A TOFD ultrasonic detection method, system, equipment and medium with adaptive control

Abstract

This application relates to the technical field of ultrasonic testing, and more particularly to an adaptively controlled TOFD ultrasonic testing method, system, equipment, and medium. This application first establishes a system parameter model based on detection configuration information, then divides the detection area into overlapping sub-areas and sets independent detection parameters for each sub-area. During the detection process, the parameters are adaptively adjusted through real-time signal feature analysis. Through an overlapping coverage partitioning strategy and a multi-layer parameter optimization mechanism, the uniformity and reliability of large-scale detection are improved.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic detection, and in particular to an adaptively controlled TOFD ultrasonic detection method, system, equipment, and medium. Background Art

[0002] As an important nondestructive testing method, TOFD ultrasonic testing technology is used in industrial weld quality inspection. This technology uses the principle of ultrasonic diffraction to locate and evaluate defects through transmitting and receiving probes, and has the advantages of high detection accuracy and strong reliability.

[0003] In the prior art, TOFD detection is usually performed using a fixed parameter setting method. The system performs the entire detection process according to pre-set detection parameters, and these parameters remain constant during the detection process.

[0004] However, due to the uneven thickness and surface conditions of the material being inspected, fixed parameter settings are difficult to adapt to the changing characteristics of the inspection area. This leads to uneven detection sensitivity and unstable signal quality, especially when inspecting large areas, which can easily lead to missed detections and misjudgments. This situation needs further improvement. Summary of the Invention

[0005] In order to solve the problem that existing fixed parameter settings are difficult to adapt to the changing characteristics of the detection area, resulting in uneven detection sensitivity and unstable signal quality, the present application provides an adaptively controlled TOFD ultrasonic detection method, system, equipment and medium, which adopts the following technical solutions:

[0006] In a first aspect, the present application provides an adaptively controlled TOFD ultrasonic detection method, comprising the following steps:

[0007] Collecting detection configuration information, and generating system parameter information based on the detection configuration information, wherein the detection configuration information includes detection range, material type, and expected defect type;

[0008] Collecting information about the inspected area and generating partitioned detection parameters based on the inspected area information and the system parameter information; wherein the inspected area is divided into a plurality of overlapping sub-areas for independent control, the partitioned detection parameters include a transmission gain, a receiving gain, a threshold value, and a time delay compensation value for each sub-area; and the inspected area information includes material thickness and surface condition;

[0009] Based on the partition detection parameters and the system parameter information, perform TOFD detection to obtain a detection signal;

[0010] Collecting partition signal characteristic information based on the detection signal, comparing the partition signal characteristic information with a preset standard to obtain a characteristic deviation value, comparing the characteristic deviation value with a preset change threshold value for a range, and generating a parameter optimization instruction if the characteristic deviation value is outside the preset change threshold range;

[0011] Based on the parameter optimization instruction, parameter correction information is generated, and based on the parameter correction information, partition parameter adjustment is performed.

[0012] By adopting the above-mentioned technical solution, in order to solve the problem that the detection parameters in TOFD ultrasonic testing are difficult to adapt to the changes in the characteristics of a large-scale detection area, for example, when inspecting the welds of large pressure vessels, due to the large span of the weld area, the material thickness may gradually change from 20mm to 50mm, and the surface condition also changes from roughness Ra3.2 to Ra6.3. If the traditional fixed parameter setting method is adopted, there will be severe signal attenuation in the thicker area, resulting in missed detection, and in the rough surface area, it may cause misjudgment due to excessive scattering noise. The present application first establishes a system parameter model based on the detection configuration information, and then divides the detection area into overlapping sub-areas and sets independent detection parameters for each sub-area. During the detection process, the parameters are adaptively adjusted through real-time signal feature analysis. The uniformity and reliability of large-scale detection are improved through the overlapping coverage partitioning strategy and multi-layer parameter optimization mechanism.

[0013] Optionally, the collecting detection configuration information and generating system parameter information based on the detection configuration information specifically includes:

[0014] Based on the material type, obtaining material acoustic characteristic parameters;

[0015] Acquiring probe combination parameters based on the detection range and the expected defect type;

[0016] The material acoustic characteristic parameters and the probe combination parameters are associated to generate the system parameter information.

