Eddy current automatic detection and calibration method and system, medium, product and computer equipment

Abstract

The invention belongs to the technical field of eddy current detection. The eddy current automatic detection and calibration method comprises the steps that a pose deviation matrix is calculated, a mechanical arm carrying an eddy current probe is controlled to conduct pose adjustment according to the pose deviation matrix, and the current pose of the eddy current probe is obtained; under the current pose, an inductive impedance signal caused by eddy current change of the workpiece to be detected is obtained, and associated data of the current pose and the inductive impedance signal are obtained; and performing defect positioning on the workpiece entity to be detected according to the associated data, calculating the signal-to-noise ratio of the workpiece entity to be detected in combination with a defect positioning result, performing iterative optimization on the signal-to-noise ratio within a preset time, and determining the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximum. According to the method, personal errors caused by traditional manual calibration are eliminated, the multi-attitude detection requirement of the complex curved surface workpiece can be met, and the accuracy of defect quantitative analysis is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of eddy current testing technology, and specifically to an automatic eddy current testing and calibration method, system, medium, product, and computer equipment. Background Technology

[0002] Eddy current testing technology currently struggles to achieve precise quantitative analysis of defects, primarily due to the ambiguity of signals caused by multi-factor coupling interference. Eddy current signals are influenced by multiple factors, including material conductivity, magnetic permeability, defect geometry (such as depth, length, and shape), detection coil lift-off distance, and edge effects. These factors couple with each other in the electromagnetic field, causing the impedance signal of a single defect to correspond to multiple different combinations of actual defect parameters, resulting in a ambiguity problem. For example, changes in conductivity may mask the signal of a tiny crack or superimpose with the lift-off effect to produce a similar response.

[0003] Currently, in order to achieve quantitative analysis of eddy current testing, calibration blocks are typically used. By pre-setting artificial defects (grooves, holes, etc.), they can provide a standardized reference for the testing system, ensuring the comparability of test results from different devices or operators under the same conditions. By calibrating probe sensitivity, lift-off compensation, and phase adjustment using the calibration blocks, human error is reduced, and the stability of the testing process is improved.

[0004] However, artificial defects in calibration blocks are mostly regular in shape, which differs significantly from the signal characteristics of actual complex and rough defects. The defect size of calibration blocks is limited, making it impossible to verify the detection capability of micron-level or ultra-large-scale defects, and the signal superposition effect of dense defect groups is ignored. Calibration blocks cannot simulate the material property degradation of the workpiece under test due to the service environment (such as high-temperature oxidation, irradiation damage, and residual stress), which makes the calibration parameters prone to failure. In addition, manual calibration is subject to human error and is difficult to adapt to the multi-pose detection requirements of complex curved workpieces. Summary of the Invention

[0005] To address the issues of low accuracy and reliability in existing eddy current testing methods, the present invention aims to provide an automatic eddy current testing and calibration method, system, medium, product, and computer equipment. By calculating the pose deviation matrix, it eliminates the human error inherent in traditional manual calibration, adapts to the multi-pose testing requirements of complex curved workpieces, and significantly improves the accuracy of quantitative defect analysis.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the present invention provides an automatic eddy current detection and calibration method.

[0007] An automatic eddy current detection and calibration method includes the following steps: Based on the pre-constructed workpiece model of the workpiece entity, the measured point cloud data of the surface of the workpiece entity is obtained, the measured point cloud data and the workpiece model are matched, the pose deviation matrix is ​​calculated, and the pose adjustment of the robotic arm carrying the eddy current probe is controlled according to the pose deviation matrix to obtain the current pose of the eddy current probe. Under the current pose, acquire the induced impedance signal caused by the eddy current change of the workpiece under test, and obtain the correlation data between the current pose and the induced impedance signal; Based on the associated data, the defects of the workpiece under test are located. The signal-to-noise ratio of the workpiece under test is calculated based on the defect location results. The signal-to-noise ratio is iteratively optimized within a preset time to determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

[0008] In one implementation of the first aspect of the present invention, solving the pose deviation matrix includes: A pre-constructed workpiece model of the workpiece entity to be tested is constructed, and the surface feature point cloud of the workpiece model is extracted. The surface feature point cloud contains theoretical three-dimensional coordinate information. Using a robotic arm carrying a structured light 3D scanner, coded stripes are projected onto the surface of the workpiece, and reflected light signals are collected to generate measured point cloud data. The measured point cloud data contains the real three-dimensional coordinate information of the surface of the workpiece. The surface feature point cloud and the measured point cloud data are iteratively matched to calculate the pose deviation matrix.

