Ultrasonic phased array full-focusing mechanical arm scanning method for seamless steel rail weld defects

Abstract

The invention provides an ultrasonic phased array full-focusing mechanical arm scanning method for seamless steel rail welding seam defects, and relates to the field of welding seam detection.The ultrasonic phased array full-focusing mechanical arm scanning method comprises the steps that S1, a flaw detection car advances on a steel rail route, a laser scanner synchronously scans a steel rail, and the flaw detection car stops when the surplus height of a steel rail welding seam is collected; s2, the upper computer corrects the preset scanning path point set through an automatic guide positioning algorithm to obtain a new scanning path point set, and a maintenance path is generated; s3, the upper computer drives the mechanical arm to clamp the ultrasonic phased array probe to scan along the maintenance path; and S4, the ultrasonic phased array displays a real-time full-focusing imaging picture according to the scanned and acquired signal data and can record the detected defects. According to the method, the full-section position of the steel rail weld defect is automatically scanned, the mechanical arm scanning path is corrected by using an automatic guide positioning technology, and the positioning error influence of a mechanical arm scanning program in automatic application is solved.

Description

Technical Field

[0001] The invention relates to the field of rail weld detection, and in particular to an ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects. Background Art

[0002] Tracks directly bear various loads from locomotives and vehicles, and are the basis for train operation. They bear the repeated effects of dynamic loads from locomotives and vehicles. In this process, rail welds, especially on seamless lines, may suffer various damages. If the damage is not detected and repaired in time, it will continue to develop, and rail weld breakage accidents are likely to occur. In recent years, nearly two-thirds of rail breakage accidents have occurred at rail welds and heat-affected zones, and flaw detection at rail welds has attracted great attention from railway regulatory authorities. Considering the short window time, the need to conduct a full coverage scan of seamless rail welds, and the common orientation defects represented by vertical crack defects are difficult to detect.

[0003] The existing technical solutions for non-destructive testing of seamless rails mainly include regular inspections by large rail flaw detection vehicles, electric flaw detection trolleys, hand-pushed flaw detection vehicles and manual inspections. Rail flaw detection vehicles and electric flaw detection trolleys are fast, but they cannot fully detect weld defects and there are detection blind spots. The hand-pushed flaw detection vehicle has a slow detection speed. Since the probe is fixed in position, it uses the traditional detection combination probe solution, and there is a blind spot in the bottom of the rail. At present, only manual handheld probes can achieve comprehensive defect detection of rail welds, but the detection rate of rail jaw excess height defects and rail bottom defects is low, and the detection efficiency of detection personnel working at night is low and they are prone to fatigue. The detection results are affected by factors such as the level, attitude and fatigue of the detection personnel. Rail flaw detection vehicles and electric flaw detection trolleys are fast, but they cannot fully detect weld defects and there are detection blind spots. The hand-pushed flaw detection vehicle has a slow detection speed. Since the probe is fixed in position, it uses the traditional detection combination probe solution, and there is a blind spot in the bottom of the rail. Currently, only manual handheld probes can achieve comprehensive defect detection on rail welds. However, the detection rate of rail jaw excess height defects and rail bottom defects is low. In addition, the detection efficiency of inspectors working at night is low and they are prone to fatigue. The detection results are affected by factors such as the inspectors' level, attitude and fatigue level.

[0004] Ultrasonic phased array full focusing technology is an advanced ultrasonic testing technology that combines high precision and high resolution. This technology records the ultrasonic pulses received by all chips corresponding to each chip, and uses this to delay and sum the time delays corresponding to each point in the imaging area to construct high-resolution imaging. As an advanced method in the field of ultrasonic imaging, the full focusing technology is unique in that it can use all chips of the entire aperture of the probe to generate full matrix capture through synthetic beam forming. The use of delay ensures focused imaging of each point in the area, thereby constructing an image with significantly improved clarity. This technology not only greatly enhances the resolution and signal-to-noise ratio of the image, but also makes the detection of tiny defects feasible. Traditional phased array detection has a good effect on defect imaging at the focus depth, but defect imaging at other locations will be distorted and the amplitude will be reduced, which is not conducive to defect quantification and tiny defect detection. The phased array full focusing imaging technology achieves high focus in the specified detection area, which is conducive to better simultaneous detection of a larger range.

[0005] Chinese patent application publication number CN116465965A discloses a rail defect detection method based on phased array ultrasound, the method comprising: the probe used to detect the rail is a 128-element phased array ultrasound probe, which is installed on a mechanical arm that can move along the longitudinal direction of the rail, and the array elements of the phased array ultrasound probe are divided into five groups, the first group includes array elements 1-50, which are used to scan the damage at the upper right side deflection angle of the rail head, the second group includes array elements 51-60, which are used to scan the damage near the right rail jaw of the rail, the third group includes array elements 61-68, which are used to scan the damage in the middle of the rail head, the rail waist and the middle of the rail bottom, the fourth group includes array elements 69-78, which are used to scan the damage near the left rail jaw of the rail, and the fifth group includes array elements 79-128, which are used to scan the damage at the upper left side deflection angle of the rail head, and the waveform in the rail is a transverse wave. The existing technical solution uses a mechanical arm to clamp a phased array probe and simultaneously excite multiple beams on a path on the rail top to detect damage to the rail head, rail waist, rail jaw, and rail bottom area of ​​the rail under water immersion conditions. It can be seen that the existing technology still has the following problems:

[0006] 1. The detection objects and detection range of the existing technology are insufficient: the detection range of the existing technology only has one detection route of the rail top, which belongs to the detection of internal defects of the rail. At the same time, the 128 array elements in the technology only detect the area inside the rail head and part of the rail bottom and rail waist area, and cannot detect the defects of the entire rail bottom area and weld excess height that are not mentioned in the current standards;

[0007] 2. The existing technology uses phased array sector scanning technology, which has limited detection and evaluation capabilities for tiny defects: The existing technology uses a phased array 128-element probe to perform sector scanning imaging based on the delay law. It has weak detection and evaluation capabilities for tiny defects, and weak image expression capabilities for defects, and cannot directly and quickly locate defects intuitively;

[0008] 3. The application scope of existing technologies is limited, and the degree of automation is not high: the existing technologies are based on water coupling, which requires the rails to be placed inside the water tank. At the same time, the installation method of the robotic arm is not proposed, and it only stays at the defect detection of the rails before leaving the factory. It is not suitable for the detection needs of the rails during actual service, and the detection method has great limitations;

[0009] 4. The detection precision and accuracy of the existing technology are subject to a high degree of interference: During the actual detection process, the rail will have position offset and deflection errors relative to the robotic arm, which will seriously interfere with the precision and accuracy of the robotic arm's fixed scanning path method. The existing technology has not yet proposed a method to solve the position error, and the detection precision and accuracy are subject to a high degree of interference.