[0017] By adopting the above technical solution, the present application first obtains acoustic characteristic parameters such as sound velocity and attenuation coefficient based on the material type, and determines the combined parameters such as probe frequency and layout based on the detection range and expected defect type. Then, the system parameters are generated through the systematic association of the material acoustic characteristics with the probe parameters, thereby ensuring the theoretical rationality of the detection parameter setting.

[0018] Optionally, the material acoustic characteristic parameters include sound velocity and attenuation coefficient, and the probe combination parameters include probe frequency and probe arrangement parameters. Correlating the material acoustic characteristic parameters with the probe combination parameters to generate the system parameter information specifically includes the following steps:

[0019] determining the probe incident angle according to the sound velocity;

[0020] determining a probe spacing according to the probe incident angle and the sound velocity;

[0021] The scanning speed is determined according to the probe frequency and the probe spacing.

[0022] By adopting the above technical solution, in actual detection, problems such as insufficient diffraction wave coverage due to the mismatch between the material sound velocity and the incident angle, or uneven sampling point density due to the mismatch between the probe spacing and the scanning speed often occur. The present application first determines the optimal incident angle based on the material sound velocity, then calculates the appropriate probe spacing based on the incident angle and sound velocity, and finally optimizes the scanning speed according to the probe frequency and spacing, thereby ensuring the physical correlation between the various parameters, and improving the overall matching of the system parameters through progressive optimization between the parameters.

[0023] Optionally, collecting the inspected area information and generating the partition detection parameters based on the inspected area information and the system parameter information specifically includes the following steps:

[0024] Dividing the inspected area into a plurality of overlapping sub-areas according to the material thickness and the surface condition;

[0025] Determine the transmission gain and the receiving gain of each sub-area based on the surface conditions of each sub-area;

[0026] Based on the material thickness of each sub-region, a threshold value and a time delay compensation value of each sub-region are determined.

[0027] By adopting the above technical solution, in order to solve the problem that the large differences in detection area characteristics in TOFD detection make it difficult for a single parameter setting to meet the detection requirements; this application first divides the detection area into multiple overlapping sub-areas according to the changing characteristics of material thickness and surface condition, and then configures the transmission and receiving gains according to the surface condition. At the same time, it optimizes the threshold value and delay compensation based on the material thickness, which not only realizes the regional adaptation of the detection parameters, but also solves the problem of detection uniformity in areas with different characteristics through parameter decoupling configuration and regional overlapping transition.

[0028] Optionally, based on the material thickness of each sub-region, determining the threshold value and time delay compensation value of each sub-region specifically includes the following steps:

[0029] Obtaining thickness distribution information of the sub-region, and calculating an acoustic wave attenuation compensation coefficient based on the thickness distribution information;

[0030] determining a threshold value of the sub-region based on the sound wave attenuation compensation coefficient;

[0031] Obtaining thickness change gradient information of the sub-region, and calculating an acoustic path difference compensation coefficient based on the thickness change gradient information;

[0032] Based on the sound path difference compensation coefficient, a time delay compensation value of the sub-region is determined.

[0033] By adopting the above technical solution, the present application first calculates the sound wave attenuation compensation coefficient based on the thickness distribution information and optimizes the threshold value accordingly, then calculates the sound path difference compensation coefficient based on the thickness change gradient information and determines the time delay compensation value, forming a complete parameter compensation system.

[0034] Optionally, the characteristic deviation value is compared with a preset change threshold value. If the characteristic deviation value is outside the preset change threshold value range, a parameter optimization instruction is generated, which specifically includes the following steps:

[0035] Obtaining partition signal change trend information according to the characteristic deviation value;

[0036] Acquiring signal stability status information, and triggering a detection distance calibration instruction according to the signal stability status information;

[0037] Obtaining a detection distance calibration result, and determining a current detection state based on the detection distance calibration result and the partition signal change trend information;

[0038] generating an amplitude deviation and a time deviation according to the current detection state;

[0039] generating a gain adjustment instruction based on the amplitude deviation;

[0040] A time delay compensation adjustment instruction is generated based on the time deviation.

