detection for large-size cast billet samples, and ensure the reliability of thickness measurement.

[0029] To achieve the foregoing and related objectives, one or more aspects of the invention include the features that will be described in detail below. The following description and accompanying drawings illustrate certain exemplary aspects of the invention. However, these aspects indicate only a few of the various ways in which the principles of the invention can be used. Furthermore, the invention is intended to encompass all such aspects and their equivalents. Attached Figure Description

[0030] Other objects and results of the invention will become more apparent and readily understood with reference to the following description taken in conjunction with the accompanying drawings. In the drawings:

[0031] Figure 1 This is a flowchart illustrating the method for constructing the thickness and grayscale functions according to an embodiment of the present invention;

[0032] Figure 2 This is a design diagram of the position coordinates of pixels on a grayscale image according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of a workpiece being irradiated with X-rays in the existing technology. Detailed Implementation

[0034] In the following description, numerous specific details are set forth for illustrative purposes and to provide a thorough understanding of one or more embodiments. However, it will be apparent that these embodiments may also be implemented without these specific details.

[0035] In response to the problem that the gray-scale and thickness function relationship obtained by combining physical formula derivation and fitting method in the aforementioned existing technology is only suitable for small-sized test samples and has a large error for large-sized samples, a method for constructing the thickness and gray-scale function is proposed.

[0036] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0037] To illustrate the method for constructing the thickness and grayscale functions provided by this invention, Figure 1 The flowchart of a method for constructing thickness and grayscale functions according to an embodiment of the present invention is shown; Figure 2The design of the position coordinates of pixels on a grayscale image according to an embodiment of the present invention is shown.

[0038] like Figure 1 Combination Figure 2 As shown in the figure, the method for constructing the thickness and grayscale function provided by the present invention includes the following steps:

[0039] S1. According to the preset thickness difference, prepare a preset number of samples of different thicknesses, and arrange the samples of different thicknesses in a stepped manner to form a stepped sample.

[0040] S2. Perform X-ray scanning on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of samples of different thicknesses.

[0041] S3. Perform localization processing on the grayscale images in the grayscale image set to obtain the position coordinates of each pixel in the grayscale image plane, forming pixel comprehensive information; wherein, the pixel comprehensive information includes the position of the pixel and the corresponding grayscale value.

[0042] S4. Based on the pixel information, regression processing is performed on the thickness and gray value corresponding to the pixel at a specific position of the stepped sample to form a position-related thickness and gray value function.

[0043] The specific positions of the stepped specimen are mainly selected based on the location where the thickness of the test specimen (or the specimen to be tested) needs to be calculated. Generally, these are the various positions of the stepped specimen, thereby obtaining the thickness and grayscale function related to each position. Then, the thickness of the corresponding position of the test specimen is calculated using the thickness and grayscale function related to each position, thus obtaining the thickness distribution on the entire surface of the test specimen.

[0044] By embedding the distribution pattern of X-rays into the location information, the functional relationship between grayscale and thickness at different locations is obtained, avoiding the measurement errors or even mistakes caused by a single grayscale and thickness relationship in large-size samples. This invention can effectively solve the problem of thickness attenuation when using X-rays to detect the thickness of large-size cast billet samples, ensuring the reliability of thickness measurement.

[0045] As a preferred embodiment of the present invention, after performing regression processing on the thickness corresponding to a pixel at a specific location of the stepped sample and the gray value corresponding to the thickness based on the pixel comprehensive information to form a position-related function of thickness and gray value, the invention further includes:

[0046] X-ray scanning is performed on the test sample to obtain a grayscale image of the test sample;

[0047] The grayscale image of the test sample is processed for localization. Based on the grayscale values ​​at each position in the grayscale image of the test sample, the thickness corresponding to the grayscale value at each position is calculated using the thickness and grayscale function, thereby obtaining the thickness distribution on the entire surface of the test sample.

[0048] When using the thickness and grayscale functions obtained at different locations, the test sample is subjected to X-ray scanning to obtain a grayscale image of the test sample. The same positioning method as the sample is used, that is, the grayscale image of the test sample is positioned using the same coordinates. Then, based on the grayscale values ​​at each location of the grayscale image of the test sample, the thickness at the corresponding location is calculated using the thickness and grayscale functions related to each location, thereby obtaining the thickness distribution on the entire surface of the test sample.

[0049] As a preferred embodiment of the present invention, the material of the sample is the same as that of the test sample; and the thickness of the test sample includes the thickness of its internal defects; the thickness of the test sample is within the thickness range of the stepped sample; the width of the sample is greater than or equal to the width of the test sample; and the length of the sample is greater than or equal to the length of the test sample.