[0009] As a further limitation of the first aspect of the present invention, the surface feature point cloud and the measured point cloud data are iteratively matched to calculate the pose deviation matrix, including: Set an initial pose transformation matrix, transform each point in the measured point cloud data to the coordinates of the surface feature point cloud through the initial pose transformation matrix to obtain the first transformation point. Find the surface feature point with the closest Euclidean distance for each first transformation point in the surface feature point cloud and establish a point pair between the first transformation point and the surface feature point. Based on the established point pairs, the error function is solved using the least squares method to obtain the pose transformation matrix when the error function is minimized; The solved pose transformation matrix is ​​used as the initial pose transformation matrix for the next iteration, and the process of establishing point pairs is repeated. When the error change between two adjacent iterations is less than a preset threshold or the number of iterations reaches the upper limit, the iteration stops, and the pose deviation matrix is ​​obtained.

[0010] In one implementation of the first aspect of the present invention, the contact pressure between the eddy current probe and the workpiece to be tested is monitored in real time, and the contact pressure is compared with a preset pressure threshold. If the contact pressure is less than or equal to the preset pressure threshold, the detection action is performed normally according to the preset trajectory. If the contact pressure is greater than the preset pressure threshold, a protection mechanism is triggered, and the robotic arm is controlled to carry the eddy current probe to perform reverse displacement compensation of a set distance.

[0011] In one implementation of the first aspect of the present invention, a signal-to-noise ratio objective function is constructed with the probe tilt angle, lift-off distance and excitation frequency as decision variables, and the optimal detection pose of the eddy current probe is determined with the goal of maximizing the signal-to-noise ratio objective function.

[0012] As a further limitation of the first aspect of the present invention, the signal-to-noise ratio objective function, which calculates the signal-to-noise ratio of the workpiece entity under test in conjunction with the defect location results, includes: ,in, It refers to the effective value of the defect signal within the defect response range. The mean square of the noise signal within the defect-free interval is given, where the defect response interval and the defect-free interval are determined based on the induced impedance signal.

[0013] Secondly, the present invention provides an automatic eddy current detection and calibration system.

[0014] An automatic eddy current detection and calibration system includes: The pose deviation calculation unit is configured to: pre-build a workpiece model based on the workpiece entity to be measured, obtain the measured point cloud data of the surface of the workpiece entity to be measured, match the measured point cloud data and the workpiece model, calculate the pose deviation matrix, and control the robotic arm carrying the eddy current probe to adjust the pose according to the pose deviation matrix to obtain the current pose of the eddy current probe. The correlation data generation unit is configured to: acquire the induced impedance signal caused by the eddy current change of the workpiece under test in the current pose, and obtain the correlation data between the current pose and the induced impedance signal. The optimal detection pose generation unit is configured to: locate defects in the workpiece entity based on associated data, calculate the signal-to-noise ratio of the workpiece entity based on the defect location results, iteratively optimize the signal-to-noise ratio within a preset time, and determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is at its maximum.

[0015] Thirdly, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the eddy current automatic detection and calibration method of the first aspect of the present invention.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute the eddy current automatic detection and calibration method of the first aspect of the present invention.

[0017] Fifthly, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the eddy current automatic detection and calibration method of the first aspect of the present invention.

[0018] The beneficial effects of the present invention are as follows: This invention innovatively proposes an automatic eddy current detection and calibration method. By constructing a model based on the physical entity of the workpiece under test and calculating the pose deviation matrix by matching the measured point cloud data, the method drives a robotic arm to precisely adjust the pose of the eddy current probe. This eliminates the dependence on calibration blocks. Compared with regular artificial defects in the test blocks, it directly acquires the induced impedance signal of the physical entity of the workpiece under test and establishes the correlation data between pose and signal. It can truly restore the signal characteristics of actual complex and rough defects and eliminate the detection deviation caused by signal differences.

