A method for obtaining shot scattering distribution function in shot peening process

Abstract

The present invention discloses a method for obtaining a shot scattering distribution function in a shot peening process. The shot stream ejected from the spray gun is scattered to form a shot peening strip. By cutting a section of the shot peening strip, the shooting device is used to shoot and scan the cut-off area along the length direction perpendicular to the shot peening strip according to a predetermined step length to obtain a plurality of captured images with overlapping areas. Then, the aforementioned multiple captured images are sorted, and an automatic image processing algorithm is used to distinguish the overlapping areas of the previous and next images and to splice the adjacent captured images in sequence, and finally a full spliced image of the multiple captured images is obtained. The full spliced image is pre-processed and the effective pixel points and their corresponding coordinate values are counted. According to the aforementioned statistical values, a normal distribution fitting is performed to obtain the distribution function of the scattered shot in the said area, thereby obtaining the said shot scattering distribution in the entire shot peening strip.

Description

Technical Field

[0001] The present invention relates to a shot peening process for strengthening the surface of a metal material, and in particular to a method for obtaining a shot scattering distribution function in the shot peening process. Background Art

[0002] Shot peening processes primarily include shot strengthening and shot forming. Both involve spraying a stream of projectiles through a spray gun, causing them to scatter and impact the surface of metal parts, generating a corresponding residual stress field on the metal parts, thereby strengthening and enhancing the fatigue strength of the metal parts. Therefore, understanding the distribution of the scattered projectiles during shot peening under certain process parameters is crucial for subsequent analysis of the residual stress field in metal parts.

[0003] Currently, using image recognition algorithms to analyze and determine the distribution of scattered shot is an effective method. However, the diameter of craters in shot peening, especially in shot peening reinforcement, is very small. To identify valid peening craters, the effective field of view of a single shot is very small if the camera is to maintain image resolution. Therefore, it is impossible to capture a single image of the entire shot-scattered area, making it impossible to accurately analyze the distribution of scattered shot using image recognition algorithms. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a method for obtaining the distribution function of shot scattering in the shot peening process based on digital image recognition technology. By accurately obtaining and processing the digital image of the shot scattering area, the distribution model of the shot can be obtained, and then the distribution function of the shot scattering in the shot peening process is obtained by fitting the Gaussian distribution function (also known as the normal distribution), and finally the distribution of the shot scattering is obtained.

[0005] Specifically, the present invention provides a method for obtaining a shot scattering distribution function in a shot peening process, wherein a stream of shots ejected from a blast gun scatters to form a shot peening strip, wherein the method comprises the following steps:

[0006] A region is cut out of the shot peening strip, and within the region, a photographing device is moved from one side of the region to the other side in a direction perpendicular to the length of the shot peening strip according to a predetermined step length to obtain a plurality of photographed images having overlapping regions;

[0007] Sorting the plurality of captured images according to a shooting order, using an automatic image processing algorithm to distinguish overlapping areas of the front and rear images and sequentially stitching adjacent captured images, ultimately obtaining a fully stitched image of the plurality of captured images;

[0008] The fully stitched image is preprocessed to count the effective pixels and their corresponding coordinate values, and a normal distribution fitting is performed based on the counted effective pixels and their coordinate values to obtain the distribution function of the scattered projectiles in the area;

[0009] The shot scattering distribution in the entire shot peening strip is obtained by calculating the shot scattering distribution function.

[0010] The method for obtaining the shot scattering distribution function in the shot peening process disclosed in the present invention can not only accurately identify effective shot craters while ensuring image resolution, but also obtain images of shot scattering areas with a large effective field of view, thereby ensuring the accuracy of the shot scattering distribution function obtained by using the image recognition algorithm, minimizing errors as much as possible, and realizing accurate analysis of the residual stress field of metal parts.

[0011] In addition, by intercepting part of the shot peening strip and fitting the shot distribution function, the overall shot scattering distribution in the shot peening process can be quickly obtained, thereby accurately analyzing the global residual stress field of the metal parts, greatly reducing cost and time and improving analysis efficiency.

