A detection method for the local forming condition of large pipe fittings

Abstract

The present invention relates to the technical field of detecting the local forming conditions of pipe fittings, and specifically to a method for detecting the local forming conditions of large pipe fittings. The detection method, through the synergy of a machine vision system and an image processing algorithm, can collect images of the end faces of pipe fittings in real time, calculate the gap value between the pipe fitting contour and a standard circle, and compare it with a preset threshold value to automatically determine the forming quality. Specifically, the method for detecting the local forming conditions of large pipe fittings of the present invention has the following advantages: non-contact detection: it can avoid surface damage of the pipe fitting caused by mechanical contact, and the accuracy is improved to 99%; fully automated process: the detection time is shortened to 2 seconds / time, the production line does not need to be shut down, and the efficiency is improved by 90%; multi-dimensional parameter measurement: it can synchronously obtain the gap value and the straight edge length, and the ovality error is reduced to less than 2%.

Description

Technical Field

[0001] The present invention relates to the technical field of detecting the local forming condition of pipe fittings, and in particular to a method for detecting the local forming condition of large pipe fittings. Background Art

[0002] As the industry's demand for precision in large pipes (such as oil and gas pipelines and marine structural pipes) continues to increase, the JCO (JCO progressive forming) process has become widely used due to its suitability for the efficient forming of large-diameter straight seam welded pipes. However, local deformation of the pipe during the JCO forming process directly affects the ovality and geometric accuracy of the final formed pipe. Therefore, accurately and in real time detecting the local forming state of the pipe has become a key technical challenge in improving pipe quality.

[0003] Currently, the industry generally adopts a manual inspection method: after each forming pass, the operator uses a standard arc template to fit the surface of the pipe fitting and uses a feeler gauge to measure the gap between the template and the pipe fitting to evaluate the forming quality. This method is highly dependent on manual experience and has the following problems in actual operation:

[0004] 1. Low detection efficiency: Each test requires downtime, which takes about 10-15 seconds per test, seriously affecting the continuity of the production line;

[0005] 2. Insufficient accuracy: Manual measurement is easily affected by subjective factors, and the feeler gauge can only measure the gap at a local point, and cannot capture the overall profile deviation of the pipe (such as the length of the straight edge), resulting in the subsequent ovality exceeding the standard (see Figure 8 Example);

[0006] 3. Lack of automation: It relies on manual intervention and cannot be integrated with automated production lines, which is not in line with the development trend of Industry 4.0. Summary of the Invention

[0007] The purpose of this invention is to provide a method for detecting the local forming condition of large pipe fittings. The design method uses a machine vision system (camera, light source, displacement sensor) and an image processing algorithm (see Figure 5 System diagram), which can capture the end face image of the pipe in real time, calculate the gap between its contour and the standard circle, and compare it with the preset threshold to automatically determine the forming quality.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for detecting the local forming condition of a large pipe fitting comprises the following steps:

[0010] Step 1: Read the standard pipe diameter information and reasonable gap value information and input them into the processor;

[0011] Step 2: Adjust the light source and camera position and angle to ensure effective acquisition of the image of the pipe being tested;

[0012] Step 3: Determine whether camera calibration is required. If so, proceed to step 4; otherwise, proceed to step 5.

[0013] Step 4: Calibrate the relative position information and scaling ratio between the pipe end face and the camera face through the calibration module, and store the information in the processor;

[0014] Step 5: Determine whether the movement sensor sends a signal that the pipe to be tested has entered the detection position. If so, proceed to step 6; otherwise, wait for 0.1s and repeat step 5;

[0015] Step 6: Collect the tube end face image;

[0016] Step 7: The collected image information is transmitted to the processor and processed by the image processing program to obtain image features;

[0017] Step 8: Based on the image features obtained in step 7, the data calculation module calculates the gap between the pipe contour and the standard circle, and compares it with the reasonable gap value to determine whether the forming condition is qualified. The obtained image and test results are then displayed on the monitor;

[0018] Step 9: Store the acquired image and detection results, and determine whether to end the detection. If so, execute step 10; otherwise, execute step 5;

[0019] Step 10: End.