[0041] By adopting the above technical solution, in actual detection, changes in signal characteristics often occur due to fluctuations in the probe coupling state or offsets in the scanning trajectory. If the gain is directly adjusted only according to the change in signal amplitude, the actual change in the detection state may be masked. For example, when the coupling is poor, even if the gain compensation is increased, an effective detection signal cannot be obtained. This application first analyzes the signal change trend, combines the signal stable state to trigger the distance calibration, comprehensively evaluates the current detection state through the calibration results and the change trend, and then extracts the amplitude deviation and time deviation respectively, generates gain adjustment and time compensation instructions, and forms a complete parameter optimization system.

[0042] In a second aspect, the present application provides an adaptively controlled TOFD ultrasonic detection system, comprising:

[0043] a system parameter information generating module, configured to collect detection configuration information and generate system parameter information based on the detection configuration information, wherein the detection configuration information includes detection range, material type, and expected defect type;

[0044] a partition detection parameter generation module, configured to collect information about the inspected area and generate partition detection parameters based on the inspected area information and the system parameter information; wherein the inspected area is divided into a plurality of overlapping sub-areas for independent control, and the partition detection parameters include the transmission gain, receiving gain, threshold value, and time delay compensation value of each sub-area; and the inspected area information includes material thickness and surface condition;

[0045] A detection signal acquisition module, configured to perform TOFD detection based on the partition detection parameters and the system parameter information to acquire a detection signal;

[0046] a comparison module, configured to collect partition signal characteristic information based on the detection signal, compare the partition signal characteristic information with a preset standard to obtain a characteristic deviation value, compare the characteristic deviation value with a preset change threshold value for a range, and generate a parameter optimization instruction if the characteristic deviation value is outside the preset change threshold range;

[0047] The parameter adjustment module is used to generate parameter correction information based on the parameter optimization instruction, and perform partition parameter adjustment based on the parameter correction information.

[0048] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned adaptively controlled TOFD ultrasonic detection method when executing the computer program.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned adaptively controlled TOFD ultrasonic detection method.

[0050] In summary, this application includes at least one of the following beneficial technical effects:

[0051] 1. This application first establishes a system parameter model based on the detection configuration information. It then divides the detection area into overlapping sub-areas and sets independent detection parameters for each sub-area. During the detection process, the parameters are adaptively adjusted through real-time signal feature analysis. Through the overlapping coverage partitioning strategy and multi-layer parameter optimization mechanism, the uniformity and reliability of large-scale detection are improved.

[0052] 2. This application first obtains acoustic characteristic parameters such as sound velocity and attenuation coefficient based on the material type. Combined parameters such as probe frequency and layout are determined based on the detection range and expected defect type. Then, system parameters are generated by systematically correlating the material acoustic characteristics with the probe parameters, ensuring the theoretical rationality of the detection parameter settings.

[0053] 3. This application first determines the optimal incident angle based on the material sound velocity, then calculates the appropriate probe spacing based on the incident angle and sound velocity, and finally optimizes the scanning speed based on the probe frequency and spacing, ensuring the physical correlation between various parameters. Moreover, through progressive optimization between parameters, the overall matching of system parameters is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 1 is a flow chart of an adaptively controlled TOFD ultrasonic detection method according to an embodiment of the present application;

[0055] Figure 2 1 is a flow chart of an adaptively controlled TOFD ultrasonic detection method S100 according to an embodiment of the present application;

[0056] Figure 3 1 is a flow chart of an adaptively controlled TOFD ultrasonic detection method S200 according to an embodiment of the present application;

[0057] Figure 4 2 is a flow chart of an adaptively controlled TOFD ultrasonic detection method S230 according to an embodiment of the present application;

[0058] Figure 5 4 is a flow chart of an adaptively controlled TOFD ultrasonic detection method S400 according to an embodiment of the present application;

[0059] Figure 6 This is a module diagram of an adaptively controlled TOFD ultrasonic detection system according to an embodiment of the present application;

[0060] Figure 7 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0062] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0063] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0064] In the first aspect, the present application provides an adaptively controlled TOFD ultrasonic detection method, referring to Figure 1 , including the following steps:

[0065] S100: Collect detection configuration information, and generate system parameter information based on the detection configuration information.

[0066] The inspection configuration information includes the inspection range, material type, and expected defect type.