[0012] According to one embodiment of the present invention, the region cut out from the shot peening strip includes two edge lines parallel to each other drawn on both sides of the shot peening strip along the length direction of the shot peening strip.

[0013] According to another embodiment of the present invention, the two edge lines are designed so that the area formed between them can contain at least 95% of the scattered pellets in the shot peening strip. By drawing an edge line that encompasses nearly all of the scattered pellets, the camera's movement is guided and blank areas near the shot peening strip are prevented from affecting image processing accuracy, thus reducing errors.

[0014] According to another embodiment of the present invention, the predetermined step size is set to be less than half the length of a single captured image. By setting the predetermined step size to be less than half the length of a single captured image, it is possible to ensure that there is a certain overlap between the front and back images, thereby ensuring smooth subsequent image stitching and obtaining a complete image of the captured area.

[0015] According to another embodiment of the present invention, within the interception area, the camera moves from one edge line to another in a direction perpendicular to the edge lines according to a predetermined step size to obtain multiple captured images with overlapping areas. Setting the edge lines as the starting and ending positions of the camera movement ensures that the projectiles within the interception area are effectively captured while reducing the capture of blank and ineffective areas, reducing space waste and unnecessary recognition computing resources, and improving efficiency.

[0016] According to another embodiment of the present invention, distinguishing the overlapping area of the front and back images using an automatic image processing algorithm includes: pre-processing the front and back images by binarization to obtain grayscale values of the front and back images, and calculating a matching degree table consistent with the pixel size of the front image using a template matching method based on the grayscale values according to the grayscale values;

[0017] The position with the maximum value in the matching table is the best matching position. The rear image is moved to the best matching position and a completely matched part is obtained to distinguish the overlapping area of the front and rear images.

[0018] According to another embodiment of the present invention, the method further includes: dividing the front image into a front part and an overlapping part according to the best matching position, dividing the rear image into an overlapping part and a rear part, and splicing the rear part to the front image based on the overlapping part of the front and rear images, thereby obtaining a spliced image of adjacent images.

[0019] According to another embodiment of the present invention, the distance between two edge lines is measured, and the pixel size of the captured image is calibrated based on this distance and the pixel distance between the two edge lines in the fully stitched image. By measuring the physical distance between the edge lines and the distance between the edge lines in the image, it is possible to establish a uniformity between the pixel size and the physical distance. This allows the accurate identification of overlapping areas, the coordinate values corresponding to each pixel value in the image, and the pixel displacement required for the front and back images to be moved during the stitching process based on the step size of the camera movement and the information fed back from the image.

[0020] According to another embodiment of the present invention, the process of preprocessing the fully stitched image includes: preprocessing the fully stitched image by averaging to reduce background noise and improve the signal-to-noise ratio of the image.

[0021] According to another embodiment of the present invention, the process of preprocessing the fully stitched image further includes: preprocessing the fully stitched image by binarization to enhance the brightness of the crater position in the fully stitched image, thereby segmenting out particles representing the crater.

[0022] According to another embodiment of the present invention, the process of pre-processing the fully stitched image further includes: pre-processing the fully stitched image by performing an image morphological opening operation to remove scratches in the background of the fully stitched image.

[0023] According to another embodiment of the present invention, the step of counting valid pixel points and their corresponding coordinate values includes: counting the pixel values at the initial position and the pixel values at each shooting position to obtain valid pixel points and their corresponding coordinate values, thereby finally obtaining the distribution of pixel values in the fully stitched image.

[0024] According to another embodiment of the present invention, the method for obtaining the shot scattering distribution function in the shot peening process also includes: performing least squares fitting of the quadratic distribution function based on the distribution of pixel values in the full stitched image, obtaining the standard deviation of the quadratic distribution function, and thus fitting the shot scattering distribution function in the area, and finally obtaining the shot scattering distribution of the entire shot peening process.