[0020] Furthermore, in step three, the calibration process is implemented as follows:

[0021] Step a: Read the standard calibration plate information;

[0022] Step b: obtaining an image of a standard calibration plate that coincides with the end face of the tube blank and transmitting it to a processor;

[0023] Step c: Use corner detection method to detect all corner points in the image;

[0024] Step d: Determine whether the number of detected corner points is the same as the number of corner points on the standard calibration plate. If so, execute step e; if not, execute step b to re-acquire the calibration image.

[0025] Step e: obtaining the relative position information and scaling ratio between the end face of the pipe fitting and the camera face through a perspective transformation algorithm, and storing the information;

[0026] Step f: End.

[0027] Furthermore, in step seven, the image processing process is implemented in the following specific steps:

[0028] Step a: Read image information;

[0029] Step b: perform threshold segmentation on the image using a threshold segmentation algorithm written according to image features;

[0030] Step c: Use the algorithm for removing small areas to remove the small spots in the image after threshold segmentation;

[0031] Step d: Use edge detection algorithm to detect the image contour and save the contour information in coordinate form;

[0032] Step e: End.

[0033] Furthermore, in step eight, the data calculation process is implemented in the following specific steps:

[0034] Step a: Read the contour coordinate information;

[0035] Step b: Use the algorithm for finding the maximum curvature coordinates to find the coordinates of the maximum curvature position in the contour;

[0036] Step c: Using the algorithm for obtaining the maximum gap in combination with the standard circle radius, the gap value at the position with the maximum curvature under the standard circle radius is obtained, which is the maximum gap value;

[0037] Step d: End.

[0038] The detection device used in the detection method of the local forming condition of a large pipe fitting of the present invention includes a workbench, and a camera with a light source and a photosensitive element is respectively arranged above the workbench. The light source is connected to the camera via a transmission line so that the light source can flash simultaneously when the camera takes a picture. The processor is connected to the camera via a transmission line, and the displacement sensor is connected to the processor via a transmission line. A slide rail is installed on the workbench, and the slide rail can facilitate the adjustment of the position of the camera and the light source.

[0039] The beneficial effects of the present invention are as follows: a method for detecting the local forming conditions of large pipe fittings provided by the present invention, through the synergy of a machine vision system (camera, light source, displacement sensor) and an image processing algorithm, can collect images of the end faces of pipe fittings in real time, calculate the gap value between the pipe fitting contour and the standard circle, and compare it with a preset threshold value to automatically determine the forming quality. Specifically, the method for detecting the local forming conditions of large pipe fittings of the present invention has the following advantages: non-contact detection: it can avoid surface damage to the pipe fittings caused by mechanical contact, and the accuracy is improved to 99%; fully automated process: the detection time is shortened to 2 seconds / time, the production line does not need to be shut down, and the efficiency is improved by 90%; multi-dimensional parameter measurement: it can simultaneously obtain the gap value and the straight edge length, and the ovality error is reduced to less than 2%. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the detection method of the present invention;

[0041] Figure 2 It is a flowchart of the calibration process in the present invention;

[0042] Figure 3 is a flowchart of the image processing process in the present invention;

[0043] Figure 4 A flowchart of the data calculation process in the present invention;

[0044] Figure 5 is a schematic diagram of the detection device used in the present invention;

[0045] Figure 6 The image of the calibration plate captured in the detection method of the present invention;

[0046] Figure 7 The image of the calibration plate after calibration in the detection method of the invention;

[0047] Figure 8 A local image of the pipe taken in the detection method of the invention;

[0048] Figure 9 A local image of the pipe after processing in the detection method of the invention;

[0049] Figure 5 The numbers in the figure are: 1-workbench; 2-light source; 3-camera; 4-processor; 5-displacement sensor; 6-pipe fitting. DETAILED DESCRIPTION

[0050] Specific embodiment 1: The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that: in the present invention, unless otherwise specified, all the implementation methods and preferred implementation methods mentioned herein can be combined with each other to form a new technical solution. In the present invention, unless otherwise specified, all the technical features and preferred features mentioned herein can be combined with each other to form a new technical solution. The "scope" disclosed in the present invention can be in the form of lower limit and upper limit, which can be one or more lower limits, and one or more upper limits respectively. Unless otherwise specified, the professional and scientific terms used in this article have the same meaning as those familiar to those skilled in the art. In addition, any method or material similar or equal to the recorded content can also be applied to the present invention.