[0010] The background description provided herein is for the purpose of generally presenting the context of the present disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and are not admitted to be prior art by inclusion in this section. Summary of the invention

[0011] In order to overcome the deficiencies in the background technology, the present invention discloses an ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects.

[0012] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0013] An ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects comprises the following steps:

[0014] S1. The flaw detection vehicle travels on the rail route, and the laser scanner synchronously scans the rail. When the laser scanner collects the residual height information of the rail weld, the flaw detection vehicle stops; the flaw detection vehicle is equipped with a laser scanner and a mechanical arm;

[0015] S2. The host computer calculates the deflection and offset of the current weld relative to the ideal weld position based on the three-dimensional coordinate data of the rail weld side collected by the laser scanner through the automatic guidance positioning algorithm, and corrects the preset scanning path point set to obtain a new scanning path point set to generate the maintenance path;

[0016] The automatic guidance positioning algorithm is specifically as follows: filtering and denoising the three-dimensional coordinate data of the rail weld collected by the laser scanner through a point cloud voxel gridding algorithm, and then obtaining a relative deflection deflection matrix and a relative offset offset matrix based on the denoised original point cloud through a point cloud matching algorithm, and transforming the coordinates of each point in the preset scanning path point set according to the above matrix to obtain a corrected new scanning path point set;

[0017] The preset scanning paths include: the first path is used to scan the rail head, rail waist and rail bottom center area near the weld; the second path is used to scan the rail waist area near the weld; the third path is used to scan the transition area between the rail waist and the rail bottom beside the weld; the fourth path is used to scan the bottom surface area of ​​the weld excess height side;

[0018] S3, the host computer drives the robotic arm to hold the ultrasonic phased array probe and scan along the inspection path;

[0019] S4. The ultrasonic phased array displays a real-time fully focused image based on the signal data collected by scanning and can record the detected defects.

[0020] Specifically, the point cloud voxel gridding algorithm includes the following steps:

[0021] T1. Determine the maximum and minimum values ​​of the point cloud data in the X-axis, Y-axis and Z-axis directions;

[0022] T2, based on the maximum and minimum values ​​in the X-axis, Y-axis and Z-axis directions and the preset voxel grid size, establish a voxel grid and assign an index to it;

[0023] T3. For each voxel grid, determine whether the number of points in the grid is less than a preset screening threshold thV; when the number of points in the voxel grid is less than the screening threshold thV, discard all points in the voxel grid;

[0024] T4. For each remaining voxel grid, the density value of the voxel grid is calculated according to the number of points in its surrounding neighboring voxel grids and its own grid;

[0025] T5. Output voxel grid with density values.

[0026] Specifically, the point cloud matching algorithm includes the following steps:

[0027] D1, initialize the translation matrix T as the identity matrix;

[0028] D2, initialize the rotation matrix R to the unit matrix;

[0029] D3. Perform the following iterative process until the convergence condition is met or the maximum number of iterations is reached:

[0030] a. Select sample points from the source point cloud source_cloud;

[0031] b. Find the nearest corresponding point for each sample point in the target point cloud target_cloud;

[0032] c. Calculate the optimal rigid body transformation, including translation and rotation, to minimize the distance between corresponding points in the source point cloud and the target point cloud;

[0033] d. Apply the calculated translation matrix T and rotation matrix R to update the position of the source point cloud source_cloud;

[0034] e. Calculate the transformation change of this iteration. If the change is less than the preset convergence threshold tolerance, the registration process is considered to have converged and the iteration is terminated.

[0035] f. Increase the number of iterations iter. If iter reaches the preset maximum number of iterations max_iterations, the iteration is terminated;

[0036] D4, output the final translation matrix T and rotation matrix R;

[0037] D5. Calculate the corrected scanning path point set. According to the above output results, the corrected scanning path point set can be calculated using the following formula:

[0038] P_correction = T*R*P_preset

[0039] Among them, P_corrected is the corrected scanning path point set, and P_preset is the preset scanning path point set.

[0040] Specifically, the first path is a straight line that coincides with the center line of the rail top, and the probe direction is parallel to the length direction of the rail during detection.

[0041] Specifically, the second path is a straight line detection path along the waist portion of the rail and perpendicular to the bottom surface of the rail.

[0042] Specifically, the third path is on the bottom surface of the rail, with the starting point being the detection position of the defect in the transition area between the rail waist and the rail bottom beside the weld, the probe is kept extending along the length of the rail at a preset angle, and the end point is a straight line where the wave points of the edge structure of the rail bottom are visible.

[0043] Specifically, the fourth path is on the bottom surface of the rail, the initial point and the detection direction are the detection position and probe direction of the defect where the center of the weld excess edge is located, the end point is the straight line of the detection position and probe direction of the visible weld excess edge structural wave, and the probe direction changes evenly during the movement.

[0044] Specifically, the maintenance path also includes a path generated by symmetrically copying the second path, the third path and the fourth path to the other three quadrants except the own quadrant and connecting them;

[0045] The quadrant division is specifically as follows: the rail can be divided into two sides along the axis according to the I-shaped structure on the side of the rail in the direction of the rail width, and then divided into two ends along the extension direction of the rail according to the two ends of the rail connected by the weld, and the entire area to be inspected is divided into four quadrants.

[0046] Specifically, step S1 specifically includes:

[0047] S11. Data acquisition: The laser scanner continuously scans the rail surface while the flaw detection vehicle is moving to obtain real-time 3D point cloud data;

[0048] S12, feature extraction: processing the three-dimensional point cloud data in real time to extract contour features of the rail surface;

[0049] S13, feature recognition: analyzing the contour features to identify features representing weld excess height; specifically including:

[0050] S131, detecting sudden changes in the rail surface profile;

[0051] S132, identifying abnormal shapes that do not conform to the standard rail profile;

[0052] S133, analyzing density changes of point cloud data;

[0053] S134, threshold determination: comparing the identified feature with a preset weld excess height feature threshold;

[0054] S14, stop signal triggering: if the identified feature exceeds the preset threshold, the system determines that the weld excess height has been detected and triggers a stop signal;

[0055] S15, the flaw detection vehicle stops: after receiving the stop signal, the control system in the upper computer immediately executes the braking procedure to make the flaw detection vehicle stop safely within the shortest distance;

[0056] S16, position confirmation: After the flaw detection vehicle stops, the system performs another precise scan to confirm the weld position.

[0057] Specifically, the flaw detection vehicle is also equipped with a water spraying device to spray water onto the surface of the rail to be tested to form a water film.