[0025] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0026] The positive progress effect of the present invention is:

[0027] According to the above embodiments of the present invention, a method for obtaining a shot scattering distribution function during a shot peening process is provided. By scanning and capturing a portion of the shot peening strip formed by the shot stream, the method not only ensures image resolution sufficient to effectively identify shot craters but also captures an image of the shot scattering area, reducing measurement errors and simplifying the measurement process. By fitting the shot scattering distribution within the scanned area with a normal distribution function, the shot scattering distribution within the shot peening strip is determined, enabling rapid and accurate analysis of the residual stress field in metal parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of cutting out a region according to a preferred embodiment of the present invention.

[0029] Figure 2 1 and 2 are multiple images captured by a capturing device according to a preferred embodiment of the present invention.

[0030] Figure 3 Flowchart of image stitching steps according to a preferred embodiment of the present invention.

[0031] Figure 4 The original images of the front and back images in two adjacent captured images.

[0032] Figure 5 The image is the binarized image of the front image and the back image in two adjacent captured images.

[0033] Figure 6 Schematic diagram of the best matching position determined according to a preferred embodiment of the present invention.

[0034] Figure 7 Schematic diagram of a fully stitched image of a cropped area according to a preferred embodiment of the present invention.

[0035] Figure 8 for Figure 7 The schematic diagram of the fully stitched image after preprocessing, binarization and morphological opening operation is shown.

[0036] Figure 9 For use Figure 7 Schematic diagram of the pellet scatter distribution function fit for the full mosaic image shown. DETAILED DESCRIPTION

[0037] The preferred embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Please note that the following description is only exemplary and does not limit the scope of protection of the present invention. Any other similar situations will also fall within the scope of protection of the present invention.

[0038] In the following detailed description, directional and sequential terms, such as "front," "back," "X-axis direction," "Y-axis direction," and "Z-axis direction," are used with reference to the order and directions depicted in the accompanying drawings. The images of embodiments of the present invention can be positioned in a variety of different orientations and orders, and the directional and sequential terms are used for illustrative purposes only and are not intended to be limiting.

[0039] The term "image binarization" used in this article refers to setting the grayscale values of pixels on an image to 0 or 255, and by selecting an appropriate threshold value, making the entire image appear distinctly black and white. This results in a binary image that reflects both the overall and local features of the image, facilitating the segmentation of particles containing the target object in the image. The term "image grayscale-based template matching" used in this article refers to performing grayscale-based template matching using the front image as the target image and the back image as the template image. After the template matching calculation, a matching degree table with the same pixel size as the front image can be obtained. The higher the matching degree between the front and back images, the higher the grayscale value corresponding to that location. The term "mean processing" used in this article refers to subtracting the average pixel value of the image from the pixel value at each location in the image.

[0040] The term "morphological opening" used in this article can also be understood as morphological opening processing, which refers to first corroding the image and then dilating the corroded image. The specific process is to first use a structuring element (such as a 3×3 matrix) to scan each pixel in the image. Each pixel in the structuring element is subjected to an "AND" operation with the pixels it covers. If both are 1, the pixel is 1, otherwise it is 0. In the image, if there is a point in the image center and its neighborhood that is not a black point, the point is corroded into a white point. Then, each pixel in the image is scanned with the structuring element. Each pixel in the structuring element is subjected to an "OR" operation with the pixels it covers. If both are 0, the pixel is 0, otherwise it is 1.

[0041] The shot peening process primarily involves shot strengthening and shot forming. Specifically, shots are ejected from a spray gun at high speed, driven by airflow, and scattered into a peening strip. The scattered shots impact the surface of metal parts, creating craters and generating a corresponding residual stress field at the impact area. Analyzing this residual stress field can help understand the surface strengthening and fatigue strength improvement of metal parts.

