Size detection method and system for welded pipeline

Abstract

The invention discloses a size detection method and system for a welded pipeline, and relates to the technical field of welded pipeline size detection, and the system comprises an annular track assembly, a detection execution unit, a multi-dimensional sensing module and a data processing terminal. By means of the annular track assembly cooperatively fixed through electromagnetic adsorption and vacuum-assisted adsorption and the visual and laser double-positioning welding seam tracking calibrator, core technologies such as time sequence alignment and dynamic reference correction of the multi-dimensional sensing module and the data processing terminal are combined, and stable adaptation and cleaning of pipelines with different pipe diameters and surface flatness are achieved; and the accurate detection reference is ensured, comprehensive and reliable data support is provided for quality evaluation of the welded pipeline, and the detection efficiency and safety are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of welded pipe size inspection technology, specifically to a method and system for inspecting the size of welded pipes. Background Technology

[0002] In industrial fields such as petrochemicals, municipal pipelines, and energy transmission, welded pipelines serve as the core carriers for fluid and media transport. Their welding quality directly affects the safety and stability of system operation. With the expansion of industrial production scale and the increase in the complexity of operating conditions, more stringent requirements have been placed on the dimensional accuracy and defect control of welded pipelines. The detection of key dimensional parameters such as weld reinforcement height, width, misalignment, and edge angle, as well as internal defects, has become a core link in ensuring the long-term reliable operation of pipelines.

[0003] The quality inspection of welded pipes needs to take into account both dimensional accuracy and defect identification. Especially in special application scenarios such as high-temperature operation, thick-walled pipe welding or high-pressure transportation, the complexity of the inspection environment further increases the difficulty of inspection. Traditional inspection methods mostly rely on manual visual inspection combined with handheld instrument measurement, which is not only inefficient, but also greatly affected by the experience of the operators. It is difficult to achieve comprehensive coverage and accurate measurement of the circumferential welds of the pipe, and cannot meet the high-efficiency quality inspection requirements of modern industrial production.

[0004] In existing technologies, automated inspection equipment generally lacks a precise coordination mechanism for multi-sensor data. The sampling timing of different sensors is misaligned, signals are easily interfered with in high-temperature environments, and the size calculation does not fully combine the actual wall thickness of the pipeline and the stress characteristics of the weld, resulting in insufficient measurement accuracy of key size parameters and difficulty in accurately supporting the comprehensive assessment of pipeline welding quality. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method and system for dimensional inspection of welded pipes, so as to solve the technical problem that existing inspection equipment is difficult to accurately support the comprehensive evaluation of pipe welding quality.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dimensional inspection system for welded pipes, comprising: The annular track assembly is fixed to the outer wall of the welded pipe through the cooperation of an electromagnetic adsorption module and a vacuum-assisted adsorption module. The inner side of the track is equipped with a gear ring drive structure, and the track surface is equipped with an adaptive cleaning module. The detection execution unit is slidably connected to the annular track assembly, with a built-in drive gear meshing with a gear ring transmission structure, and equipped with a weld seam tracking calibrator to achieve uniform speed movement along the circumference of the pipeline and always align with the center of the weld seam. The multi-dimensional sensing module is integrated at the bottom of the detection execution unit, including a blue laser profile sensor, a high-frequency eddy current sensor, an infrared temperature sensor, and an ultrasonic-assisted verification sensor. The four sensor detection ends are arranged sequentially along the pipe axis, at an angle of 30°-60° to the pipe surface. The data processing terminal connects to the detection execution unit via a wireless transmission module and has built-in dimensional analysis algorithms, defect identification models, and dynamic benchmark correction modules.

[0007] The annular track assembly also includes an adaptive adsorption force feedback unit, which consists of a miniature pressure sensor and a closed-loop controller. The miniature pressure sensor is evenly installed inside the elastic buffer pad to detect the contact pressure between the track and the pipe in real time. The closed-loop controller automatically adjusts the current of the electromagnetic adsorption module and the negative pressure of the vacuum-assisted adsorption module based on the pressure value, maintaining the contact pressure at 20-30 N / cm². 2 Furthermore, when the surface flatness deviation of the pipe exceeds 2mm, the adaptive cleaning module is triggered to extend the cleaning time by 1-2 times.