[0019] This invention is not limited by the defect size of the test block. Through defect localization and iterative optimization of signal-to-noise ratio, it can accurately capture the signal of micron-sized defects and cover a very large range of defect detection. In addition, the signal superposition effect of dense defect groups can be clearly identified during the iteration process, avoiding misjudgment caused by signal interference.

[0020] This invention uses the physical workpiece under test after it has been in service as the calibration benchmark. It can naturally incorporate performance degradation factors such as high-temperature oxidation, irradiation damage, and residual stress of the material, ensuring that the calibration parameters are highly matched with the actual state of the workpiece and preventing parameter failure. In addition, the automatic alignment of the robotic arm replaces manual calibration, which not only avoids human operation errors, but also perfectly adapts to the testing needs of complex curved workpieces through multi-posture adjustment, greatly improving the accuracy, applicability and reliability of testing and calibration.

[0021] This invention eliminates the inherent differences between the calibration test block and the actual workpiece by solving the pose deviation matrix and directly using the real workpiece as the reference. By accurately mapping the surface morphology of the workpiece, the pose adjustment of the robotic arm is based on the real geometric features, avoiding the signal reference deviation caused by the regularity of artificial defects in the test block. This provides a pose reference consistent with the actual state of the workpiece for subsequent detection and solves the problem of signal feature mismatch of traditional test blocks.

[0022] This invention controls a robotic arm to adjust the eddy current probe's pose according to a pose deviation matrix, replacing manual calibration to achieve automated alignment. This eliminates positioning errors caused by human operation and allows the robotic arm's multi-degree-of-freedom motion to adapt to different detection postures of complex curved workpieces, ensuring that the probe is always close to the workpiece surface at the optimal angle. This solves the limitation of manual calibration being unable to adapt to complex workpiece shapes.

[0023] This invention obtains correlation data between the current pose and the induced impedance signal, and can directly bind parameters such as probe position and angle to the signal characteristics of actual defects. Compared with the fixed signal mode of calibration test blocks, it can truly record the signal response of complex and rough defects under different poses, completely preserve the original characteristics of the defects, and solve the problem of significant differences between artificially calibrated defects and actual defects.

[0024] This invention combines the defect location results with an iterative optimization process of signal-to-noise ratio. By continuously adjusting the pose to find the strongest signal state, it can capture the weak signals of micron-sized defects and cover the signal characteristics of ultra-large-scale defects. At the same time, it can clearly identify the signal superposition rules of dense defect groups during iteration, thus solving the problems of limited defect size in calibration test blocks and the neglect of signal superposition effects. Attached Figure Description

[0025] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0026] Figure 1 A flowchart illustrating an exemplary embodiment of the eddy current automatic detection and calibration method provided by the present invention; Figure 2 A schematic diagram of an eddy current automatic detection and calibration system provided as an exemplary embodiment of the present invention; Figure 3 A schematic diagram of a computer device provided for an exemplary embodiment of the present invention. Detailed Implementation

[0027] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0028] This implementation proposes an automatic eddy current detection and calibration method. By suppressing jitter through a closed-loop feedback system of a multi-joint robotic arm and a harmonic reducer, and combining structured light 3D scanning with ICP registration algorithm, high-precision repeatable positioning is achieved, eliminating the human error of traditional manual calibration. By utilizing structured light automatic correction and pose deviation compensation, it can adapt to the multi-pose detection requirements of complex curved surface workpieces. Through DQN real-time control, it can cope with dynamic interferences such as temperature drift and mechanical vibration, significantly improving the accuracy of defect quantitative analysis.

[0029] More specifically, such as Figure 1 As shown, the process includes the following: S101: Based on the pre-constructed workpiece model of the workpiece entity, project coded stripes onto the surface of the workpiece entity to generate measured point cloud data, match the measured point cloud data and the workpiece model, and solve the pose deviation matrix.