[0050] When preparing a stepped specimen, a specimen of the same material but different thickness as the test specimen must be selected, and the thickness of the stepped specimen must be greater than or equal to the thickness of the test specimen. The thickness of the test specimen includes the thickness of its internal defects and the thickness excluding the defects. Furthermore, to ensure that the thickness distribution across the entire surface of the test specimen can be obtained, the dimensions (width and length) of the stepped specimen must be greater than or equal to the dimensions (width and length) of the test specimen.

[0051] In a preferred embodiment of the present invention, the sample does not contain dense defects. The sample does not contain dense defects. A stepped sample is constructed using samples of different thicknesses without dense defects to create thickness and grayscale functions related to each location. These thickness and grayscale functions are then used to calculate the thickness at each corresponding location of the test sample, thereby completing the density detection of the test sample. Therefore, the test sample contains dense defects.

[0052] As a preferred embodiment of the present invention, the preset thickness difference is 1mm to 5mm. Preferably, it is 2mm, and the preset thickness difference can be determined according to the actual situation, that is, the thickness difference between adjacent samples of different thicknesses in the stepped sample.

[0053] As a preferred embodiment of the present invention, the preset quantity is at least 3. The preset quantity can be determined according to the actual situation. The more preset quantities there are, the more samples of different thicknesses in the stepped sample, which is more beneficial to the accuracy of the obtained position-related thickness and grayscale function.

[0054] In a preferred embodiment of the present invention, a predetermined number of samples of different thicknesses are prepared according to a predetermined thickness difference, and the samples of different thicknesses are arranged in a stepped manner to form a stepped sample.

[0055] Samples of different thicknesses are arranged in a stepped manner, either in order of increasing thickness or in order of decreasing thickness, to form stepped samples.

[0056] As a preferred embodiment of the present invention, in the process of performing X-ray scanning on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of samples of different thicknesses, X-rays are used to perform X-ray scanning on samples of different thicknesses in the stepped sample.

[0057] As a preferred embodiment of the present invention, the grayscale images in the grayscale image set are subjected to positioning processing to obtain the position coordinates of each pixel point on the plane of the grayscale image, forming pixel point comprehensive information including:

[0058] Using the position perpendicular to the ray source as the origin of the coordinate system, the position of the pixel in the x-direction is labeled as i, and the position in the y-direction is labeled as j, resulting in the position label of the pixel as P(i,j);

[0059] The pixel position P(i,j) and the gray value G corresponding to position P(i,j) are used as the pixel's comprehensive information.

[0060] As a preferred embodiment of the present invention, based on the pixel comprehensive information, regression processing is performed on the thickness corresponding to the pixel at a specific position of the stepped sample and the gray value corresponding to the thickness to form a position-related thickness and gray value function, including:

[0061] The specimens of different thicknesses in the stepped specimen are numbered, and the thickness of the specimen corresponding to each number is marked as T;

[0062] For the pixel position P(i,j) of the stepped sample, establish the data correspondence between the thickness T(i,j) and gray value G(i,j) corresponding to the position P(i,j);

[0063] Based on the data correspondence, regression processing is performed on the thickness T(i,j) and the gray value G(i,j) to obtain the functional relationship between the thickness T(i,j) and the gray value G(i,j) related to the position P(i,j): G(i,j) = f(T(i,j)).

[0064] To better illustrate the method for constructing the thickness and grayscale functions provided by this invention, the following example is provided:

[0065] Example 1

[0066] The test sample is a continuously cast billet, the steel grade is 70# steel, the size of the test sample is 180X250mm, the machining thickness of the test sample is 30mm, but considering internal defects, the minimum thickness is not less than 22mm.

[0067] Therefore, stepped specimens were made, each specimen measuring 180 x 250 mm with a thickness ranging from 32 to 20 mm, spaced 2 mm apart, thus creating stepped specimens of 32 mm, 30 mm, 28 mm...22 mm, 20 mm.

[0068] The grayscale image is obtained by scanning each thickness of the stepped sample. The pixels in the grayscale image are numbered and located; this embodiment uses... Figure 2 The pixel coordinates shown are used for positioning, with the position perpendicular to the ray source as the origin. The planar coordinates include the positive direction as defined in the diagram. Figure 2 In this diagram, the x-axis position of a pixel is identified by 'i', and the y-axis position by 'j', so pixel P is identified as P(i,j). G represents the grayscale, T represents the thickness, and n is the number of the stepped sample. In this embodiment, n ranges from 0 to 6, where T0 = 32 mm, T1 = 30 mm, ..., T6 = 30 mm. Therefore, G(i,j,n) and T(i,j,n) are formed for each pixel P(i,j). Clearly, in the stepped sample, the thickness of T(i,j,n) is only related to n.

[0069] Then, for each pixel P(i,j), we obtain the corresponding n G and T data. By performing regression, we can obtain the functional relationship between G and T at the corresponding pixel, i.e., G(i,j) = f(T(i,j)). The regression method can follow any existing regression method, without special restrictions here, such as the regression method described in "Research on Transmissive Thickness and Gray Scale Model of X-ray Digital Imaging".