[0008] The weld seam tracking calibrator adopts a vision and laser dual positioning fusion structure: a miniature camera acquires weld seam images and identifies the weld seam edge contour, while a blue laser contour sensor synchronously emits an auxiliary positioning laser beam, which is focused on the center area of ​​the weld seam to form a light spot mark. A small displacement adjustment motor performs micron-level compensation within a lateral adjustment range of ±10mm based on the positional deviation between the contour edge and the laser spot. At the same time, combined with the circumferential movement speed of the detection execution unit, the compensation response speed is dynamically adjusted to ensure that the light spot always fits the center of the weld seam during the circumferential movement of the pipeline.

[0009] The data processing terminal also includes a multi-sensor data time-series alignment module. The time-series alignment module extracts the sampling timestamps of each sensor and, based on a wireless transmission delay compensation algorithm, corrects the time deviations of laser profile data, eddy current impedance data, ultrasonic verification data, and temperature data to within ±1ms. The dynamic benchmark correction module works in conjunction with the time-series alignment module to remove interference points only from the pipe surface data at the same time node, ensuring the spatiotemporal consistency of the benchmark fitting.

[0010] A method for dimensional inspection of welded pipes, characterized by comprising the following steps: S1. Assemble the ring track assembly and fit it to both sides of the pipe weld seam at a distance of 300-500mm. First, start the adaptive cleaning module to remove oil and rust from the pipe surface. Then, use electromagnetic and vacuum adsorption to fix it. The adsorption force adaptive feedback unit calibrates the fitting pressure in real time and starts the detection execution unit to perform initial position calibration. S2. Turn on the weld seam tracking calibrator, lock the weld seam center through vision and laser dual positioning, set the circumferential movement speed of the detection execution unit to 5-15mm / s and the sampling frequency to 100-500Hz, and simultaneously start all sensors; S3. Continuously collect laser profile data, eddy current impedance data, surface temperature data and ultrasonic verification data of the weld area, and transmit them to the data processing terminal in real time after being corrected by the timing alignment module. S4. The data processing terminal uses the dynamic benchmark correction module to eliminate interference points such as scratches and impurities on the pipe surface, fits an accurate benchmark line, and calculates the weld reinforcement height, width, misalignment and edge angle. S5. Combine multi-sensor data to identify defects such as porosity and slag inclusions inside the weld, and generate an inspection report that includes dimensional parameters, defect information classification, and ultrasonic verification results.

[0011] The calculation of the misalignment amount in step S4 adopts a piecewise fitting and weighted average method: First, divide the 100mm range on both sides of the weld into 5 segments along the pipe axis, each segment being 20mm. Then, use the dynamic benchmark correction module to remove abnormal data points in each segment. For each segment of valid data, fit a local baseline and calculate the local misalignment D1-D5 for each segment. Weights are assigned based on the distance from both ends of the pipe to the center of the weld. The section closer to the weld has a weight of 0.3, and the section farther from the weld has a weight of 0.1. The final misalignment D = 0.3D1 + 0.2D2 + 0.2D3 + 0.15D4 + 0.15D5, and the measurement accuracy is improved to ±0.01mm.

[0012] The edge angle calculation in step S4 incorporates adaptive correction for pipe wall thickness: First, the actual wall thickness t of the pipe is measured by the ultrasonic-assisted verification sensor. Then, the correction factor K = t / R is calculated in combination with the nominal radius R of the pipe. The value of K ranges from 0.05 to 0.2. The original edge angle E is corrected using the formula E'=E×(1+K), where K is taken as the upper limit when t>10% of the nominal wall thickness and as the lower limit when t<10% of the nominal wall thickness. The corrected edge angle calculation is more in line with the stress characteristics requirements of pipes with different wall thicknesses.

[0013] In step S3, when the infrared temperature sensor detects that the pipe surface temperature exceeds 800°C, in addition to triggering the small electric lift to raise the sensing module, the following adaptation actions are simultaneously initiated: The blue laser profile sensor reduces laser power by 30% and switches to a high-temperature dedicated filtering mode to avoid signal saturation caused by high-temperature radiation from the workpiece. The ultrasonic-assisted verification sensor was switched to a low-frequency detection mode, with the frequency reduced from 2MHz to 1MHz, to improve signal penetration stability under high-temperature conditions; The high-frequency eddy current sensor increases the excitation current by 15% to compensate for the impedance signal attenuation caused by high temperature.