[0042] The present invention is based on the concept that the distribution of impact points between scattered projectiles and metal surfaces typically follows a Gaussian density distribution function. Therefore, by calculating the parameters involved in this Gaussian density distribution function, the projectile scattering distribution function during the shot peening process can be derived. This allows understanding the distribution of scattered projectiles under certain process parameters and, in turn, allows analysis of the residual stress field generated within metal parts after the shot peening process.

[0043] Therefore, it can be seen that obtaining the distribution function of scattered shot during the shot peening process is very important for analyzing the residual stress field generated by metal parts after shot peening, as well as understanding the surface strengthening effect and fatigue strength improvement of metal parts. Therefore, it is necessary to analyze the Gaussian density distribution function involved in the distribution of scattered shot during the shot peening process and clearly calculate and solve the various parameters involved in the Gaussian density distribution function. The detailed analysis is described below.

[0044] Generally speaking, the projectile flows out of the spray gun at high speed under the influence of the airflow, and is distributed in a two-dimensional Gaussian density distribution function with the center axis of the shot as the origin, randomly impacting the surface of the metal part to be processed. In the Cartesian coordinate system, the two-dimensional Gaussian density distribution function is shown as follows:

[0045]

[0046] Where σ1 is the standard deviation of the distribution in the X-axis direction, σ2 is the standard deviation of the distribution in the Y-axis direction, μ1 is the original position in the X-axis direction, μ2 is the original position in the Y-axis direction, and ρ is the correlation coefficient between the X-axis and Y-axis directions.

[0047] In the shot peening shot flow distribution, the default direction of the spray gun axis is the Z axis direction, and the forward direction of the spray gun is the X axis direction. From this, the Y axis direction, which is perpendicular to both the X axis and the Z axis direction, can be determined. The distribution of scattered shot in the X axis direction and the Y axis direction is independent, so the value of the correlation coefficient ρ in formula (1) is 0. At the same time, the shot flow can be regarded as a cone and is rotationally symmetric about the center of the cone. The coordinate origin and the Z axis are both located on the center line of the cone, so:

[0048] σ1=σ2=σ (2)

[0049] μ1=μ2=0 (3)

[0050] Therefore, combining equations (1) to (3), the two-dimensional Gaussian density distribution function in equation (1) can be simplified to the following equation:

[0051]

[0052] Formula (4) is the two-dimensional Gaussian density distribution function of scattered projectiles in the shot peening process in the Cartesian coordinate system. For the polar coordinate system, r 2 =x 2 +y 2 , then the above formula (4) can be further simplified as:

[0053]

[0054] It can be seen from formula (5) that the specific distribution of scattered projectiles in the shot peening process depends on the size of the standard deviation σ.

[0055] Based on the digital image of the shot peening strip, the position of the scattered shot can be accurately obtained through the digital image processing recognition algorithm, and the value of the standard deviation σ in formula (5) can be calculated by fitting, thereby obtaining the distribution function of the scattered shot.

[0056] Specifically, a section of the shot peening strip is cut out, and the camera scans the cutout area from one side to the other in a direction perpendicular to the shot peening strip at a predetermined step length, obtaining multiple captured images with overlapping areas. These images are then sorted, and an automatic image processing algorithm is used to stitch adjacent images together, ultimately obtaining a fully stitched image showing the cutout area. The fully stitched image is then preprocessed, and the valid pixels and their corresponding coordinates are counted to calculate the standard deviation σ, ultimately fitting a distribution function for the scattered shots within the area. Finally, based on the fitted scattered shot distribution function, the scattered shot distribution within the entire shot peening strip is calculated. The specific process will be further described below in conjunction with the accompanying drawings in the specification.

[0057] like Figure 1 As shown, the stream of pellets ejected from the spray gun scatters to form a shot stripe. In this disclosure, the direction of the pellets formed as the spray gun advances is defined as the length of the shot stripe, and two parallel edge lines are drawn on either side of the shot stripe. These two edge lines need to encompass as much of the shot stripe as possible to facilitate pixel calibration of the image. Specifically, the two edge lines are configured to encompass at least 95% of the scattered pellets in the shot stripe.