[0067] In this embodiment, the detection range is used to determine the geometric dimensions of the workpiece to be inspected, the material type is used to determine the acoustic wave propagation characteristic parameters, and the expected defect type is used to optimize the inspection strategy. The collection of this configuration information is completed through manual input or automatic recognition.

[0068] For example, for pressure vessel weld inspection, the inspection range can be set to 100mm on each side of the weld, with a total length of 2000mm. The primary defects expected to be detected are lack of fusion and cracks. Based on this input, the system automatically generates system parameters, including probe frequency selection and scanning range settings.

[0069] S200 , collecting information of the inspected area, and generating partition detection parameters based on the inspected area information and system parameter information.

[0070] The inspected area is divided into several overlapping sub-areas for independent control. The partition detection parameters include the transmission gain, receiving gain, threshold value and time delay compensation value of each sub-area. The inspected area information includes material thickness and surface condition.

[0071] In this embodiment, material thickness information can be obtained through ultrasonic thickness measurement or mechanical measurement, and surface condition can be determined through roughness measurement or visual inspection. Based on this information, the system will partition the inspection area and configure independent inspection parameters for each sub-area.

[0072] Specifically, for a special-shaped connector with a thickness gradually transitioning from 20mm to 40mm, the inspection area can be divided into three overlapping sub-areas: 20-30mm, 25-35mm, and 30-40mm. Adjacent areas overlap by 5mm to ensure detection continuity. Each sub-area is configured with corresponding inspection parameters based on its characteristics. For example, a higher transmission gain is set for thicker areas to compensate for attenuation losses.

[0073] S300: Perform TOFD detection based on partition detection parameters and system parameter information to obtain a detection signal.

[0074] Specifically, TOFD testing uses dual-probe technology: one probe acts as a transmitter to emit ultrasonic waves, and the other as a receiver to receive diffracted wave signals. During the test, the probe pair moves along a pre-set scanning path, collecting ultrasonic test data from each sub-area in real time.

[0075] S400. Based on the detection signal, the partition signal characteristic information is collected, the partition signal characteristic information is compared with the preset standard to obtain a characteristic deviation value, and the characteristic deviation value is compared with the preset change threshold value. If the characteristic deviation value is outside the preset change threshold range, a parameter optimization instruction is generated.

[0076] In this embodiment, the system extracts characteristic parameters of the detection signal in real time, including the shear wave amplitude, the longitudinal wave amplitude ratio, and the signal arrival time, and compares them with pre-set standards to evaluate the stability of the detection state.

[0077] Specifically, when the shear wave amplitude of a sub-area is lower than 80% of the preset standard, or the signal arrival time deviation exceeds 0.5μs, the system determines that the detection parameters of the area need to be optimized and adjusted, and then triggers the parameter optimization process.

[0078] S500 : Generate parameter correction information based on the parameter optimization instruction, and perform partition parameter adjustment based on the parameter correction information.

[0079] In this embodiment, the system generates corresponding parameter correction instructions based on the characteristic deviation analysis results, including gain adjustment instructions and time compensation adjustment instructions, and performs parameter updates in real time.

[0080] Specifically, when amplitude attenuation is detected, the system generates a +3dB gain adjustment instruction; when it is found that the signal delay increases, it generates a +0.2μs time compensation adjustment instruction.

[0081] In one embodiment, referring to Figure 2 In step S100, detection configuration information is collected and system parameter information is generated based on the detection configuration information, specifically including:

[0082] S110. Obtain material acoustic characteristic parameters based on the material type.

[0083] Among them, the acoustic characteristic parameters of the material mainly include the sound velocity and the attenuation coefficient. In this embodiment, a material database is pre-set, and the system can obtain these parameters by querying the built-in material database or by measuring standard test blocks.

[0084] Specifically, when you enter a material type, the system automatically matches the acoustic characteristic parameters of that material. These parameter values ​​can be fine-tuned based on actual measurement results.

[0085] S120. Obtain probe combination parameters based on the detection range and expected defect type.