[0051] As the instruction manual Figure 1 As shown, a method for detecting the local forming condition of a large pipe fitting according to the present invention comprises the following steps:

[0052] Step 1: Read the standard pipe diameter information and reasonable gap value information and input them into the processor;

[0053] Step 2: Adjust the light source and camera position and angle to ensure effective acquisition of the image of the pipe being tested;

[0054] Step 3: Determine whether camera calibration is required. If so, proceed to step 4; otherwise, proceed to step 5.

[0055] Step 4: Calibrate the relative position information and scaling ratio between the pipe end face and the camera face through the calibration module, and store the information in the processor;

[0056] As the instruction manual Figure 2 As shown in Figure 2, the specific steps for implementing the calibration process are as follows:

[0057] Step a: Read the standard calibration plate information;

[0058] Step b: Obtain an image of a standard calibration plate that coincides with the end face of the tube billet and transmit it to the processor; Step c: Use a corner detection method (findChessboardCorners function in the OpenCV computer vision function library) to detect all corner points in the image;

[0059] Step d: Determine whether the number of detected corner points is the same as the number of corner points on the standard calibration plate. If so, proceed to step e; if not, proceed to step b to re-acquire the calibration image; Step e: Obtain the relative position information and scaling ratio between the end face of the pipe fitting and the camera's image plane through the perspective transformation algorithm (cv2.getPerspectiveTransform function in the OpenCV computer vision function library) and store them;

[0060] Step f: End;

[0061] Step 5: Determine whether the movement sensor sends a signal that the pipe to be tested has entered the detection position. If so, proceed to step 6; otherwise, wait for 0.1s and repeat step 5;

[0062] Step 6: Collect the tube end face image;

[0063] Step 7: The collected image information is transmitted to the processor and processed by the image processing program to obtain image features;

[0064] As the instruction manual Figure 3 As shown in the figure, the specific steps of the image processing process are as follows:

[0065] Step a: Read image information;

[0066] Step b: Using a threshold segmentation algorithm developed based on image features (first, based on the characteristic that the tube end face has the highest brightness, i.e., the highest grayscale value, the image is processed to obtain the coordinates of all pixels in the region of interest. Specifically, starting from the first column from the left of the image, the sum of the grayscale values of a line graph with a length of twice the plate thickness is calculated, and the position with the maximum grayscale value sum of twice the plate thickness in the column is compared. Then, the second column, the third column, and so on to the end are counted, and the frequency of occurrence of the position with the maximum grayscale value sum in each column is counted to find the range with the highest frequency of occurrence. Then, on the basis of ensuring the continuity of the plate, the connected region of interest in the image is obtained with the highest grayscale value within the range with the highest frequency of occurrence. The number of occurrences of the grayscale values of all pixels in the region of interest is counted, and two concentrated grayscale value ranges will appear. The average grayscale values of these two ranges are calculated respectively, and the middle value of these two average values is used as the threshold. If the grayscale value of all pixels in the region of interest is less than the threshold, the grayscale value of the pixel is assigned to 0, and if it is greater than the threshold, the grayscale value of the pixel is assigned to 255). Threshold segmentation is performed on the image;