[0014] In step S5, defect classification is incorporated into defect propagation trend prediction: By combining the three-dimensional contour data of the weld with the defect morphology data of the eddy current sensor, the aspect ratio, depth gradient and distribution location of the defects are analyzed through the defect identification model. If the length-to-width ratio of the defect is greater than 3:1 and it is close to the stress concentration area of ​​the pipeline, it is judged to have the risk of expansion. Based on the original classification, it is upgraded by one level, one level to two levels, and two levels to three levels. The defect classification results are synchronously linked to the pipeline usage scenarios. The higher the scenario level, the stricter the classification judgment threshold.

[0015] When the defect classification result shows a level 2 or higher defect, a re-inspection step is initiated: During the re-inspection, the ultrasonic-assisted verification sensor adopted a frequency sweep detection mode to collect material density distribution data in the defect area; Based on the material density benchmark value of the standard weld 3D model, the density deviation rate of the defect area is calculated. like If the defect rate is greater than 5%, it is determined to be a material porosity defect and will be marked in the inspection report. During the re-inspection and comparison, both the size deviation map and the density deviation map are output simultaneously. In summary, the present invention has the following main advantages: By using a ring track assembly that is fixed by electromagnetic adsorption and vacuum-assisted adsorption, and a weld seam tracking calibrator that is positioned by both vision and laser, combined with core technologies such as time alignment and dynamic benchmark correction of multi-dimensional sensing modules and data processing terminals, the present invention not only achieves stable adaptation and cleaning of pipes with different diameters and surface flatness, but also ensures accurate detection benchmarks, provides comprehensive and reliable data support for the quality assessment of welded pipes, and significantly improves detection efficiency and safety. Detailed Implementation

[0016] This embodiment takes the inspection of a carbon steel welded pipe with a nominal diameter of 500mm and a nominal wall thickness of 10mm as an example. This pipe is suitable for medium-pressure fluid transportation scenarios. The weld is a manual arc welding butt weld. It is necessary to inspect the reinforcement height, width, misalignment, edge angle and internal defects.

[0017] The dimensional inspection system for welded pipes includes a ring track assembly, an inspection execution unit, a multi-dimensional sensing module, and a data processing terminal.

[0018] The ring track assembly is fixed to the outer wall of the welded pipe through the cooperation of the electromagnetic adsorption module and the vacuum-assisted adsorption module. The inner side of the track is equipped with a toothed ring drive structure, and the track surface is equipped with an adaptive cleaning module.

[0019] The ring track assembly is composed of three arc-shaped track segments, each with a central angle of 120° and a length of approximately 523mm. It can accommodate pipes with a diameter of 500mm. The track is made of 6061 aluminum alloy with an anodized surface to enhance wear resistance. Each arc-shaped track has a flange connection structure at its end, secured with M8 stainless steel bolts. After assembly, the coaxiality error is ≤0.5mm, ensuring smooth movement of the detection execution unit.

[0020] The gear transmission structure inside the track is a spur gear ring with a module of 2mm and 160 teeth. The tooth surface hardness is quenched to HRC45-50, and the meshing clearance with the drive gear of the detection and execution unit is controlled at 0.1-0.2mm. The adaptive cleaning module on the track surface includes 6 cleaning units evenly distributed along the track circumference. Each cleaning unit consists of a miniature DC motor with a rated voltage of 12V and a speed of 3000rpm, a nylon brush with a bristle diameter of 0.1mm, and a high-pressure airflow nozzle with a working pressure of 0.4-0.6MPa. The contact depth between the brush and the pipe surface can be adaptively adjusted by an elastic adjustment mechanism, with an adjustment range of 0.5-1mm.

[0021] The electromagnetic adsorption module includes 12 evenly distributed electromagnets, each with 1500 coil turns and a core made of DT4 electrical pure iron. The rated operating current is 0.5-2A, and the adsorption force of a single electromagnet is ≥80N. The vacuum-assisted adsorption module includes 6 vacuum suction cups with a diameter of 50mm, evenly distributed on the inner side of the track. Negative pressure is provided by a micro vacuum pump, and the suction cups are made of nitrile rubber.

[0022] The adsorption force adaptive feedback unit consists of 6 miniature pressure sensors with a measurement range of 0-50 N / cm. 2 Accuracy ±0.1N / cm 2 Composed of a closed-loop controller with a sampling frequency of 100Hz, miniature pressure sensors are attached to the inside of the elastic buffer pad on the inner side of the track, with each sensor spaced 60° apart. These sensors collect the contact pressure values ​​between the track and the pipe in real time and transmit them to the closed-loop controller. The closed-loop controller has a built-in PID control algorithm that automatically adjusts the power supply current of the electromagnet and the pumping power of the vacuum pump based on the pressure feedback value, ensuring that the contact pressure is stably maintained at 20-30 N / cm². 2 When a pressure sensor detects that a pressure fluctuation causes a deviation in the flatness of the pipe surface to exceed 2mm, the closed-loop controller can calculate this by using the pressure difference between adjacent sensors. The closed-loop controller then sends a signal to the adaptive cleaning module to extend the initial 30-second cleaning time to 60 seconds, ensuring that the cleanliness of the pipe surface meets the detection requirements.