[0086] Among them, the system will determine the basic parameter requirements of the probe according to the detection range, and optimize the specific configuration of the probe combination in combination with the expected defect type, significantly improving the pertinence and effectiveness of the detection. The system first classifies and parameterizes common defects according to their characteristics by establishing a library of expected defect types. The defect type library contains four major types of defects: cracks (such as transverse cracks, longitudinal cracks, root cracks, etc.), inclusions (such as slag inclusions, sand inclusions, pores, etc.), unfused types (such as root unfused, sidewall unfused, etc.) and geometric types (such as biting, misalignment, excess height, etc.). Each defect is marked with key parameters such as its typical size range, spatial orientation characteristics, and acoustic reflection characteristics. Based on these parameters, the system establishes a defect-probe characteristic mapping relationship to provide a basis for the selection of probe parameters.

[0087] Specifically, when inspecting a 30mm thick pressure vessel weld and anticipating crack-type defects, the system retrieves crack characteristic parameters from the defect type library and, based on the inspection range requirements, recommends a probe pair with a 5MHz frequency and a wafer diameter. If slag inclusions are the primary defect, based on the acoustic characteristic parameters in the defect library, it is recommended to lower the frequency to 3.5MHz for better penetration and possibly adjust the wafer size to 8mm for greater inspection coverage. The system also recommends detection sensitivity values ​​based on the difficulty of detecting different defect types.

[0088] S130 : Correlate the material acoustic characteristic parameters and the probe combination parameters to generate system parameter information.

[0089] Among them, the generation of system parameter information requires comprehensive analysis and correlation calculation of material properties and probe parameters. By establishing a mapping relationship between acoustic parameters and probe characteristics, a set of optimized system operating parameters can be obtained. The optimal incident angle is calculated based on the first critical angle of the shear wave (obtained by the ratio of the shear wave speed to the longitudinal wave speed), usually 1.8-2.0 times the first critical angle; the probe spacing is determined based on the workpiece thickness, the incident angle, and the distance from the center of the sound beam to the workpiece surface; the initial gain value needs to be calculated by comprehensively considering the basic gain, material attenuation compensation, and sound wave diffusion loss.

[0090] Specifically, when the material's shear wave velocity is 3240 m / s and a 5 MHz probe is used, the system first calculates the first critical angle, determining the optimal angle of incidence to be 60 degrees. It then calculates the probe spacing to be 75 mm based on the workpiece thickness and angle of incidence. For gain setting, the system uses 20 dB as the base gain, adding compensation for material attenuation (determined by the attenuation coefficient and the acoustic path distance) and diffusion loss (calculated based on the logarithm of the acoustic path distance), ultimately setting the initial gain to 42 dB to ensure sufficient detection sensitivity.

[0091] In one embodiment, referring to Figure 3 In step S200, the information of the inspected area is collected, and based on the inspected area information and the system parameter information, the partition detection parameters are generated, which specifically includes the following steps:

[0092] S210 , dividing the inspected area into a plurality of overlapping sub-areas according to the material thickness and surface condition.

[0093] In this embodiment, the system determines the partition position by analyzing the thickness variation rate and the surface roughness gradient.

[0094] Specifically, when the thickness difference between adjacent areas is greater than a preset thickness threshold, sub-areas are divided at the thickness mutation point; when the surface roughness change exceeds a preset roughness threshold, sub-areas are divided at the surface condition mutation point; the overlap of adjacent sub-areas is not less than a preset overlap ratio, which is determined based on the maximum thickness of the detection area; the detection range of each sub-area does not exceed a preset maximum detection span, which is determined based on the probe detection sensitivity.

[0095] S220: Determine the transmission gain and the receiving gain of each sub-area based on the surface conditions of each sub-area.

[0096] The system first calculates the coupling loss caused by surface roughness, then combines this with the reflection loss of the sound wave at the interface to determine the compensation gain. To avoid noise interference caused by overcompensation, the system sets an upper limit for gain compensation.

[0097] S230 : Determine a threshold value and a time delay compensation value for each sub-region based on the material thickness of each sub-region.

[0098] The threshold setting needs to consider the impact of material thickness on echo amplitude. A dynamic threshold strategy is generally adopted, allowing the threshold to adjust dynamically with the acoustic path distance. Time delay compensation is primarily used to compensate for time offsets caused by acoustic path differences in areas of varying thickness, ensuring the timing consistency of the detection signal.