[0067] Step c: Use the algorithm written to remove small areas (the image after threshold segmentation sometimes has small, spotty, non-correlated areas. If the next step of edge detection is performed directly, contours unrelated to the required area will appear, which will seriously affect the subsequent processing process. To avoid this situation, it is necessary to remove small areas. The main process is to extract all connected areas with a grayscale value of 255 in the image after threshold segmentation and calculate the area of each area respectively, retain only the area with the largest area, and assign the grayscale value of the pixels in the remaining connected areas with smaller areas to 0. Then, starting from the first column from the left of the image, find the first and last pixels with a grayscale value of 255 from top to bottom, calculate the length between the two points and save it, then perform the second column detection until the last column, and then compare all saved lengths with 1 times the board width from the first column to find all columns with a length error of plus or minus 2. If all are within the range, the threshold segmentation is completed. If not, the average value of the vertical coordinates of all pixels with a grayscale value of 255 in the column is calculated. And take the average value as the benchmark, judge whether the difference between the average value of the vertical coordinates of the pixels with a grayscale value of 255 on both sides and the benchmark is within 2 times the horizontal coordinate difference. If so, the grayscale values of the pixels within the non-0.5 times distance on the upper and lower sides are assigned to 0 with the vertical coordinate of the column average value as the center. If not, judge whether the highest pixel point with a grayscale value of 255 in the first column is less than the highest pixel point with a grayscale value of 255 in the last column. If so, and the horizontal coordinate of the column is less than the horizontal coordinate of the benchmark point, the benchmark point in the column is set to 0. The grayscale values of the points within the non-0.5 times distance on both sides of the vertical coordinate minus 2 times the horizontal coordinate are assigned to 0. If the horizontal coordinate of the column is greater than the horizontal coordinate of the reference point, the grayscale values of the pixels within the non-0.5 times distance on both sides of the vertical coordinate plus 2 times the horizontal coordinate of the reference point in the column are assigned to 0. If the highest pixel point of the vertical coordinate of the grayscale value in the first column is greater than the highest pixel point of the vertical coordinate of the grayscale value in the last column, the positive and negative signs of the above process are swapped, and finally the algorithm for removing small areas is completed) to remove the small spots in the image after threshold segmentation;

[0068] Step d: Use edge detection algorithm (retain only the grayscale values of pixels within the edge width of the area with the largest area, and assign all grayscale values of other pixels inside to 0) to detect the image contour and save the contour information in the form of coordinates;

[0069] Step e: End;

[0070] Step 8: Based on the image features obtained in step 7, the data calculation module calculates the gap between the pipe contour and the standard circle, and compares it with the reasonable gap value to determine whether the forming condition is qualified. The obtained image and test results are then displayed on the monitor;

[0071] As the instruction manual Figure 4 As shown in Figure 2, the implementation of the data calculation process has the following specific steps:

[0072] Step a: Read the contour coordinate information;

[0073] Step b: Use the algorithm for finding the maximum curvature coordinate (find the line connecting the highest pixel point with a grayscale value of 255 in the first column and the highest pixel point with a grayscale value of 255 in the last column) l The equation is used to calculate the average value of the horizontal and vertical coordinates of each column of pixels with a grayscale of 255 and calculate its average value to the line l The distance, the pixel with the maximum distance is the point with the maximum curvature A ) Find the coordinates of the position with maximum curvature in the contour;

[0074] Step c: Use the algorithm to find the maximum gap (through the pixel point A Making lines l perpendicular line l 2. Find the distance from the point with the maximum curvature on the line to the standard circle radius r point O , with this point as the center O 1 Make a standard circle and determine whether the circle is tangent to the contour. If not, O 1 point along l 2 Move up to the nearby area, continue to make a standard circle and determine whether the circle is tangent to the contour until it is tangent. If so, O 1 A length d minus r Combined with the standard circle radius, the gap value at the position with the maximum curvature under the standard circle radius is the maximum gap value.

[0075] Step d: End;

[0076] Step 9: Store the acquired image and detection results, and determine whether to end the detection. If so, execute step 10; otherwise, execute step 5;

[0077] Step 10: End.