[0030] More specifically, this includes: pre-constructing a three-dimensional digital model of the workpiece entity, extracting a set of surface feature point clouds from the three-dimensional digital model, the surface feature point clouds containing theoretical three-dimensional coordinate information; using a six-degree-of-freedom multi-joint robotic arm, equipped with a harmonic reducer and carrying a structured light 3D scanner to project coded stripes onto the surface of the workpiece to be measured, collecting reflected light signals, and generating measured point cloud data, the measured point cloud data containing the three-dimensional coordinates of the surface of the workpiece entity to be measured.

[0031] In this implementation, a six-degree-of-freedom multi-joint robotic arm carrying a high-precision eddy current detector (with an eddy current probe) repeatedly contacts the standard detection point. The readings of the eddy current detector are statistically analyzed to determine the repeatability of the robotic arm's positioning accuracy. The repeatability is compared with a preset standard threshold to achieve real-time monitoring and deviation compensation of the detection action. A structured light 3D scanner is used to project coded stripes onto the surface of the workpiece to generate millions of measured point cloud data. The surface feature point cloud and the measured point cloud data are iteratively matched using the ICP point cloud registration algorithm to calculate the pose deviation matrix and drive the robotic arm to move to compensate for the initial position deviation.

[0032] Furthermore, an initial pose transformation matrix is ​​set, which includes an initial translation vector and a rotation angle. Each point in the measured point cloud data is transformed to the coordinates of the surface feature point cloud set using the initial pose transformation matrix, and the first transformation point is obtained. For each first transformation point in the surface feature point cloud set, the surface feature point with the closest Euclidean distance is found, establishing a corresponding point pair of "first transformation point - surface feature point". Based on the established corresponding point pairs, the error function is solved using the least squares method, and the pose transformation matrix with the minimum error function is obtained. The pose transformation matrix contains... The translation vector and rotation angle in the latest state are used to describe the position deviation and the rotation angle to describe the attitude deviation. The pose transformation matrix is ​​used as the initial pose transformation matrix for the next iteration, and the corresponding point pair steps are repeated until the pose deviation matrix is ​​obtained. When the error change between two adjacent iterations is less than a preset threshold or the number of iterations reaches the upper limit, the iteration stops and the pose deviation matrix is ​​output. According to the calculated pose deviation matrix, the six-degree-of-freedom multi-joint robot arm is controlled to adjust its pose so that the relative pose of the probe carried by the robot arm and the workpiece meets the detection requirements.

[0033] S102: The eddy current probe applies an alternating electromagnetic field to the surface of the workpiece, thereby inducing eddy currents in the workpiece. The probe impedance change caused by the eddy current is detected to obtain the induced impedance signal. Combined with the current pose of the eddy current probe, the defect is located, and the alignment between the defect location and the detection position is adjusted (i.e., to ensure that the eddy current probe can be accurately aligned with the defect location on the workpiece for accurate detection).

[0034] Eddy current detectors utilize the principle of electromagnetic induction. An internal excitation coil generates an alternating electromagnetic field. When this alternating electromagnetic field approaches a conductive material (such as a metal workpiece), it induces eddy currents within the workpiece. The distribution and magnitude of these eddy currents are influenced by the workpiece's material properties (such as electrical conductivity and magnetic permeability), geometry, and internal defects. As the eddy currents flow through the workpiece, they generate a reverse electromagnetic field. This reverse field interacts with the original electromagnetic field generated by the probe, thus altering the probe's impedance. The impedance change of the eddy current probe contains information about the workpiece's material properties, geometry, and internal defects; therefore, the internal condition of the workpiece can be inferred by detecting the impedance change of the eddy current probe.

[0035] In this implementation, the internal clocks of the robotic arm controller, eddy current detector, and structured light 3D scanner are acquired and uniformly calibrated. A time synchronization algorithm is then used to control the clock errors of each device within a specified range. Within this range, to ensure consistency in the time reference between subsequent data acquisition and action execution, the excitation frequency range of the eddy current detector is set to 1kHz- It supports multi-frequency switching to adapt to the detection needs of different defect types (such as depth and size), and is configured with an FPGA data acquisition unit to set the sampling frequency to... It is used to capture the impedance signal (including real and imaginary features) output by the eddy current probe in real time.