[0058] Then, perpendicular to the shot peening strip, cut out an area from the shot peening strip, which includes the two drawn edge lines. Start shooting and scanning from one side of the cut-off area. Preferably, the images taken at the starting position and the final shooting position can include the hand-drawn edge lines. After each image is taken, the shooting device moves a predetermined step length in the direction perpendicular to the strip area until it moves to the final shooting position. Preferably, the shooting device moves and shoots in the direction perpendicular to the edge line. The predetermined step length of each movement of the shooting device can be fixed or not. Preferably, the predetermined step length of each movement of the shooting device is less than half of the length of the field of view of a single shot. As an example, assuming that the field of view size of the image captured by the shooting device in a single shot is L*W, and the predetermined step length of each movement is △L, then △L<0.5*L is required. In this way, the fields of view of the images taken at the front and rear positions will have overlapping parts, and the overlapping parts will appear in the images taken at the front and rear positions. When the shooting device moves to the other side of the cut-off area, the next step of moving shooting will be stopped after the shooting is completed.

[0059] For a section of the shot peening strip, multiple images with overlapping areas can be obtained by the above-mentioned method of gradually capturing images. Figure 2 Then, the multiple images are sorted, and the overlapping areas in adjacent images are identified through an automatic image processing algorithm. The multiple images are then stitched together to obtain a fully stitched image of the multiple images. The specific process is as follows.

[0060] like Figure 3 As shown, firstly, the multiple images taken are arranged. Preferably, the multiple images taken are arranged in front and back according to the order in which the shooting device takes the images. In the front and back images, based on the above-mentioned shooting and scanning strategy, there will be a certain overlap area in the front and back images. Subsequently, in the image sequence, according to the order relationship of the front and back images, the front and back images are binarized to obtain the grayscale values of the front and back images, and the main features of the image, the scattered projectiles and the scratches in the background, are separated from the background, and the intensity of these main features is increased to facilitate finding the overlap area of the front and back images. Specifically, the binarization of the image is to set the grayscale value of the pixel point on the image to 0 or 255, and to make the entire image present an obvious black and white effect through appropriate threshold selection, thereby obtaining a binarized image that can reflect the overall and local features of the image, and to separate the particles where the craters are located in the shot image. Exemplarily, two adjacent shot images taken before and after are randomly selected, and the front and back adjacent shot images before binarization are respectively as shown. Figure 4 As shown in the left and right figures in the figure, the front and back adjacent images after binarization are respectively as follows Figure 5 As shown in the left and right figures.

[0061] Next, the distance between the two edge lines is measured, and the pixel size of the captured image is calibrated based on the distance and the pixel distance between the two edge lines in the full stitched image. Then, the front and back images are preprocessed based on the grayscale matching analysis. Specifically, the front image is used as the target image and the back image is used as the template image, and template matching based on grayscale values is performed. After the template matching calculation, a matching degree table consistent with the pixel size of the front image can be obtained. The higher the matching degree in the front and back images, the higher the grayscale value corresponding to the position, such as Figure 6 As shown in the table, the position corresponding to the maximum value in the matching table is the best matching position of the front and back images. When the back image is moved to the best matching position, the overlapping parts of the front and back images will completely match.

[0062] Then the front and back images are divided and intercepted according to the best matching position, and the front and back images can be divided into three parts: the front part, the overlapping part and the back part. Specifically, the front image is divided into the front part and the overlapping part, and the back image is divided into the overlapping part and the back part, wherein the overlapping part of the front image and the back image completely overlap. Then, based on the distinguished overlapping part, the front part, the overlapping part and the back part of the front and back images are spliced. Preferably, the front image in the adjacent image is retained, the overlapping part in the back image is removed, and the remaining part of the back image is spliced onto the front image. Then, the above-mentioned pre-processing and splicing processing is repeated for the images in the image sequence to complete the splicing of multiple captured images, and finally a spliced image of all captured graphics is obtained, that is, a full spliced image of the entire intercepted area is obtained, such as Figure 7 shown.