[0058] The ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects provided by the present invention comprises the following steps: S1, the flaw detection vehicle travels on the rail route, the laser scanner synchronously scans the rail, and the flaw detection vehicle stops when the laser scanner collects the residual height information of the rail weld; the flaw detection vehicle is loaded with a laser scanner and a mechanical arm; S2, the host computer calculates the deflection and offset of the current weld relative to the ideal weld position according to the three-dimensional coordinate data of the side of the rail weld collected by the laser scanner through the automatic guidance positioning algorithm, and corrects the preset scanning path point set to obtain a new scanning path point set to generate the maintenance path; S3, the host computer drives the mechanical arm to clamp the ultrasonic phased array probe and scan along the maintenance path; S4, the ultrasonic phased array displays a real-time fully focused imaging image according to the signal data collected by the scanning and can record the detected defects. The scanning method provided by the present invention realizes the automatic scanning of the full-section position of the rail weld defect, and realizes the detection of the residual height area of ​​the rail bottom and rail jaw weld compared with the existing detection, overcoming the disadvantage that the existing technology cannot scan the full section.

[0059] In addition, the present invention can identify welds and correct the robot arm scanning path through automatic guidance positioning technology, which solves the influence of positioning errors of the robot arm scanning program in automated applications, ensures the relative center offset and deflection between the flaw detection vehicle and the rail during operation, and ensures the accuracy and reproducibility of automated detection.

[0060] In addition, the present invention uses automatic guided positioning technology to perform full-section inspection and the full-process inspection time is 3 minutes, while the average time for manual inspection is 30 minutes. Compared with traditional manual inspection, it has obvious advantages in efficiency and avoids the interference of experience factors and personal status of manual inspection on the inspection results, effectively improving the inspection efficiency and accuracy.

[0061] In addition, the present invention provides a complete flaw detection vehicle detection system that can operate autonomously on a track line. The present invention only needs to use the system's water spraying device to spray water on the surface of the rail to be tested to form a water film to provide detection conditions, which solves the problem that the detection conditions of the prior art require immersion detection and the detection range is limited, thereby improving the convenience of detection.

[0062] In addition, the present invention uses phased array full focusing technology to detect and quantify vertical crack defects with a radius of 4.5 mm near the rail weld and transverse through holes with a diameter of 2 mm. Repeated tests show that all defects are detected, and the quantitative effect is 5.4% more accurate than the sector scanning data results, effectively improving the accuracy of detection.

[0063] In addition, the relative errors between the defect center detection amplitudes in actual operation of the present invention and the defect center detection amplitudes under the preset detection process are all within 8%, which effectively controls the experimental error. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 It is a schematic diagram of a process of an ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects provided by an embodiment of the present invention;

[0066] Figure 2 It is a schematic diagram of the overall hardware structure of an ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects provided by an embodiment of the present invention;

[0067] Figure 3 is a schematic structural diagram of the main part of a flaw detection vehicle provided according to an embodiment of the present invention;

[0068] Figure 4 is a schematic diagram of the structure of the handlebar portion of a flaw detection vehicle provided according to an embodiment of the present invention;

[0069] Figure 5 It is a schematic diagram of the structure of a partial installation platform of a flaw detection vehicle provided in an embodiment of the present invention;

[0070] Figure 6 It is a schematic diagram of the structure of the elevated frame of a flaw detection vehicle provided according to an embodiment of the present invention;

[0071] Figure 7 is a schematic diagram of a denoising comparison diagram provided according to an embodiment of the present invention; wherein, Figure 7 (a) is the point cloud before denoising. Figure 7 (b) is the point cloud after denoising;

[0072] Figure 8 is a schematic diagram of a detection path provided according to an embodiment of the present invention;

[0073] Fig. 9 is a schematic diagram of a first path detection area provided according to an embodiment of the present invention;

[0074] Fig.10 is a first path schematic diagram provided according to an embodiment of the present invention;

[0075] Fig.11 is a schematic diagram of an ultrasonic propagation path at a first path limit point provided according to an embodiment of the present invention;

[0076] Fig.12 is a schematic diagram of a second path detection area provided according to an embodiment of the present invention;

[0077] Fig.13 is a second path schematic diagram provided according to an embodiment of the present invention;

[0078] Fig.14 is a schematic diagram of the third path and fourth path detection areas provided according to an embodiment of the present invention;

[0079] Fig.15 is a schematic diagram of a third path provided according to an embodiment of the present invention;

[0080] Fig.16 is a fourth path schematic diagram provided according to an embodiment of the present invention;

[0081] Fig.17 It is a schematic diagram of an overall solution provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right", etc. indicating directions or positional relationships, they only correspond to the drawings of the present application for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction.

[0083] Embodiment 1

[0084] refer to Figure 1 This embodiment provides an ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects, which specifically includes the following steps:

[0085] S1. The flaw detection vehicle moves on the rail route, and the laser scanner scans the rail synchronously. When the laser scanner collects the residual height of the rail weld, the flaw detection vehicle stops;

[0086] The end of the mechanical arm is clamped with an ultrasonic phased array probe with 32 chip array elements; the ultrasonic phased array probe is mounted on a 36° wedge block;

[0087] This embodiment uses a self-designed flaw detection vehicle that can travel on rails. The mechanical arm arranged on the flaw detection vehicle clamps the ultrasonic phased array probe with 32 chip array elements to automatically detect defects near the rail weld, especially crack defects. The phased array probe is mounted on a 36° wedge block, generates refracted shear waves in the rail to detect defects, and collects 1024 groups of signals excited and received by 32 chips for full-focus imaging to display the defect waveform.

[0088] refer to Figure 2 , Fig.17The overall structure equipped with a robotic arm and phased array is integrated on the designed flaw detection vehicle. A water tank for water supply and a generator to ensure power demand are placed on the flaw detection vehicle. The generator is responsible for the power supply of the entire vehicle hardware. The overall control of the robotic arm control cabinet, ultrasonic phased array and laser scanner is completed by the host computer laptop. When the flaw detection vehicle is traveling on the railway rail line, the laser scanner synchronously scans the rail. When the laser scanner collects key feature information such as the residual height of the rail weld, the flaw detection vehicle stops immediately, and the laser scanner imports the three-dimensional coordinate data of the side of the rail weld into the host computer.

[0089] The overall hardware structure of the detection method in this embodiment is as follows Figure 2 As shown, the flaw detection vehicle structure includes the following sections:

[0090] 1) Main plate

[0091] Considering that the experimental equipment can be used conveniently and flexibly on the actual rails, the appearance and structure of the detachable hand-pushed flaw detection vehicle are designed. After planning the position layout of each instrument on the vehicle surface, considering the weight of the vehicle itself, aluminum alloy and other materials are used to simulate the strength of the vehicle. Aluminum alloy is selected as the body material based on strength and lightness. In order to facilitate loading and transportation, the main body of the vehicle is divided into left and right parts, which are connected by bolts in the middle. Figure 3 .

[0092] 2)Handle plate

[0093] Considering the weight of the vehicle, the thrust, the convenience of transportation and disassembly, and combining ergonomics, we continuously adjusted the size and tilt angle of the handle as well as the disassembly method. Finally, we decided to use a round tube made of 45 steel and a diameter of 50mm. The handle is assembled from three parts, which can be disassembled and assembled separately, and connected to the vehicle body with bolts. Figure 4 .