[0023] The detection execution unit is slidably connected to the annular track assembly, with a built-in drive gear meshing with the gear ring transmission structure, and equipped with a weld seam tracking calibrator to achieve uniform speed movement along the circumference of the pipeline and always align with the center of the weld seam.

[0024] The main body of the detection execution unit is an aluminum alloy shell. Two sets of sliding rollers are provided at the bottom of the shell, which cooperate with the guide rail of the ring track assembly. The execution unit has a built-in drive gear with a module of 2mm and 20 teeth. It meshes with the gear ring transmission structure. The drive gear is driven by a stepper motor and achieves micro-step control through micro-stepping technology to ensure the circumferential motion speed accuracy of ±0.1mm / s.

[0025] The weld seam tracking calibrator is integrated into the front end of the detection execution unit. It adopts a dual positioning fusion structure of vision and laser. The miniature camera acquires weld seam images through an 8mm fixed-focus lens with an infrared cutoff filter to avoid ambient light interference. The blue laser contour sensor, model KeyenceLJ-V7000, has a laser wavelength of 450nm, a measurement range of 10-30mm, and an accuracy of ±0.005mm. The emitted auxiliary positioning laser beam is focused on the center area of ​​the weld seam, forming a circular light spot mark with a diameter of 0.1mm. The small displacement adjustment motor is a piezoelectric ceramic motor, and its output shaft is connected to the lateral adjustment mechanism of the detection execution unit. Based on the deviation between the contour edge and the laser spot position obtained from image recognition, the weld seam edge coordinates are extracted through the OpenCV image algorithm, and the lateral distance between the weld seam edge and the center of the laser spot is calculated, and real-time micron-level compensation is performed. For example, when the deviation is 0.3mm, the motor drives the adjustment mechanism to move laterally by 0.3mm. At the same time, the compensation response time is dynamically adjusted according to the circumferential movement speed of the detection execution unit to ensure that the light spot always fits the weld center during the circumferential movement of the pipeline, with a deviation ≤0.01mm.

[0026] The multi-dimensional sensing module is integrated into the sensor mounting base at the bottom of the detection execution unit. The mounting base can be height-adjusted by a small electric lifter with a stroke of 0-50mm and an adjustment accuracy of 0.1mm. The four sensors are arranged sequentially along the pipe axis, from upstream to downstream of the weld: infrared temperature sensor, blue laser profile sensor, high-frequency eddy current sensor, and ultrasonic-assisted verification sensor. The detection end of each sensor is at a 45° angle to the pipe surface, within the range of 30°-60°, balancing detection range and measurement accuracy. The center-to-center spacing of the sensors is 30mm to avoid mutual interference.

[0027] The specific parameters of each sensor are as follows: Blue laser profile sensor: measurement range 10-30mm, sampling frequency 100-500Hz, output point cloud data resolution 0.001mm; High-frequency eddy current sensor: excitation frequency 1MHz, measurement range 0-5mm, resolution 0.001mm, used to detect surface and near-surface defects in welds; Infrared temperature sensor: Measurement range 0-1200℃, accuracy ±1%FS, response time ≤10ms, non-contact measurement; Ultrasonic-assisted verification sensor: The probe type is a straight probe with a nominal frequency of 2MHz, which can be switched to a 1MHz low-frequency mode. The detection depth is 0-50mm and the resolution is 0.01mm. It is used to measure the pipe wall thickness and verify internal defects.

[0028] The sensor mounting base has a built-in copper shield to reduce electromagnetic interference. The signal cables of each sensor are shielded cables and are connected to the signal processing module of the detection and execution unit.

[0029] The data processing terminal uses an industrial tablet PC with a built-in Windows 10 IoT operating system. It establishes communication with the detection and execution unit through a LoRa wireless transmission module (communication distance ≥100m, transmission rate ≥1Mbps, latency ≤50ms).