[0036] More specifically, the eddy current detector detects the workpiece in real time under multi-frequency excitation and outputs an impedance signal to the workpiece. The FPGA data acquisition unit synchronously acquires the impedance signal to generate continuous signal frame data. When each frame of signal data is acquired, the current time tag is automatically bound, and the signal data of each frame is bound to the timing tag and the pose data of the six-degree-of-freedom multi-joint robot (including XYZ coordinates and Euler angles). The pose data is associated with the impedance signal and timing tag of the corresponding frame and the detection data for storage (storing impedance-pose timing data).

[0037] Furthermore, a torque sensor can be installed on the six-degree-of-freedom multi-joint robotic arm to monitor the contact pressure between the eddy current probe and the workpiece in real time. The measured pressure value is continuously compared with the preset pressure threshold. If the pressure does not exceed the preset pressure threshold, the robotic arm performs the detection action normally according to the preset trajectory. If the pressure exceeds the preset pressure threshold, the protection mechanism is immediately triggered, and the six-degree-of-freedom multi-joint robotic arm is controlled to perform a reverse displacement compensation of 0.1mm to reduce the contact pressure between the eddy current probe and the workpiece, and avoid rigid collisions that could damage the eddy current probe or the workpiece.

[0038] S103: Obtain the signal-to-noise ratio of the physical scan of the workpiece to be tested, and iteratively optimize the signal-to-noise ratio within a preset time. Combined with the detection environment status, adjust the parameters and output the optimized parameters of the eddy current probe (i.e., determine the optimal detection pose).

[0039] A Deep Q-Network (DQN) core network is constructed to achieve online learning and real-time optimization of the agent (control system) through the dynamic coordination of three elements: state space perception of environmental state, action space execution of control decisions, and reward function quantification of action value. In this implementation, the DQN core network perceives and detects the environment through the state space, collects the detection state in real time, and transforms the detection state into a quantified feature vector. Then, it makes decisions through the decision space, selecting actions based on the parameter categories within the feature vector. Finally, a reward function is constructed, combining signal-to-noise ratio (SNR) rewards, defect recognition rewards, and constraint penalties to obtain the SNR-based incentive state. Positive incentive actions are selected and iterated based on the incentive state to achieve maximum SNR optimization and output of probe optimization parameters.

[0040] Specifically, construct the objective function: (1); In the formula, Let be the objective function. For signal-to-noise ratio, , Let be the decision vector. The probe tilt angle of the eddy current detector. It is the lift-off distance. It is the excitation frequency; (2); (3); (4); in, Defect response range Internal defect signals The effective value, For the defect-free region Noise signals within The mean square of.

[0041] Based on the objective function, and considering the constraints of the decision vector and the preset time, the signal-to-noise ratio is iteratively optimized to its maximum value. The maximum value of the signal-to-noise ratio iteration within the preset time is obtained, and the optimal probe tilt angle, optimal lift-off distance, and optimal excitation frequency at the maximum signal-to-noise ratio are recorded. Finally, the optimal detection pose of the eddy current detector is output.

[0042] In this implementation, a coupled model is established for simulation, theoretical detection data is acquired, and the theoretical detection data and actual detection results are verified. Specifically, a digital twin model consistent with the actual workpiece under test is created in the simulation software. The digital twin model presets artificial defect parameters and incorporates physical parameters such as material conductivity, magnetic permeability, and detection environment temperature to establish an "electromagnetic-thermal" multiphysics coupling model to simulate multi-factor interference scenarios in actual detection, simulate multiphysics eddy current response, and generate "theoretical impedance-pose timing data". The timestamps of the "theoretical impedance-pose timing data" are aligned with the timing labels of the actual detection data (i.e., the impedance-pose timing data obtained from detection), and the cross-correlation coefficient and root mean square error are calculated. Finally, a quantitative report is output, which may include a positioning error distribution map, defect detection rate, signal-to-noise ratio improvement, and DQN reward convergence curve, forming a "scan-optimize-verify" closed loop to ensure the reliability of detection in complex curved surface scenarios.