[0094] 3) Install the platform block

[0095] Considering that the mounting platform needs to load the manipulator, has a large load-bearing capacity, and has strict requirements on deformation, the mounting platform is designed separately, and the material selection and strength verification are carried out. After investigation, it is decided to use Q235 as the material, and add ribs on the basis of the simply supported beam to reduce the deformation of the mounting platform to 0.01mm level. Figure 5 shown.

[0096] 4) Elevated frame

[0097] In order to facilitate viewing the measurement results during the measurement process, a heightening frame is added to raise the result display screen, making it easier for testers to observe the data and measurement status while pushing the vehicle. Figure 6 shown.

[0098] 5) Guardrail panels and overall model:

[0099] Taking the movement of the vehicle into consideration during the measurement process, guardrails are added to each instrument to limit the movement of the instrument, and the layout of the instrument on the vehicle is adjusted so that its center of gravity is centered.

[0100] The flaw detection vehicle described in this embodiment is driven by manpower, and can also be driven by installing an engine or a motor. Any method is acceptable as long as it can achieve the driving function, and is not limited here.

[0101] In this embodiment, laser contour scanning is used as the visual sensor. Depth cameras, binocular cameras, laser radars, etc. can also be used as the visual sensors of this device, which is not limited here.

[0102] The robot arm described in this example is an ABB-1200 six-axis robot arm for detection, and other robot arms can also be used for detection, which is not limited here.

[0103] Step S1 specifically includes:

[0104] S11. Data acquisition: The laser scanner continuously scans the rail surface while the flaw detection vehicle is moving to obtain real-time 3D point cloud data;

[0105] S12, feature extraction: processing the three-dimensional point cloud data in real time to extract contour features of the rail surface;

[0106] S13, feature recognition: analyzing the contour features to identify features representing weld excess height;

[0107] Specifically include:

[0108] S131, detecting sudden changes in the rail surface profile;

[0109] S132, identifying abnormal shapes that do not conform to the standard rail profile;

[0110] S133, analyzing density changes of point cloud data;

[0111] S134, threshold determination: comparing the identified feature with a preset weld excess height feature threshold;

[0112] The preset weld reinforcement feature threshold is determined based on a combination of one or more features of height difference, width or shape feature;

[0113] S14, stop signal triggering: if the identified feature exceeds the preset threshold, the system determines that the weld excess height has been detected and triggers a stop signal;

[0114] S15, NDT vehicle stops: After receiving the stop signal, the control system in the upper computer immediately executes the braking procedure to stop the NDT vehicle safely within the shortest distance.

[0115] In order to further ensure the accuracy of the scanning position, the position confirmation operation can also be performed after the flaw detection vehicle stops.

[0116] S16, position confirmation: After the flaw detection vehicle stops, the system performs another precise scan to confirm the weld position in preparation for subsequent testing.

[0117] S2. The host computer calculates the deflection and offset of the current weld relative to the ideal weld position based on the three-dimensional coordinate data of the rail weld side collected by the laser scanner through the automatic guidance positioning algorithm, and corrects the preset scanning path point set to obtain a new scanning path point set to generate the maintenance path;

[0118] The automatic guidance positioning algorithm is specifically as follows: filtering and denoising the three-dimensional coordinate data of the rail weld collected by the laser scanner through a point cloud voxel gridding algorithm, and then obtaining a relative deflection deflection matrix and a relative offset offset matrix based on the denoised original point cloud through a point cloud matching algorithm, and transforming the coordinates of each point in the preset scanning path point set according to the above matrix to obtain a corrected new scanning path point set, thereby completing the dynamic adjustment of the preset scanning path;

[0119] The ideal weld position is the position of the weld when the flaw detection vehicle is parked at the required distance according to the planned preset scanning path and the center plane of the flaw detection vehicle is parallel to the extension direction of the rail; at this time, the position of the midpoint of the bottom of the rail weld in the robot arm base coordinate system is x=230mm, y=0mm, z=-295mm, and the rail guide is parallel to the axis of the robot arm base coordinate system.

[0120] It can be understood that the path point set refers to a set of a series of discrete coordinate points that constitute a path, and these sets of discrete coordinate points can be connected continuously to form a path. This is a prior art and will not be described in detail here.

[0121] In order to cooperate with the ultrasonic testing robot arm system to realize the inspection and positioning of the seamless rail weld according to the planned specified program, it is necessary to provide offset correction for the robot arm programming to ensure that the center offset and deflection between the flaw detection vehicle and the rail occur relative to each other during operation, so as to ensure the accuracy and reproducibility of automated detection.

[0122] This method uses SmartRay 2D laser profiler to realize an automatic guidance positioning method based on 2D profile laser scanner. First, the collected 3D data coordinates of rail welds are imported into MATLAB, and the point cloud voxel gridding algorithm is used to filter and denoise the collected data to simplify the huge and cumbersome 3D point cloud data.

[0123] Specifically, a 3D voxel grid is created by using the input point cloud data, and then the centroid of all points in each voxel is used to approximate the other points in the voxel, so that all points in the voxel are finally represented by a centroid point. For the point cloud after all voxels are processed and filtered, the size of the voxel needs to be based on the actual data. After the denoising filter, some irrelevant point cloud data is deleted and the remaining point cloud coordinates are extracted, and the denoising process is completed. Figure 7 As shown, Figure 7 (a) is the point cloud before denoising. Figure 7 (b) is the point cloud after denoising.

[0124] The point cloud voxel gridding algorithm specifically includes the following steps:

[0125] T1. Determine the maximum and minimum values ​​of the point cloud data in the X-axis, Y-axis and Z-axis directions;

[0126] T2, based on the maximum and minimum values ​​in the X-axis, Y-axis and Z-axis directions and the preset voxel grid size, establish a voxel grid and assign an index to it;

[0127] T3. For each voxel grid, determine whether the number of points in the grid is less than a preset screening threshold thV; when the number of points in the voxel grid is less than the screening threshold thV, discard all points in the voxel grid;

[0128] T4. For each remaining voxel grid, the density value of the voxel grid is calculated according to the number of points in its surrounding neighboring voxel grids and its own grid;

[0129] T5. Output voxel grid with density values.

[0130] The point cloud matching algorithm specifically includes the following steps:

[0131] D1, initialize the translation matrix T as the identity matrix;

[0132] D2, initialize the rotation matrix R to the unit matrix;

[0133] D3. Perform the following iterative process until the convergence condition is met or the maximum number of iterations is reached:

[0134] a. Select sample points from the source point cloud source_cloud;

[0135] b. Find the nearest corresponding point for each sample point in the target point cloud target_cloud;

[0136] c. Calculate the optimal rigid body transformation, including translation and rotation, to minimize the distance between corresponding points in the source point cloud and the target point cloud;

[0137] d. Apply the calculated translation matrix T and rotation matrix R to update the position of the source point cloud source_cloud;

[0138] e. Calculate the transformation change of this iteration. If the change is less than the preset convergence threshold tolerance, the registration process is considered to have converged and the iteration is terminated.