[0030] The core modules built into the data processing terminal are implemented as follows: Multi-sensor data timing alignment module: By extracting the timestamps from the data frames of each sensor, and based on the wireless transmission delay compensation algorithm, the module performs time calibration on the laser profile data, eddy current impedance data, ultrasonic verification data, and temperature data, correcting the time deviation to within ±0.5ms. Dimensional analysis algorithm: integrates sub-algorithms for calculating excess height, width, misalignment, and edge angle; Defect recognition model: Based on a CNN deep learning model, the training dataset contains 10,000+ sets of multi-sensor fusion data of different types of defects, such as porosity, inclusions, and cracks. The model has an accuracy of ≥95% and can output information such as defect type, size, and location. Dynamic baseline correction module: In conjunction with the time alignment module, it processes only the pipe surface data at the same time point, uses the 3σ criterion to remove interference points, such as abnormal data caused by scratches and impurities, and then fits an accurate baseline using the least squares method to ensure the spatiotemporal consistency of the baseline fitting, with a fitting error ≤0.01mm.

[0031] The data processing terminal is also equipped with a printer interface, which can print test reports in real time and support data storage for easy traceability later.

[0032] The dimension inspection method for welded pipes in this embodiment is implemented through the following steps: S1. Track Installation and Preparation First, the three arc-shaped track segments are spliced ​​into a complete ring and fitted to both sides of the pipe weld seam at a distance of 300-500mm. In this embodiment, the distance is selected at 400mm. The track is then tightened with flange bolts to ensure that the coaxiality of the track is ≤0.5mm. The adaptive cleaning module is then activated. A micro DC motor drives the nylon brush to rotate, while a high-pressure airflow nozzle sprays dry compressed air to remove oil, rust, and dust from the pipe surface. The initial cleaning time is 30 seconds.

[0033] After cleaning, the electromagnetic adsorption module and the vacuum-assisted adsorption module are activated simultaneously: an initial current of 1A is applied to the electromagnet, generating an adsorption force that initially brings the track into contact with the pipe; the vacuum pump is activated to create a negative pressure of -0.08MPa inside the vacuum suction cup; the miniature pressure sensor in the adsorption force adaptive feedback unit collects the contact pressure value in real time; if the pressure in a certain area is lower than 20N / cm², the system will detect the contact pressure. 2 The closed-loop controller increases the electromagnet current and improves the vacuum pump's pumping power; if the pressure is higher than 30 N / cm², the closed-loop controller will increase the current to the electromagnet and improve the pumping power of the vacuum pump. 2 This reduces the current and pumping power, ultimately stabilizing the bonding pressure at 25 N / cm throughout the circumference. 2 During this period, if the pressure sensor detects a deviation of more than 2mm in the flatness of the pipe surface, such as a local bulge causing a sudden increase in pressure, the adaptive cleaning module will automatically extend the cleaning time to 60 seconds to remove impurities a second time.

[0034] After adsorption and fixation are completed, the detection execution unit is started and driven to move circumferentially along the track for one revolution at a speed of 10 mm / s. The weld position is identified by the visual positioning function of the weld tracking calibrator, and the initial position calibration is completed. The execution unit stops at the beginning of the weld.

[0035] S2. Parameter Setting and Startup Activate the weld seam tracking calibrator: A miniature camera captures weld seam images and extracts the weld seam edge contour through an edge detection algorithm; a blue laser contour sensor emits a laser beam, which is focused on the center of the weld seam to form a light spot mark; a closed-loop controller calculates the positional deviation between the contour edge and the laser spot, and adjusts the lateral position of the detection execution unit through a piezoelectric ceramic motor until the light spot coincides with the center of the weld seam, locking the positioning state.

[0036] Set the detection parameters: the circumferential movement speed of the detection execution unit is 10 mm / s, within the range of 5-15 mm / s, and the sampling frequency is 300 Hz, within the range of 100-500 Hz; all sensors are turned on simultaneously, the infrared temperature sensor monitors the pipe surface temperature in real time, the blue laser profile sensor collects profile data, the high-frequency eddy current sensor detects impedance changes, and the ultrasonic-assisted verification sensor measures the wall thickness and internal signals.

[0037] S3. Data Acquisition and Transmission The detection execution unit moves at a constant speed along the circular track, and each sensor continuously collects data at a set frequency: the blue laser contour sensor outputs a set of point cloud data every millisecond, the high-frequency eddy current sensor synchronously collects impedance signals, the infrared temperature sensor provides real-time feedback of temperature values, and the ultrasonic-assisted verification sensor outputs wall thickness and reflection signals every 2 milliseconds.