[0078] It should be further pointed out that, as shown in Figure 5 of the specification of the present invention, the detection device adopted by the detection method of the present invention includes a workbench 1, and a camera 3 with a light source 2 and a photosensitive element is respectively arranged above the workbench 1. The light source 2 is connected to the camera 3 via a transmission line, so that the light source 2 can flash simultaneously when the camera 3 takes a picture, the processor 4 is connected to the camera 3 via a transmission line, and the displacement sensor 5 is connected to the processor 4 via a transmission line. At the same time, a slide rail is installed on the workbench 1, and the conveyor slide rail can facilitate the adjustment of the positions of the camera 3 and the light source 2.

[0079] It should be further pointed out that the key technical modules of the method for detecting the local forming condition of large pipe fittings of the present invention are as follows:

[0080] 1. Calibration module: This module can read the standard calibration plate information, obtain the standard calibration plate image that coincides with the tube end face, and transmit it to the processor. It uses the corner detection method to detect all corners in the image, and obtains the relative position information and scaling ratio between the tube end face and the camera through the perspective transformation algorithm. It stores the information and solves the camera distortion and spatial positioning problems, ensuring measurement accuracy (error <0.1mm).

[0081] 2. Image processing algorithm: It can read image information and perform threshold segmentation on the image using a threshold segmentation algorithm developed based on image features. It can also use the algorithm developed to remove small areas of speckled areas in the image after threshold segmentation. It can also use the edge detection algorithm to detect the image contour and save the contour information in the form of coordinates. It can accurately convert the forming conditions of the pipe during the forming process into a digital model, laying the foundation for fast and high-precision detection.

[0082] 3. Curvature calculation module: It can read the contour coordinate information, use the algorithm for calculating the maximum curvature coordinates to calculate the coordinates of the maximum curvature position in the contour, and use the algorithm for calculating the maximum gap in combination with the standard circle radius to calculate the gap value at the maximum curvature position under the standard circle radius, which is the maximum gap value. The result is compared with the read standard pipe diameter information and the reasonable gap value information, thereby improving the detection efficiency and accuracy.

[0083] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with the present profession can make slight changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for detecting the local forming condition of large pipe fittings, characterized in that: The steps include: Collect the end face image of the tube blank; The collected image information is transmitted to the processor and processed by the image processing program to obtain image features. Specifically, the process includes: reading the image information; performing threshold segmentation on the image using a threshold segmentation algorithm developed based on the image features; using the developed algorithm for removing small areas to remove small spots in the image after threshold segmentation; using an edge detection algorithm to detect the image contour and save the contour information in the form of coordinates; Based on the acquired image features, the data calculation module calculates the gap between the pipe contour and the standard circle, and compares it with the reasonable gap value to determine whether the forming condition is qualified; The algorithm for removing small areas has the following specific steps: Extract all connected areas with a grayscale value of 255 in the image after threshold segmentation and calculate the area of each area separately, retain only the area with the largest area, and assign the grayscale value of the pixels in the remaining connected areas with smaller areas to 0. Then, starting from the first column from the left of the image, find the first and last pixel points with a grayscale value of 255 from top to bottom, calculate the length between the two points and save it, then perform the second column detection until the last column, and then compare all saved lengths with 1 times the board width length starting from the first column, and find all columns with a length error of plus or minus 2. If all are within the range, the threshold segmentation is completed. If not, find the average of the vertical coordinates of all pixels with a grayscale value of 255, and use the average as a benchmark to judge the average of the vertical coordinates of the pixels with a grayscale value of 255 on both sides whose length errors are not within plus or minus 2. Is the difference between the mean and its benchmark within 2 times the horizontal coordinate difference? If so, the grayscale values of the pixels within the non-0.5 times distance on both sides are assigned to 0 with the vertical coordinate of the average value as the center. If not, determine whether the highest pixel point with the vertical coordinate of the first column grayscale value of 255 is smaller than the highest pixel point with the vertical coordinate of the last column grayscale value of 255. If so and the horizontal coordinate is smaller than the horizontal coordinate of the reference point, the grayscale values of the points within the non-0.5 times distance on both sides of the vertical coordinate minus 2 times the horizontal coordinate value are assigned to 0. If the horizontal coordinate is greater than the horizontal coordinate of the reference point, the grayscale values of the pixels within the non-0.5 times distance on both sides of the vertical coordinate plus 2 times the horizontal coordinate value are assigned to 0. If the highest pixel point with the vertical coordinate of the first column grayscale value is greater than the highest pixel point with the vertical coordinate of the last column grayscale value, swap the positive and negative signs, and finally complete the algorithm to remove small areas.