[0139] f. Increase the number of iterations iter. If iter reaches the preset maximum number of iterations max_iterations, the iteration is terminated;

[0140] D4, output the final translation matrix T and rotation matrix R;

[0141] D5. Calculate the corrected scanning path point set. According to the above output results, the corrected scanning path point set can be calculated using the following formula:

[0142] P_correction = T*R*P_preset

[0143] Where P_corrected is the corrected scanning path point set, and P_preset is the preset scanning path point set.

[0144] The source point cloud source_cloud, the target point cloud target_cloud, the number of iterations max_iterations, and the convergence threshold tolerance are used to obtain the translation matrix T and rotation matrix R used to correct the path through the point cloud matching algorithm.

[0145] The target point cloud is obtained by modeling the rail and the weld as a whole and exporting point cloud data according to the position of the rail relative to the robot arm in an ideal situation.

[0146] The ideal situation is when the flaw detection vehicle stops at a required distance according to the planned preset scanning path and the center plane of the flaw detection vehicle is parallel to the extension direction of the rail.

[0147] The number of iterations max_iterations can be selected between 50 and 100. To ensure good matching accuracy and iteration rate, experiments show that the accuracy is good when the number of iterations is 80, and increasing the number of iterations will not significantly improve the accuracy.

[0148] The convergence threshold tolerance is 0.001-0.01, which can be comprehensively considered based on the actual calculation time and matching accuracy. In practice, 0.007 is selected for better calculation effect.

[0149] The point cloud data, the preset voxel grid size and the screening threshold thV are calculated according to the voxel grid filtering method to obtain a voxel grid with a density value;

[0150] The preset voxel grid size is 0.7-0.9, which needs to be adjusted according to the point cloud density of the actual data set and the required downsampling degree. The point cloud data of the collected rail welds is relatively dense, so selecting 0.8 has a better effect.

[0151] The screening threshold thV ranges from 0.01 to 0.1, and 0.05 is selected for calculation according to the actual processing situation.

[0152] This calculation process is applied to each point on the preset path to obtain the complete corrected scanning path. This corrected path is the new path that the robot arm actually needs to follow, that is, the inspection path, which takes into account the deviation of the actual position and direction of the rail. The host computer uses this inspection path to guide the robot arm to detect the rail weld, thereby realizing automatic guided positioning under the guidance of laser positioning.

[0153] The preset scanning paths include: the first path is used to scan the rail head, rail waist and rail bottom center area near the weld; the second path is used to scan the rail waist area near the weld; the third path is used to scan the transition area between the rail waist and the rail bottom beside the weld; the fourth path is used to scan the bottom surface area of ​​the weld excess height side;

[0154] The host computer uses the laser scanner to collect the three-dimensional coordinate data of the side of the rail weld to calculate the actual distance between the robot arm and the weld, and corrects the existing scanning path according to the offset deflection data, thereby achieving real-time position and posture adjustment of the detection path to ensure the accuracy of subsequent operations. Then, the robot arm control cabinet receives the host computer command, drives the robot arm to clamp the ultrasonic phased array probe, and executes the preset programming file along the corrected path. At the same time, the water pump draws clean water from the water tank and sprays it through the nozzle at the end of the robot arm to form a uniform water film on the surface of the rail weld to achieve the water coupling conditions required for ultrasonic detection and complete the detection of the rail weld.

[0155] In order to cooperate with the ultrasonic testing robot arm system to realize the inspection and positioning of the seamless rail weld according to the planned specified program, it is necessary to provide offset correction for the robot arm programming to ensure that the center offset and deflection between the flaw detection vehicle and the rail occur relative to each other during operation, so as to ensure the accuracy and reproducibility of automated detection.

[0156] The moving path of the robot arm for rail weld defect detection and the corresponding defect area containing cracks that can be detected near the rail weld are shown below. During the movement, interference collision areas such as rail welds and rail fasteners are avoided:

[0157] In this embodiment, path 1 is the first path, path 2 is the second path, path 3 is the third path, and path 4 is the fourth path. Figure 8 As shown;

[0158] The first path is a straight line that coincides with the center line of the rail top, and the probe direction is parallel to the length direction of the rail during detection.

[0159] (1) Check the rail head, rail waist and rail bottom center area near the weld, such as Fig. 9 As shown;

[0160] Path 1 is a straight line that coincides with the center line of the rail top. During detection, the probe direction is parallel to the length direction of the rail. Fig.10 By cutting the rail from the middle along the centerline of the rail top, the propagation path at the extreme endpoint can be drawn, as shown in Fig.11 As shown,

[0161] In a possible implementation, by calculation and reasonable extension, the farthest end of path 1 from the weld is 177 mm from the weld edge. In order to adapt to the symmetrical characteristics, symmetrical extension is performed. The length of path 1 is 177 mm on both sides of the center line of the top of the rail, with a total length of 354 mm. When the probe passes through the center line of the weld, it turns 180°.

[0162] The second path is a straight line detection path along the waist of the rail and perpendicular to the bottom surface of the rail.

[0163] (2) Check the rail waist area near the weld, such as Fig.12 As shown;

[0164] Because the detection is parallel to the defect on the opposite side of the rail, according to the detection principle, it is only necessary to expand the detection point to a straight line perpendicular to the bottom surface and fitting the rail waist to obtain the detection path 2 at the rail waist, such as Fig.13 It should be noted that the waist of the rail is a curved surface, but the curve is gentle, the wedge can move in a straight line, and the gap between the wedge and the rail is small and can be filled with water coupling agent.

[0165] The length of path 2 needs to consider the extreme positions at both ends, and there is a size limit for the tooling of the clamping wedge. According to the size of the clamping tooling designed by the present invention, the detection path needs to ensure that the tooling maintains a 5mm gap between the lower jaw surface of the rail head and the bottom surface of the rail. By drawing and simulating in solidworks software, it can be obtained that path 4 is 46.5mm away from the weld edge, the length is about 69mm, and the vertical distances from the highest point and the lowest point to the bottom surface of the rail are 114mm and 45mm respectively.

[0166] The third path is a straight line extending from the transition area between the rail waist and the rail bottom beside the weld along the incident point and the length of the rail at a first preset angle.

[0167] Specifically, the third path is on the rail bottom surface, with the starting point being the detection position of the defect in the transition area between the rail waist and the rail bottom beside the weld, and the probe is kept extending along the length of the rail at a first preset angle, and the end point is a straight line where the wave point of the edge structure of the rail bottom is visible;

[0168] In this embodiment, the first preset angle is 27.3°.