[0063] Subsequently, the aforementioned fully stitched image is preprocessed. Specifically, the preprocessing of the fully stitched image includes averaging the fully stitched image to remove background noise and improve the signal-to-noise ratio of the image. Specifically, the averaging operation on the image is to use the pixel values at each position of the image minus the pixel average of the image. The preprocessed fully stitched image is then binarized. Furthermore, a morphological opening operation is performed on the binarized fully stitched image to remove impurities such as scratches in the image background through the opening operation. The morphological opening operation here is to first erode the image and then dilate the eroded image. The specific process is to first use a structural element (such as a 3×3 matrix) to scan each pixel in the image, and perform an "AND" operation with each pixel in the structural element and the pixel it covers. If both are 1, the pixel is 1, otherwise it is 0. In the image, it is represented as a point in the center and neighborhood of the image that is not a black point, and the point is eroded into a white point. Then use the structure element to scan each pixel in the image, and do an "OR" operation with each pixel in the structure element and the pixel it covers. If both are 0, the pixel is 0, otherwise it is 1. Finally, the full spliced image after the morphological opening operation is obtained as follows Figure 8 shown.

[0064] Based on the fully stitched image after the morphological opening operation, the horizontal effective pixel points and their coordinate values are counted perpendicular to the direction of the spray gun, as shown in Table 1.

[0065] Table 1:

[0066] 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 3 1 3 0 2 3 1 2 1 1 2 3 0 1 2 1 2 0

[0067] The 1st to 8th rows in Table 1 show the pixel matrix corresponding to the full stitched image after binarization and morphological opening operations. 1 represents a valid pixel and 0 represents an invalid pixel. The number of valid pixels in each column of the image is counted vertically along the direction of the spray gun. The statistical results are shown in the 9th row in Table 1. According to the established scanning strategy, the length corresponding to the change in the coordinates of adjacent pixel values should be a single moving step. For example, the single moving step is ΔL. Then according to Figure 1 The 9th row of the table in the figure can be used to obtain the coordinate values corresponding to each pixel value, as shown in Table 2.

[0068] Table 2:

[0069] (0，1) (ΔL, 2) (2ΔL, 1) (3ΔL, 3) (4ΔL, 1) (5ΔL, 3) (6ΔL, 0) (7ΔL, 2) (8ΔL, 3) (9ΔL, 1) (10ΔL, 2) (11ΔL, 1) (12ΔL, 1) (13ΔL, 2) (14ΔL, 3) (15ΔL, 0) (16ΔL, 1) (17ΔL, 2) (18ΔL, 1) (19ΔL, 2) (20ΔL, 0)

[0070] Finally, for the pixel values and their corresponding coordinate values in Table 2, the least squares fitting of the Gaussian density distribution function (also known as the normal distribution function) in formula (5) is used to obtain the standard deviation of the Gaussian density distribution function, and the distribution function of the scattered projectiles is fitted to obtain the distribution function of the scattered projectiles. Specifically, the values in Table 2 are fitted with the least squares fitting of the one-dimensional normal distribution, and the standard deviation of the normal distribution is calculated to be about 17. The final pixel statistics and fitting function are as follows: Figure 9 shown.

[0071] The above preferred embodiments of the present invention have the following beneficial technical effects:

[0072] 1. Through the designed shooting and scanning strategy, the image of the shot peening projectile scattering area can be accurately obtained on the basis of ensuring image resolution, thereby reducing analysis and estimation errors.

[0073] 2. By intercepting a portion of the shot peening strip and shooting and scanning, the distribution function of the scattered projectiles in the intercepted area can be quickly and accurately obtained. After analysis and calculation, the distribution state of the scattered projectiles in the shot peening strip can be effectively obtained, which simplifies the shooting experimental steps and greatly improves the efficiency of analysis, fitting and estimation.