[0070] It should be noted that this embodiment is merely a detailed description of the method for constructing the thickness and grayscale function provided by the present invention in practical applications, and does not limit the technical solution provided by the present invention.

[0071] As can be seen from the above specific embodiments, the method for constructing the thickness and grayscale function provided by the present invention obtains the functional relationship between grayscale and thickness at different locations by solidifying the ray distribution law in the position information, thus avoiding the measurement error or even mistake caused by a single grayscale and thickness relationship in large-size samples; the present invention can effectively solve the problem of thickness attenuation when using ray detection for large-size cast billet samples, ensuring the reliability of thickness measurement.

[0072] The method for constructing thickness and grayscale functions according to the present invention has been described above by way of example with reference to the accompanying drawings. However, those skilled in the art should understand that various modifications can be made to the method for constructing thickness and grayscale functions according to the present invention without departing from the scope of the invention. Therefore, the scope of protection of the present invention should be determined by the contents of the appended claims.

Claims

1. A method for constructing thickness and grayscale functions, characterized in that, Includes the following steps: According to the preset thickness difference, a preset number of samples of different thicknesses are made, and the samples of different thicknesses are arranged in a stepped manner to form a stepped sample. X-ray scanning was performed on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of the samples of different thicknesses. The grayscale images in the grayscale image set are subjected to localization processing to obtain the position coordinates of each pixel in the grayscale image in the plane of the grayscale image, forming pixel comprehensive information; wherein, the pixel comprehensive information includes the position of the pixel and the corresponding grayscale value; Based on the pixel information, regression processing is performed on the thickness and gray value corresponding to the pixel at a specific position of the stepped sample to form a position-related thickness and gray value function.

2. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, After performing regression processing on the thickness corresponding to a pixel at a specific location on the stepped sample and the grayscale value corresponding to the thickness based on the pixel comprehensive information to form a position-dependent function of thickness and grayscale, the method further includes: X-ray scanning is performed on the test sample to obtain a grayscale image of the test sample; The grayscale image of the test sample is subjected to positioning processing. Based on the grayscale values ​​at each position of the grayscale image of the test sample, the thickness corresponding to the grayscale values ​​at each position is calculated using the thickness and grayscale function, thereby obtaining the thickness distribution on the entire surface of the test sample.

3. The method for constructing the thickness and grayscale function according to claim 2, characterized in that, The material of the sample is the same as that of the test sample; and the thickness of the test sample includes the thickness of its internal defects; the thickness of the test sample is within the thickness range of the stepped sample. The width of the sample is greater than or equal to the width of the test specimen; The length of the sample is greater than or equal to the length of the test specimen.

4. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, The sample does not contain dense defects.

5. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, The preset thickness difference is 1mm to 5mm.

6. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, The preset quantity is at least 3.

7. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, In the process of preparing a predetermined number of samples of different thicknesses according to a predetermined thickness difference, and arranging the samples of different thicknesses in a stepped manner to form a stepped sample, Samples of different thicknesses are arranged in a stepped manner, either in order of increasing thickness or in order of decreasing thickness, to form stepped samples.

8. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, In the process of performing X-ray scanning on samples of different thicknesses in the stepped sample to obtain a grayscale image set composed of grayscale images of samples of different thicknesses, X-ray scanning was performed on samples of different thicknesses in the stepped sample.

9. The method for constructing the thickness and grayscale function according to claim 1, characterized in that, The step of performing localization processing on the grayscale images in the grayscale image set to obtain the position coordinates of each pixel in the grayscale image on the plane of the grayscale image, forming comprehensive pixel information, includes: Using the position perpendicular to the ray source as the origin of the coordinate system, the position of the pixel in the x-direction is labeled as i, and the position in the y-direction is labeled as j, resulting in the position label of the pixel as P(i,j); The position P(i,j) of the pixel and the gray value G corresponding to the position P(i,j) are used as the pixel comprehensive information.

10. The method for constructing the thickness and grayscale function according to claim 9, characterized in that, The step of performing regression processing on the thickness and grayscale value corresponding to a pixel at a specific location of the stepped sample based on the pixel comprehensive information to form a position-related thickness and grayscale function includes: The specimens of different thicknesses in the stepped specimen are numbered, and the thickness of the specimen corresponding to each number is marked as T; For the pixel position P(i,j) of the stepped sample, establish a data correspondence between the thickness T(i,j) and gray value G(i,j) corresponding to the position P(i,j); Based on the data correspondence, regression processing is performed on the thickness T(i,j) and the gray value G(i,j) to obtain the functional relationship between the thickness T(i,j) and the gray value G(i,j) related to the position P(i,j): G(i,j) = f(T(i,j)).

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