[0043] Figure 2 An automatic eddy current detection and calibration system is shown, comprising: The pose deviation calculation unit 201 is configured to: pre-build a workpiece model based on the workpiece entity to be measured, obtain the measured point cloud data of the surface of the workpiece entity to be measured, match the measured point cloud data and the workpiece model, calculate the pose deviation matrix, and control the robotic arm carrying the eddy current probe to adjust the pose according to the pose deviation matrix to obtain the current pose of the eddy current probe. The associated data generation unit 202 is configured to: acquire the induced impedance signal caused by the eddy current change of the workpiece under test in the current pose, and obtain the associated data between the current pose and the induced impedance signal. The optimal detection pose generation unit 203 is configured to: locate the defects of the workpiece entity under test based on the associated data, calculate the signal-to-noise ratio of the workpiece entity under test based on the defect location results, iteratively optimize the signal-to-noise ratio within a preset time, and determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximum.

[0044] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0045] According to another embodiment of the present invention, the system of this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method of the present invention on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0046] Figure 3 A computer device is shown, which includes a processor 301, a communication interface 302, and a computer-readable storage medium 303. The processor 301, communication interface 302, and computer-readable storage medium 303 can be connected via a bus or other means.

[0047] The communication interface 302 is used to receive and send data. The computer-readable storage medium 303 can be stored in the memory of the electronic device. The computer-readable storage medium 303 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the computer-readable storage medium 303.

[0048] The processor 301 is the computing and control core of the electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve the corresponding method flow or corresponding function.

[0049] Processor 301 is configured to perform the following procedure: Based on the pre-constructed workpiece model of the workpiece entity, the measured point cloud data of the surface of the workpiece entity is obtained, the measured point cloud data and the workpiece model are matched, the pose deviation matrix is ​​calculated, and the pose adjustment of the robotic arm carrying the eddy current probe is controlled according to the pose deviation matrix to obtain the current pose of the eddy current probe. Under the current pose, acquire the induced impedance signal caused by the eddy current change of the workpiece under test, and obtain the correlation data between the current pose and the induced impedance signal; Based on the associated data, the defects of the workpiece under test are located. The signal-to-noise ratio of the workpiece under test is calculated based on the defect location results. The signal-to-noise ratio is iteratively optimized within a preset time to determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

[0050] This invention also provides a computer-readable storage medium, which is a memory device in an electronic device for storing programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space for storing the processing system of the electronic device.

[0051] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM memory or unstable memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0052] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to perform the following process: Based on the pre-constructed workpiece model of the workpiece entity, the measured point cloud data of the surface of the workpiece entity is obtained, the measured point cloud data and the workpiece model are matched, the pose deviation matrix is ​​calculated, and the pose adjustment of the robotic arm carrying the eddy current probe is controlled according to the pose deviation matrix to obtain the current pose of the eddy current probe. Under the current pose, acquire the induced impedance signal caused by the eddy current change of the workpiece under test, and obtain the correlation data between the current pose and the induced impedance signal; Based on the associated data, the defects of the workpiece under test are located. The signal-to-noise ratio of the workpiece under test is calculated based on the defect location results. The signal-to-noise ratio is iteratively optimized within a preset time to determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

[0053] The present invention also provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process: Based on the pre-constructed workpiece model of the workpiece entity, the measured point cloud data of the surface of the workpiece entity is obtained, the measured point cloud data and the workpiece model are matched, the pose deviation matrix is ​​calculated, and the pose adjustment of the robotic arm carrying the eddy current probe is controlled according to the pose deviation matrix to obtain the current pose of the eddy current probe. Under the current pose, acquire the induced impedance signal caused by the eddy current change of the workpiece under test, and obtain the correlation data between the current pose and the induced impedance signal; Based on the associated data, the defects of the workpiece under test are located. The signal-to-noise ratio of the workpiece under test is calculated based on the defect location results. The signal-to-noise ratio is iteratively optimized within a preset time to determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

[0054] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0055] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0056] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An automatic eddy current detection and calibration method, characterized in that, Includes the following processes: Based on the pre-constructed workpiece model of the workpiece entity, the measured point cloud data of the surface of the workpiece entity is obtained, the measured point cloud data and the workpiece model are matched, the pose deviation matrix is ​​calculated, and the pose deviation matrix is ​​controlled to adjust the pose of the robotic arm carrying the eddy current probe according to the pose deviation matrix to obtain the current pose of the eddy current probe. Under the current pose, acquire the induced impedance signal caused by the eddy current change of the workpiece under test, and obtain the correlation data between the current pose and the induced impedance signal. Based on the associated data, the defects of the workpiece entity under test are located. The signal-to-noise ratio of the workpiece entity under test is calculated in combination with the defect location results. The signal-to-noise ratio is iteratively optimized within a preset time to determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