[0038] The collected data is preprocessed by the signal processing module of the detection execution unit, i.e., filtered and amplified, and then sent to the data processing terminal through the LoRa wireless transmission module. The timing alignment module of the data processing terminal extracts the timestamps of the data from each sensor. Based on the wireless transmission delay compensation algorithm, in this embodiment the transmission distance is about 5m and the delay compensation value is 12ms. The data from different sensors are calibrated to the same time axis, and the time deviation is corrected to within ±0.5ms to ensure the spatiotemporal consistency of the data.

[0039] S4. Dimensional parameter calculation The dynamic benchmark correction module of the data processing terminal processes the calibrated dataset: it uses the 3σ criterion to remove abnormal data points caused by scratches and impurities on the pipe surface, with a removal ratio of ≤3%. Then, it fits the benchmark line of the pipe surface using the least squares method, and calculates the following dimensional parameters based on the benchmark line: Weld reinforcement height: The vertical distance between the peak point in the laser contour data and the baseline is the reinforcement height. In this embodiment, the measurement accuracy is ±0.01mm. Weld width: The lateral distance between the two edges of the weld in the laser profile data is measured, and the average value of 5 consecutive sampling points is taken as the final width, with an accuracy of ±0.01mm; Misalignment: Calculated using piecewise fitting and weighted average method. a. Divide the 100mm range on both sides of the weld into 5 sections along the pipe axis, each section being 20mm long: Section 1: 0-20mm on the weld side, Section 2: 20-40mm, Section 3: 40-60mm, Section 4: 60-80mm, Section 5: 80-100mm; b. Remove outlier data points within each segment, fit the local baseline using the least squares method for the valid data, and calculate the local misalignment amounts D1-D5 for each segment. In this embodiment, the measured values ​​are D1=0.05mm, D2=0.04mm, D3=0.03mm, D4=0.02mm, and D5=0.01mm. c. Calculate the final misalignment amount according to the weight: D=0.3×0.05+0.2×0.04+0.2×0.03+0.15×0.02+0.15×0.01=0.034mm, with a measurement accuracy of ±0.01mm; Edge angle: Adaptive correction for pipe wall thickness is added. a. The actual pipe wall thickness t = 10.8 mm was verified by ultrasonic-assisted sensor measurement, while the nominal wall thickness was 10 mm, with a deviation of 8%. b. The nominal radius of the pipe is R = 250 mm. The correction factor is calculated as K = t / R = 10.8 / 250 = 0.0432. c. Correct the original edge angle according to the formula E'=E×(1+K). If the original E=0.02mm, then the corrected E'=0.02×(1+0.0432)=0.0209mm, so that the calculation result is more consistent with the stress characteristics of the pipeline.

[0040] S5. Defect Identification and Report Generation The defect identification model of the data processing terminal combines multi-sensor data for analysis: abnormal impedance signals from high-frequency eddy current sensors indicate surface or near-surface defects in the weld; abnormal reflection signals from ultrasonic-assisted verification sensors indicate internal defects; and laser contour data assists in determining the defect morphology. The model analyzes the aspect ratio, depth gradient, and distribution location of defects: if a defect has an aspect ratio of 4:1 and is located at a pipe bend, it is determined to have a risk of expansion, and the original level 2 defect is upgraded to level 3; since the pipeline in this embodiment is a medium-pressure pipeline, the level 3 defect threshold is set to 0.5 mm according to the standard; if it is a high-pressure pipeline, it is lowered by 20% to 0.4 mm.

[0041] During re-inspection, the ultrasonic-assisted verification sensor is switched to frequency sweep detection mode (frequency continuously adjustable from 1-3MHz) to collect material density distribution data of the defect area and the material density benchmark value of the standard weld. =7.85g / cm 3 If the defect area is measured =7.4g / cm 3 Calculate density deviation rate =|7.4-7.85| / 7.85≈5.7%>5%, which is determined to be a material porosity defect and is simultaneously marked in the inspection report.

[0042] The final test report includes: basic pipe information, such as diameter, wall thickness, material, dimensional parameters such as excess height, width, misalignment, and edge angle, and defect information such as type, size, location, classification, ultrasonic verification results, and material density deviation chart. The report supports PDF format storage and printing.