[0169] The fourth path is a straight line detection path formed by selecting multiple points on the bottom surface boundary line of the weld excess height side and using the simulated propagation path to deduce the incident points, and trying to make these incident points fall on a straight line.

[0170] Specifically, the fourth path is on the bottom surface of the rail, the initial point and the detection direction are the detection position and probe direction of the defect where the center of the weld excess edge is located, the end point is the straight line of the detection position and probe direction of the visible weld excess edge structural wave, and the probe direction changes evenly during the movement.

[0171] (3) Check the transition area between the rail waist and the rail bottom beside the weld and the bottom surface area of ​​the weld excess height side, such as Fig.14 shown.

[0172] At the bottom of the rail, the transition area between the rail waist and the rail bottom beside the weld can be inspected, and the bottom area on the weld excess height side can be inspected. When inspecting the bottom of the rail, due to the complex and changeable bottom surface, the incident point should be as close to the outer edge of the rail as possible. At the same time, when inspecting the bottom of the rail, the flow rate of water coupling agent should be increased to ensure that the gap between the wedge and the bottom of the rail is filled. At this time, the path should be mainly straight as much as possible. The curved undulating movement on the multiple curved surfaces of the rail bottom can be approximately abstracted as straight line movement. The actual robot clamping inspection process will be assisted by a spring system.

[0173] ① Detection path 3 of the transition area between the rail waist and the rail bottom beside the weld, such as Fig.15 shown.

[0174] According to the propagation path Figure 8 It can be seen that because the detection is mainly aimed at the cracks at this transition position, the range to be detected is very small, and the change in the orientation angle caused by the movement of the probe in space can be ignored. The incident point in the transition position area between the rail waist and the rail bottom beside the weld can be moved forward and backward along the detection interface by an appropriate distance, thereby obtaining a straight line detection path 3.

[0175] Path 3 is 40 mm long, with an angle of about 27.3° with the length direction of the rail. The distal end is about 127 mm from the weld edge and about 23.3 mm from the rail edge along the rail width direction. The proximal end is about 164 mm from the weld edge and about 4 mm from the rail edge along the rail width direction.

[0176] Path 3 is for detecting the lower transition position of one side of the weld, and can be symmetrical to the other three positions.

[0177] ② Detection path 4 of the bottom area of ​​the weld excess height side, such as Fig.16 shown.

[0178] For the bottom surface of the weld excess height side, a primary wave should be used to detect the weld position at the bottom of the rail. Because the position of the incident point is difficult to calculate, multiple points are selected on the boundary line of the bottom surface of the excess height side to deduce the incident point through the simulation propagation path. Considering that the ultrasonic incident point position and the wedge block direction are relatively free, the incident point is made to fall on a straight line as much as possible to obtain a straight line detection path 4.

[0179] The length of path 4 is about 17.2mm, and the angle with the length direction of the rail is about 22.2°. The far end point is about 32.1mm away from the weld edge, about 19.9mm away from the rail edge along the rail width direction, the limit detection position is 5.34mm away from the weld edge on the bottom surface of the excess height side, and the angle between the probe deflection direction and the length direction of the rail is about 42.8°; the near end point is about 16.2mm away from the weld edge, about 13.4mm away from the rail edge along the rail width direction, the limit detection position is 20mm away from the weld edge on the bottom surface of the excess height side, and the angle between the probe deflection direction and the length direction of the rail is about 25°. During the movement process, the movement path is short, and the probe can be considered to be uniformly deflected from 25° to 42.8° when moving from the near end to the far end.

[0180] Path 4 is the detection of half of the transition position of the bottom surface of the weld side, which can be symmetrical to the other three positions. From the farthest end data and combined with the weld structure, it can be found that due to the protruding part of the weld excess height at the bottom of the rail, the positions on both sides of the excess height are difficult to detect, and there is a monitoring blind spot.

[0181] In this embodiment, the rail can be divided into two sides along the axis according to the rail width direction (i.e., the direction perpendicular to the rail width) according to the I-shaped structure on the side of the rail, and then divided into two ends along the extension direction of the rail (i.e., along the length direction of the rail) according to the two ends of the rail connected by the weld. According to the above division method, the entire area to be inspected can be divided into four quadrants.

[0182] The full-section inspection requires a detection range covering all four quadrants, while the above-mentioned inspection paths except the first path only detect a single quadrant range of a certain rail side and a certain weld side, so the existing paths need to be symmetrically repeated and connected.

[0183] Therefore, the maintenance path also includes the second path, the third path and the fourth path, which are symmetrically copied to the other three quadrants except the quadrant itself and connected to generate a path.

[0184] This symmetrical arrangement ensures comprehensive inspection of the weld and its surrounding areas, increasing the likelihood of defect detection.

[0185] The specific symmetry is as follows:

[0186] ① Symmetry of the second path (at four locations, the probe direction is perpendicular to the path toward the weld):

[0187] Original position: one side of the rail waist.

[0188] Symmetrical position 1: the other side rail waist.

[0189] Symmetrical position 2: The rail waist at the other end of the weld on the same side.

[0190] Symmetrical position 3: The rail waist at the other end of the weld on the other side.

[0191] The symmetry of the second pass required inspection at four locations, with the original pass on one side of the rail web and the passes repeated at the other end of the weld and on the other side of the rail. This ensured that the rail web area was fully inspected on both sides and ends of the weld.

[0192] ② Symmetry of the third path (four positions, the probe direction is all toward the weld):

[0193] Original position: the transition area between the rail waist and the rail bottom beside the weld on one side.

[0194] Symmetrical position 1: the transition area between the rail waist and the rail bottom beside the weld on the other side.

[0195] Symmetrical position 2: The transition area between the rail waist and the rail bottom at the other end of the weld on the same side.

[0196] Symmetrical position 3: The transition area between the rail waist and the rail bottom at the other end of the weld on the other side.

[0197] The third path performs inspections at four locations, which are the transition areas between the rail waist and the rail bottom on both sides of the weld, covering both sides of the rail and both ends of the weld, thereby ensuring comprehensive inspection of the transition area between the rail waist and the rail bottom.

[0198] ③ Symmetry of the fourth path (at four locations, the probe direction is all toward the weld excess height):

[0199] Original position: bottom area of ​​weld excess side on one side.

[0200] Symmetrical position 1: bottom area of ​​the weld excess height on the other side.

[0201] Symmetrical position 2: The bottom area of ​​the reinforcement side at the other end of the weld on the same side.

[0202] Symmetrical position 3: The bottom area of ​​the reinforced height side at the other end of the weld on the other side.

[0203] The fourth path performs detection at four positions, covering all possible defect locations in the bottom area of ​​the weld excess height side to ensure comprehensiveness of the detection.

[0204] The third path and the fourth path need to be symmetrical to the other three positions.