2. The eddy current automatic detection and calibration method as described in claim 1, characterized in that, Solving the pose deviation matrix includes: A pre-constructed workpiece model of the workpiece entity to be tested is constructed, and the surface feature point cloud of the workpiece model is extracted, wherein the surface feature point cloud contains theoretical three-dimensional coordinate information; Encoded stripes are projected onto the surface of the workpiece and reflected light signals are collected to generate the measured point cloud data, which contains the real three-dimensional coordinate information of the surface of the workpiece. The surface feature point cloud and the measured point cloud data are iteratively matched to calculate the pose deviation matrix.

3. The eddy current automatic detection and calibration method as described in claim 2, characterized in that, The surface feature point cloud and the measured point cloud data are iteratively matched to calculate the pose deviation matrix, including: An initial pose transformation matrix is ​​set, and each point in the measured point cloud data is transformed to the coordinates of the surface feature point cloud through the initial pose transformation matrix to obtain the first transformed point. In the surface feature point cloud, the surface feature point with the closest Euclidean distance to each first transformed point is found, and a point pair between the first transformed point and the surface feature point is established. Based on the established point pairs, the error function is solved using the least squares method to obtain the pose transformation matrix when the error function is minimized. The pose transformation matrix obtained by solving is used as the initial pose transformation matrix for the next iteration. The process of establishing point pairs is repeated. When the error change between two adjacent iterations is less than a preset threshold or the number of iterations reaches the upper limit, the iteration is stopped, and the pose deviation matrix is ​​obtained.

4. The eddy current automatic detection and calibration method as described in claim 1, characterized in that, The system monitors the contact pressure between the eddy current probe and the workpiece in real time, compares the contact pressure with a preset pressure threshold. If the contact pressure is less than or equal to the preset pressure threshold, the system performs the detection action normally according to the preset trajectory. If the contact pressure is greater than the preset pressure threshold, the system triggers a protection mechanism and controls the robotic arm to carry the eddy current probe to perform reverse displacement compensation over a set distance.

5. The eddy current automatic detection and calibration method as described in claim 1, characterized in that, A signal-to-noise ratio objective function is constructed with probe tilt angle, lift-off distance, and excitation frequency as decision variables. The optimal detection pose of the eddy current probe is determined by maximizing the signal-to-noise ratio objective function.

6. The eddy current automatic detection and calibration method as described in claim 5, characterized in that, The signal-to-noise ratio objective function, which calculates the signal-to-noise ratio of the workpiece entity under test by combining the defect location results, includes: ,in, It refers to the effective value of the defect signal within the defect response range. The mean square of the noise signal within the defect-free interval is given, wherein the defect response interval and the defect-free interval are determined based on the induced impedance signal.

7. An automatic eddy current detection and calibration system, characterized in that, include: The pose deviation calculation unit is configured to: pre-build a workpiece model based on the workpiece entity to be tested, obtain measured point cloud data of the surface of the workpiece entity to be tested, match the measured point cloud data and the workpiece model, calculate the pose deviation matrix, and control the robotic arm carrying the eddy current probe to adjust the pose according to the pose deviation matrix to obtain the current pose of the eddy current probe. The associated data generation unit is configured to: acquire the induced impedance signal caused by the eddy current change of the workpiece under test in the current pose, and obtain the associated data between the current pose and the induced impedance signal. The optimal detection pose generation unit is configured to: locate defects in the workpiece entity under test based on the associated data, calculate the signal-to-noise ratio of the workpiece entity under test based on the defect location results, iteratively optimize the signal-to-noise ratio within a preset time, and determine the optimal detection pose of the eddy current probe when the signal-to-noise ratio is maximized.

8. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the eddy current automatic detection and calibration method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the eddy current automatic detection and calibration method as described in any one of claims 1 to 6.

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