[0074] 3. The specific distribution state of scattered projectiles in the shot peening strip obtained after analysis and calculation facilitates the subsequent analysis of the residual stress field of metal parts and understands the surface strengthening and fatigue strength improvement of metal materials, which has obvious engineering application significance.

[0075] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A method for obtaining a shot scatter distribution function in a shot peening process, wherein a stream of shots ejected from a blast gun scatters to form a shot peening strip, characterized in that: The method comprises the following steps: A region is cut out of the shot peening strip, and within the region, a photographing device is moved from one side of the region to the other side in a direction perpendicular to the length of the shot peening strip according to a predetermined step length to obtain a plurality of photographed images having overlapping regions; Sorting the plurality of captured images according to a shooting order, using an automatic image processing algorithm to distinguish overlapping areas of the front and rear images and sequentially stitching adjacent captured images, ultimately obtaining a fully stitched image of the plurality of captured images; Preprocessing the fully stitched image to count valid pixels and their corresponding coordinate values, performing normal distribution fitting based on the counted valid pixels and their coordinate values to obtain a distribution function of scattered projectiles in the area; The shot scattering distribution in the entire shot peening strip is obtained by calculation based on the shot scattering distribution function.

2. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 1, wherein: The region cut out on the shot peening strip includes two edge lines drawn on both sides of the shot peening strip along the length direction of the shot peening strip and parallel to each other.

3. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 2, wherein: The two edge lines are set so that the area formed therebetween can contain at least 95% of the scattered shots in the shot peening strip.

4. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 1, wherein: The predetermined step length is set to be smaller than half of the length of a single captured image.

5. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 2, wherein: In the region, the photographing device moves from one edge line to another edge line in a direction perpendicular to the edge line according to a predetermined step length, so as to obtain a plurality of photographed images with overlapping regions.

6. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 2, wherein: Using an automatic image processing algorithm to distinguish the overlapping area of the front and back images includes: pre-processing the front and back images by binarization to obtain grayscale values of the front and back images, and using a template matching method based on grayscale values to calculate a matching degree table consistent with the pixel size of the front image according to the grayscale values, The position of the maximum value in the matching degree table is the best matching position. The rear image is moved to the best matching position and a completely matched part is obtained to distinguish the overlapping area of the front and rear images.

7. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 6, wherein: The method further includes: dividing the front image into a front part and an overlapping part according to the best matching position, dividing the rear image into an overlapping part and a rear part, and splicing the rear part to the front image based on the overlapping part of the front and rear images, thereby obtaining a spliced image of adjacent images.

8. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 6, wherein: The distance between the two edge lines is measured, and the pixel size of the captured image is calibrated according to the distance and the pixel distance between the two edge lines in the full stitched image.

9. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 1, wherein: The process of preprocessing the fully stitched image includes: preprocessing the fully stitched image by averaging to reduce background noise and improve the signal-to-noise ratio of the image.

10. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 9, wherein: The process of pre-processing the full stitching image further includes: pre-processing the full stitching image by binarization to enhance the brightness of the crater position of the projectile in the full stitching image, thereby segmenting out particles representing the crater.

11. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 9, wherein: The process of pre-processing the full stitching image further includes: pre-processing the full stitching image through an image morphological opening operation, so as to remove scratches in the background of the full stitching image.

12. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 1, wherein: The step of counting effective pixel points and their corresponding coordinate values includes: counting the pixel values at the initial position and the pixel values at each shooting position to obtain effective pixel points and their corresponding coordinate values, thereby finally obtaining the distribution of pixel values in the full stitched image.

13. The method for obtaining a shot scattering distribution function in a shot peening process according to claim 12, wherein: The method further includes: performing least squares fitting of a quadratic distribution function based on the distribution of pixel values within the fully stitched image to obtain the standard deviation of the quadratic distribution function, thereby fitting a shot scattering distribution function within the area, and ultimately obtaining the shot scattering distribution of the entire shot peening process.

Images