[0205] In summary, the combination of the four detection paths can detect the locations of the main difficult defects of the thermite welding joint. The path distribution is as follows: Figure 8 As shown. The robotic arm is connected on both sides according to the planned detection path to repeatedly realize the full-section detection near the rail weld. During the detection process, the ultrasonic phased array is in full focus mode, and the detection mode is TT. The minimum value of the displayed distribution amplitude can be controlled according to the existing set gate, so the defect position including the crack during the movement can be intuitively seen. The center position of the defect is the depth and width distance near the rail weld relative to the current position of the probe, which guides the location and repair of the defect. The defect size can be easily measured on the full focus image by the -6dB method.

[0206] This embodiment provides a specific implementation method:

[0207] A simulated test was carried out using a rail weld test block with five vertical crack defects and two transverse through hole defects processed at the rail head, rail waist, rail bottom and weld excess height of the rail weld. The detection method of the present invention achieved a one-time detection of vertical crack defects with a radius of 4.5 mm and transverse through holes with a diameter of 2 mm. The detection rate was 100% after repeating the experiment 5 times.

[0208] In the field of rail weld detection, the application of ultrasonic phased array full focusing technology has significant advantages. This technology can achieve a comprehensive scan of weld areas in different positions without frequent replacement of probes, effectively solving the problem that traditional ultrasonic detection methods are difficult to reach in specific parts. More importantly, with the high sensitivity and accuracy of full focusing technology, it can accurately identify and locate tiny defects in welds that are difficult to detect. With the development of industrial technology, robotic arms have been widely used, which can free people from heavy labor and replace people working in dangerous environments, and have high positioning accuracy. The use of robotic arms and rail weld detection can effectively reduce the degree of dependence of detection on people and promote the development of intelligent and automated detection of rail welds.

[0209] S3, the host computer drives the robotic arm to clamp the ultrasonic phased array probe and scan along the inspection path;

[0210] S4. The ultrasonic phased array displays a real-time fully focused image based on the signal data collected by scanning and can record the detected defects.

[0211] During the scanning process along the inspection path, the ultrasonic phased array displays a real-time fully focused image based on the collected signal data and can record the detected defects.

[0212] The entire detection process of the detection method in this embodiment is controlled by the self-developed software of the host computer during the mobile detection process. The software is mainly divided into three interfaces: laser locator control interface, robotic arm control interface and phased array control interface. By using the winform function of C# and secondary development of the applied phased array equipment, laser scanner and robotic arm, a simple, intuitive and easy-to-use control software is produced.

[0213] The laser scanning interface includes functions such as connecting to a laser scanner, setting laser parameters, displaying laser scanning images, and processing data.

[0214] The robot control interface has functions such as connecting the robot, controlling the robot motion parameter motor, displaying the real-time motion status of the robot, and selecting the path.

[0215] The ultrasonic phased array control interface has the functions of connecting the phased array, setting scanning parameters and displaying scanning waveforms.

[0216] In the laser scanning module, the ZLDS200Connect function is used to connect the laser scanner, and the laser parameters are modified through the ZLDS200WriteParams function. Finally, the laser scanning data is obtained through the ZLDS200GetResult function. Through data processing, the center position of the weld is obtained. When the laser is positioned at the appropriate position, the trolley stops and the robotic arm starts scanning the rail weld.

[0217] Add the ABB.Robotices.Controllers library to the robot control module to obtain the control authority of the robot, use the scanner.Scan() function to scan the available robot, and connect the robot through the Controller.Logon function. Use controller.Rapid.GetRapidData() to read the current state of the robot, and use the Mastership.(controller.Rapid) function to modify the path of the robot. When the car stops at the expected position, the robot scans.

[0218] In the ultrasonic phased array control module, the host computer communicates with the ultrasonic board to realize the parameter modification and display of the ultrasonic phased array. The board is connected by using the UtilsParameter.Instance().Connect(OnConnectCallback) function, the parameters are set by using the HardwareBase.SetGlobalParams function, and finally the waveform data is received by using the ReceiveData(NetData netData) function.

[0219] In this embodiment, the time for full-section inspection of a weld is 1.5 minutes, the time for full-process (i.e., automatic guidance plus scanning) inspection is 3 minutes, and the average time for manual inspection is 30 minutes. Compared with traditional manual inspection, this method has obvious advantages in efficiency and avoids the interference of experience factors and personal status of manual inspection on the inspection results.

[0220] The ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects provided in this embodiment includes the following steps: S1, the flaw detection vehicle travels on the rail route, the laser scanner synchronously scans the rail, and the flaw detection vehicle stops when the laser scanner collects the residual height information of the rail weld; the flaw detection vehicle is equipped with a laser scanner and a mechanical arm; S2, the host computer calculates the deflection and offset of the current weld relative to the ideal weld position based on the three-dimensional coordinate data of the side of the rail weld collected by the laser scanner through the automatic guidance positioning algorithm, and corrects the preset scanning path point set to obtain a new scanning path point set to generate the maintenance path; S3, the host computer drives the mechanical arm to clamp the ultrasonic phased array probe and scan along the maintenance path; S4, the ultrasonic phased array displays a real-time fully focused imaging image based on the signal data collected by the scanning and can record the detected defects. The scanning method provided in this embodiment realizes the automatic scanning of the full-section position of the rail weld defect, and realizes the detection of the residual height area of ​​the rail bottom and rail jaw weld compared with the existing detection, overcoming the disadvantage that the existing technology cannot scan the full section.

[0221] In addition, this embodiment can identify welds and correct the robot arm scanning path through automatic guidance positioning technology, which solves the influence of positioning errors of the robot arm scanning program in automated applications, ensures the relative center offset and deflection between the flaw detection vehicle and the rail during operation, and ensures the accuracy and reproducibility of automated detection.

[0222] This embodiment uses automatic guided positioning technology to experiment with different rail inclination and rotation conditions, and can successfully correct the detection path. It also continuously repeats the detection during the experiment, and there is no situation where the detection position is too different from the preset automated detection process and is not detected due to the offset and deflection of the test block.

[0223] In addition, the automatic guided positioning technology provided in this embodiment takes 3 minutes for full-section inspection and full-process inspection, while the average time for manual inspection is 30 minutes. Compared with traditional manual inspection, it has obvious advantages in efficiency, and avoids the interference of experience factors and personal status of manual inspection on the inspection results, effectively improving the inspection efficiency and accuracy.

[0224] In addition, this embodiment provides a complete flaw detection vehicle inspection system that can operate autonomously on the track line. It only needs to use the system's water spraying device to spray water on the surface of the rail to be tested to form a water film to provide inspection conditions. This solves the problem that the inspection conditions of the prior art require immersion inspection and the inspection range is limited, thereby improving the convenience of inspection.

[0225] In addition, this embodiment uses phased array full focusing technology to detect and quantify vertical crack defects with a radius of 4.5 mm and transverse through holes with a diameter of 2 mm near the rail weld. Repeated tests show that all defects are detected, and the quantitative effect is 5.4% more accurate than the sector scanning data results, effectively improving the accuracy of detection.