2. A method for detecting local forming conditions of large pipe fittings according to claim 1, characterized in that: The specific steps are as follows: Step 1: Read the standard pipe diameter information and reasonable gap value information and input them into the processor; Step 2: Adjust the light source and camera position and angle to ensure effective acquisition of the image of the pipe being tested; Step 3: Determine whether camera calibration is required. If so, proceed to step 4; otherwise, proceed to step 5. Step 4: Calibrate the relative position information and scaling ratio between the pipe end face and the camera face through the calibration module, and store the information in the processor; Step 5: Determine whether the movement sensor sends a signal that the pipe to be tested has entered the detection position. If so, proceed to step 6; otherwise, wait for 0.1s and repeat step 5; Step 6: Collect the tube end face image; Step 7: The collected image information is transmitted to the processor and processed by the image processing program to obtain image features; Step 8: Based on the image features obtained in step 7, the data calculation module calculates the gap between the pipe contour and the standard circle, and compares it with the reasonable gap value to determine whether the forming condition is qualified. The obtained image and test results are then displayed on the monitor; Step 9: Store the acquired image and detection results, and determine whether to end the detection. If so, proceed to step 10; Otherwise, go to step 5; Step 10: End.

3. A method for detecting local forming conditions of large pipe fittings according to claim 2, characterized in that: In step three, the calibration process is implemented as follows: Step a: Read the standard calibration plate information; Step b: obtaining an image of a standard calibration plate that coincides with the end face of the tube blank and transmitting it to a processor; Step c: Use corner detection method to detect all corner points in the image; Step d: Determine whether the number of detected corner points is the same as the number of corner points on the standard calibration plate. If so, execute step e; if not, execute step b to re-acquire the calibration image. Step e: obtaining the relative position information and scaling ratio between the end face of the pipe fitting and the camera face through a perspective transformation algorithm, and storing the information; Step f: End.

4. A method for detecting local forming conditions of large pipe fittings according to claim 3, characterized in that: In step eight, the data calculation process is implemented as follows: Step a: Read the contour coordinate information; Step b: Use the algorithm for finding the maximum curvature coordinates to find the coordinates of the maximum curvature position in the contour; Step c: Using the algorithm for obtaining the maximum gap in combination with the standard circle radius, the gap value at the position with the maximum curvature under the standard circle radius is obtained, which is the maximum gap value; Step d: End.

5. A method for detecting local forming conditions of large pipe fittings according to claim 4, characterized in that: The threshold segmentation algorithm has the following specific steps: First, based on the characteristic that the tube end face has the highest brightness, that is, the highest grayscale value, the image is processed to obtain the coordinates of all pixel points in the area of interest. Specifically, starting from the first column from the left of the image, the sum of the grayscale values of the line graph with a length of twice the plate thickness is calculated, and the position with the maximum sum of grayscale values of twice the plate thickness in each column is compared. Then, the second column, the third column, and so on to the end are processed, and the frequency of occurrence of the position with the maximum sum of grayscale values in each column is counted to find the range with the highest occurrence frequency. Then, on the basis of ensuring the continuity of the plate, the connected area of interest in the image is obtained with the highest grayscale value within the range with the highest occurrence frequency. Count the number of occurrences of the grayscale values of all pixels in the region of interest. There will be two concentrated grayscale value ranges. The average grayscale values of these two ranges are calculated respectively. The middle value of these two average values is used as the threshold. If the grayscale value of all pixels in the region of interest is less than the threshold, the grayscale value of the pixel is assigned to 0. If it is greater than the threshold, the grayscale value of the pixel is assigned to 255.

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