[0043] When the infrared temperature sensor detects that the pipe surface temperature exceeds 800°C, such as in a scenario where detection occurs immediately after welding, the data processing terminal immediately triggers the following adaptation actions: The small electric lifter is used to raise the sensing module by 10mm, increasing the distance between the sensor and the pipe surface to avoid damage from high temperature. The blue laser profile sensor reduces laser power by 30%, from 20mW to 14mW, and switches to a high-temperature dedicated filtering mode with a filtering wavelength of 450±10nm to suppress signal saturation caused by high-temperature radiation from the workpiece. The ultrasonic-assisted verification sensor was switched to low-frequency detection mode, with the frequency reduced from 2MHz to 1MHz, which improved the signal penetration stability under high-temperature conditions. High temperatures can cause changes in the sound velocity of the medium, and low-frequency signals are less affected. The high-frequency eddy current sensor increases the excitation current by 15%, from 1A to 1.15A, to compensate for the impedance signal attenuation caused by high temperature and ensure the sensitivity of defect detection.

[0044] Through the above adaptation strategy, the system can operate stably in a temperature range of 0-1200℃ without significantly affecting the measurement accuracy.

[0045] Although embodiments of the present invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions, and variations to the embodiments as needed without departing from the principles and spirit of the invention, but such modifications, substitutions, and variations are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A dimensional inspection system for welded pipes, characterized in that, include: The annular track assembly is fixed to the outer wall of the welded pipe through the cooperation of an electromagnetic adsorption module and a vacuum-assisted adsorption module. The inner side of the track is equipped with a gear ring drive structure, and the track surface is equipped with an adaptive cleaning module. The detection execution unit is slidably connected to the annular track assembly, with a built-in drive gear meshing with a gear ring transmission structure, and equipped with a weld seam tracking calibrator to achieve uniform speed movement along the circumference of the pipeline and always align with the center of the weld seam. The multi-dimensional sensing module is integrated at the bottom of the detection execution unit, including a blue laser profile sensor, a high-frequency eddy current sensor, an infrared temperature sensor, and an ultrasonic-assisted verification sensor. The four sensor detection ends are arranged sequentially along the pipe axis, at an angle of 30°-60° to the pipe surface. The data processing terminal connects to the detection execution unit via a wireless transmission module and has built-in dimensional analysis algorithms, defect identification models, and dynamic benchmark correction modules.

2. The dimensional inspection system for welded pipes according to claim 1, characterized in that: The annular track assembly also includes an adaptive adsorption force feedback unit, which consists of a miniature pressure sensor and a closed-loop controller. The miniature pressure sensor is evenly installed inside the elastic buffer pad to detect the contact pressure between the track and the pipe in real time. The closed-loop controller automatically adjusts the current of the electromagnetic adsorption module and the negative pressure of the vacuum-assisted adsorption module based on the pressure value, maintaining the contact pressure at 20-30 N / cm². 2 Furthermore, when the surface flatness deviation of the pipe exceeds 2mm, the adaptive cleaning module is triggered to extend the cleaning time by 1-2 times.

3. The dimensional inspection system for welded pipes according to claim 1, characterized in that: The weld seam tracking calibrator adopts a vision and laser dual positioning fusion structure: a miniature camera acquires weld seam images and identifies the weld seam edge contour, while a blue laser contour sensor synchronously emits an auxiliary positioning laser beam, which is focused on the center area of ​​the weld seam to form a light spot mark. A small displacement adjustment motor performs micron-level compensation within a lateral adjustment range of ±10mm based on the positional deviation between the contour edge and the laser spot. At the same time, combined with the circumferential movement speed of the detection execution unit, the compensation response speed is dynamically adjusted to ensure that the light spot always fits the center of the weld seam during the circumferential movement of the pipeline.

4. The method and system for dimensional inspection of welded pipes according to claim 1, characterized in that: The data processing terminal also includes a multi-sensor data time-series alignment module. The time-series alignment module extracts the sampling timestamps of each sensor and, based on a wireless transmission delay compensation algorithm, corrects the time deviations of laser profile data, eddy current impedance data, ultrasonic verification data, and temperature data to within ±1ms. The dynamic benchmark correction module works in conjunction with the time-series alignment module to remove interference points only from the pipe surface data at the same time node, ensuring the spatiotemporal consistency of the benchmark fitting.