[0226] The relative errors between the defect center detection amplitudes in the actual operation of this embodiment and the defect center detection amplitudes under the preset detection process are all within 8%, which effectively controls the experimental error.

[0227] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Processes Multiple processes and / or boxes Figure 1 A device that has the functions specified in one or more boxes.

[0228] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0229] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0230] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and it is intended that all changes that fall within the meaning and scope of equivalent elements are included in the present invention.

Claims

1. An ultrasonic phased array fully focused mechanical arm scanning method for seamless rail weld defects, characterized in that: The following steps are involved: S1. The flaw detection vehicle travels on the rail route, and the laser scanner synchronously scans the rail. When the laser scanner collects the residual height information of the rail weld, the flaw detection vehicle stops; the flaw detection vehicle is equipped with a laser scanner and a mechanical arm; S2. The host computer calculates the deflection and offset of the current weld relative to the ideal weld position based on the three-dimensional coordinate data of the rail weld side collected by the laser scanner through the automatic guidance positioning algorithm, and corrects the preset scanning path point set to obtain a new scanning path point set to generate the maintenance path; The automatic guidance positioning algorithm is specifically as follows: filtering and denoising the three-dimensional coordinate data of the rail weld collected by the laser scanner through a point cloud voxel gridding algorithm, and then obtaining a relative deflection deflection matrix and a relative offset offset matrix based on the denoised original point cloud through a point cloud matching algorithm, and transforming the coordinates of each point in the preset scanning path point set according to the above matrix to obtain a corrected new scanning path point set; The preset scanning paths include: the first path is used to scan the rail head, rail waist and rail bottom center area near the weld; the second path is used to scan the rail waist area near the weld; the third path is used to scan the transition area between the rail waist and the rail bottom beside the weld; the fourth path is used to scan the bottom surface area of ​​the weld excess height side; S3, the host computer drives the robotic arm to hold the ultrasonic phased array probe and scan along the inspection path; S4. The ultrasonic phased array displays a real-time fully focused image based on the signal data collected by scanning and can record the detected defects.

2. The robotic arm scanning method according to claim 1, characterized in that: The point cloud voxel gridding algorithm specifically includes the following steps: T1. Determine the maximum and minimum values ​​of the point cloud data in the X-axis, Y-axis and Z-axis directions; T2, based on the maximum and minimum values ​​in the X-axis, Y-axis and Z-axis directions and the preset voxel grid size, establish a voxel grid and assign an index to it; T3. For each voxel grid, determine whether the number of points in the grid is less than a preset screening threshold thV; when the number of points in the voxel grid is less than the screening threshold thV, discard all points in the voxel grid; T4. For each remaining voxel grid, the density value of the voxel grid is calculated according to the number of points in its surrounding neighboring voxel grids and its own grid; T5. Output voxel grid with density values.

3. The robotic arm scanning method according to claim 1, characterized in that: The point cloud matching algorithm specifically includes the following steps: D1, initialize the translation matrix T as the identity matrix; D2, initialize the rotation matrix R to the unit matrix; D3. Perform the following iterative process until the convergence condition is met or the maximum number of iterations is reached: a. Select sample points from the source point cloud source_cloud; b. Find the nearest corresponding point for each sample point in the target point cloud target_cloud; c. Calculate the optimal rigid body transformation, including translation and rotation, to minimize the distance between corresponding points in the source point cloud and the target point cloud; d. Apply the calculated translation matrix T and rotation matrix R to update the position of the source point cloud source_cloud; e. Calculate the transformation change of this iteration. If the change is less than the preset convergence threshold tolerance, the registration process is considered to have converged and the iteration is terminated. f. Increase the number of iterations iter. If iter reaches the preset maximum number of iterations max_iterations, the iteration is terminated; D4, output the final translation matrix T and rotation matrix R; D5. Calculate the corrected scanning path point set. According to the above output results, the corrected scanning path point set can be calculated using the following formula: P_correction = T*R*P_preset Among them, P_corrected is the corrected scanning path point set, and P_preset is the preset scanning path point set.

4. The robotic arm scanning method according to claim 1, characterized in that: The first path is a straight line that coincides with the center line of the rail top, and the probe direction is parallel to the length direction of the rail during detection.

5. The robotic arm scanning method according to claim 1, characterized in that: The second path is a straight line detection path along the waist of the rail and perpendicular to the bottom surface of the rail.

6. The robotic arm scanning method according to claim 1, characterized in that: The third path is on the rail bottom surface, with the starting point being the detection position of the defect in the transition area between the rail waist and the rail bottom beside the weld, and the probe is extended along the length of the rail at a preset angle, and the end point is a straight line where the wave points of the edge structure of the rail bottom are visible.

7. The robot arm scanning method according to claim 1, characterized in that: The fourth path is on the bottom surface of the rail, the initial point and the detection direction are the detection position and probe direction of the defect where the center of the weld excess edge is located, the end point is the straight line of the detection position and probe direction of the visible weld excess edge structural wave, and the probe direction changes evenly during the movement.

8. The robotic arm scanning method according to claim 1, characterized in that: The inspection path also includes a second path, a third path and a fourth path which are symmetrically copied to the other three quadrants except the quadrant itself and connected to generate a path; the quadrants are specifically divided as follows: according to the "I"-shaped structure on the side of the rail, the rail can be divided into two sides along the axis according to the width direction of the rail, and then divided into two ends along the extension direction of the rail according to the two ends of the rail connected by the weld, and the overall area to be inspected is divided into four quadrants.

9. The robot arm scanning method according to claim 1, characterized in that: Step S1 specifically includes: S11. Data acquisition: The laser scanner continuously scans the rail surface while the flaw detection vehicle is moving to obtain real-time 3D point cloud data; S12, feature extraction: processing the three-dimensional point cloud data in real time to extract contour features of the rail surface; S13, feature recognition: analyzing the contour features to identify features representing weld excess height; specifically including: S131, detecting sudden changes in the rail surface profile; S132, identifying abnormal shapes that do not conform to the standard rail profile; S133, analyzing density changes of point cloud data; S134, threshold determination: comparing the identified feature with a preset weld excess height feature threshold; S14, stop signal triggering: if the identified feature exceeds the preset threshold, the system determines that the weld excess height has been detected and triggers a stop signal; S15, the flaw detection vehicle stops: after receiving the stop signal, the control system in the upper computer immediately executes the braking procedure to make the flaw detection vehicle stop safely within the shortest distance; S16, position confirmation: After the flaw detection vehicle stops, the system performs another precise scan to confirm the weld position.

10. The robot arm scanning method according to claim 1, characterized in that: The flaw detection vehicle is also equipped with a water spraying device to spray water onto the surface of the steel rail to be tested to form a water film.

Images