5. A method for dimensional inspection of welded pipes, the dimensional inspection system for welded pipes as described in any one of claims 1-4, characterized in that, Includes the following steps: S1. Assemble the ring track assembly and fit it to both sides of the pipe weld seam at a distance of 300-500mm. First, start the adaptive cleaning module to remove oil and rust from the pipe surface. Then, use electromagnetic and vacuum adsorption to fix it. The adsorption force adaptive feedback unit calibrates the fitting pressure in real time and starts the detection execution unit to perform initial position calibration. S2. Turn on the weld seam tracking calibrator, lock the weld seam center through vision and laser dual positioning, set the circumferential movement speed of the detection execution unit to 5-15mm / s and the sampling frequency to 100-500Hz, and simultaneously start all sensors; S3. Continuously collect laser profile data, eddy current impedance data, surface temperature data and ultrasonic verification data of the weld area, and transmit them to the data processing terminal in real time after being corrected by the timing alignment module. S4. The data processing terminal uses the dynamic benchmark correction module to eliminate interference points such as scratches and impurities on the pipe surface, fits an accurate benchmark line, and calculates the weld reinforcement height, width, misalignment and edge angle. S5. Combine multi-sensor data to identify defects such as porosity and slag inclusions inside the weld, and generate an inspection report that includes dimensional parameters, defect information classification, and ultrasonic verification results.

6. The method for dimensional inspection of welded pipes according to claim 5, characterized in that: The calculation of the misalignment amount in step S4 adopts a piecewise fitting and weighted average method: First, divide the 100mm range on both sides of the weld into 5 segments along the pipe axis, each segment being 20mm. Then, use the dynamic benchmark correction module to remove abnormal data points in each segment. For each segment of valid data, fit a local baseline and calculate the local misalignment D1-D5 for each segment. Weights are assigned based on the distance from both ends of the pipe to the center of the weld. The section closer to the weld has a weight of 0.3, and the section farther from the weld has a weight of 0.

1. The final misalignment D = 0.3D1 + 0.2D2 + 0.2D3 + 0.15D4 + 0.15D5, and the measurement accuracy is improved to ±0.01mm.

7. The method for dimensional inspection of welded pipes according to claim 5, characterized in that: The edge angle calculation in step S4 incorporates adaptive correction for pipe wall thickness: First, the actual wall thickness t of the pipe is measured by the ultrasonic-assisted verification sensor. Then, the correction factor K = t / R is calculated in combination with the nominal radius R of the pipe. The value of K ranges from 0.05 to 0.

2. The original edge angle E is corrected using the formula E'=E×(1+K), where K is taken as the upper limit when t>10% of the nominal wall thickness and as the lower limit when t<10% of the nominal wall thickness. The corrected edge angle calculation is more in line with the stress characteristics requirements of pipes with different wall thicknesses.

8. A method for dimensional inspection of welded pipes according to claim 5, characterized in that: In step S3, when the infrared temperature sensor detects that the pipe surface temperature exceeds 800°C, in addition to triggering the small electric lift to raise the sensing module, the following adaptation actions are simultaneously initiated: The blue laser profile sensor reduces laser power by 30% and switches to a high-temperature dedicated filtering mode to avoid signal saturation caused by high-temperature radiation from the workpiece. The ultrasonic-assisted verification sensor was switched to a low-frequency detection mode, with the frequency reduced from 2MHz to 1MHz, to improve signal penetration stability under high-temperature conditions; The high-frequency eddy current sensor increases the excitation current by 15% to compensate for the impedance signal attenuation caused by high temperature.

9. A method for dimensional inspection of welded pipes according to claim 5, characterized in that: In step S5, defect classification is incorporated into defect propagation trend prediction: By combining the three-dimensional contour data of the weld with the defect morphology data of the eddy current sensor, the aspect ratio, depth gradient and distribution location of the defects are analyzed through the defect identification model. If the length-to-width ratio of the defect is greater than 3:1 and it is close to the stress concentration area of ​​the pipeline, it is judged to have the risk of expansion. Based on the original classification, it is upgraded by one level, one level to two levels, and two levels to three levels. The defect classification results are synchronously linked to the pipeline usage scenarios. The higher the scenario level, the stricter the classification judgment threshold.

10. A method for dimensional inspection of welded pipes according to claim 9, characterized in that: When the defect classification result shows a level 2 or higher defect, a re-inspection step is initiated: During the re-inspection, the ultrasonic-assisted verification sensor adopted a frequency sweep detection mode to collect material density distribution data in the defect area; Based on the material density benchmark value of the standard weld 3D model, the density deviation rate of the defect area is calculated. like If the defect rate is greater than 5%, it is determined to be a material porosity defect and will be marked in the inspection report. During the re-inspection and comparison, both the size deviation map and the density deviation map are output simultaneously.

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