[0099] In one embodiment, referring to Figure 4 In step S230, based on the material thickness of each sub-region, the threshold value and time delay compensation value of each sub-region are determined, which specifically includes the following steps:

[0100] S231 . Obtain thickness distribution information of the sub-region, and calculate an acoustic wave attenuation compensation coefficient based on the thickness distribution information.

[0101] In this embodiment, the thickness distribution information includes the maximum thickness, the minimum thickness, and the thickness variation characteristics within the sub-region.

[0102] Specifically, the system first obtains the thickness distribution curve of the area and then calculates the acoustic path loss based on the material attenuation coefficient. When the sound wave propagates in this area, the minimum and maximum acoustic paths are calculated. Based on this, the attenuation compensation coefficient range is determined. The compensation coefficient increases exponentially with the acoustic path distance.

[0103] S232: Determine a threshold value of the sub-region based on the sound wave attenuation compensation coefficient.

[0104] The threshold value is determined using a dynamic threshold strategy, combining the acoustic attenuation compensation coefficient with the baseline threshold value to generate a threshold curve that varies with the acoustic path. In this embodiment, the system pre-analyzes historical detection data to establish a mapping between the threshold value and the attenuation compensation coefficient, ensuring uniform detection sensitivity.

[0105] Specifically, the system generates a complete threshold curve by piecewise linear interpolation, so that the threshold value changes smoothly with the sound path.

[0106] S233 , obtaining thickness change gradient information of the sub-region, and calculating an acoustic path difference compensation coefficient based on the thickness change gradient information.

[0107] The thickness gradient reflects the rate of change in the thickness of the test area. The system calculates the local gradient by analyzing the thickness difference between adjacent measurement points. The calculation of the acoustic path difference compensation coefficient considers the geometric relationship between the incident angle and thickness change, establishing a functional relationship between the acoustic path difference and the thickness gradient.

[0108] S234: Determine a time delay compensation value for the sub-region based on the sound path difference compensation coefficient.

[0109] In this embodiment, determining the time delay compensation value requires converting the acoustic path difference compensation coefficient into a compensation value in the time domain. The system calculates the change in sound wave propagation time based on the material's sound velocity and the acoustic path difference, and achieves timing alignment of the signals using a pre-established compensation model.

[0110] In one embodiment, referring to Figure 5 In step S400, the characteristic deviation value is compared with the preset change threshold value. If the characteristic deviation value is outside the preset change threshold value range, a parameter optimization instruction is generated, which specifically includes the following steps:

[0111] S410: Obtain partition signal change trend information according to the characteristic deviation value.

[0112] In this embodiment, the partitioned signal change trend information reflects the variation patterns of the detection signal in both spatial and temporal dimensions. The system analyzes N frames of continuously acquired signal data, extracts the variation trends of characteristic parameters such as signal amplitude and phase, and establishes a correspondence between signal characteristics and detection locations.

[0113] Specifically, when inspecting a weld area, the system records the signal signature sequence at each sampling point. If the signal amplitude shows a continuous downward trend within a 100mm scanning distance, and the decrease exceeds 3dB, it is recorded as a "negative gradual change" trend; if the signal amplitude suddenly decreases by more than 5dB, it is recorded as a "step change" trend.

[0114] S420: Acquire signal stability status information, and trigger a detection distance calibration instruction based on the signal stability status information.

[0115] In this embodiment, signal stability refers to the waveform characteristics of the detection signal remaining relatively constant over a certain period of time. The system assesses the signal's stability by calculating statistical characteristics such as the signal's variance and standard deviation. When signal stability meets preset conditions, the system automatically triggers the detection range calibration process.

[0116] S430: Obtain a detection distance calibration result, and determine a current detection state based on the detection distance calibration result and the partition signal change trend information.

[0117] The detection range calibration results include a comparison between the measured and theoretical sound range values. The system correlates the calibration results with signal trend analysis to comprehensively assess the detection system's operating status.

[0118] Specifically, if the calibration results show that the measured sound path deviates from the theoretical value by more than 0.5mm, and the signal exhibits a negative gradient, the system determines that the current detection state is "parameter offset." This indicates that the detection parameters need to be compensated and adjusted to maintain detection accuracy.

[0119] S440: Generate an amplitude deviation and a time deviation according to the current detection state.

[0120] The system calculates these two types of deviations by comparing the characteristic parameters of the measured signal with those of the reference signal. The deviation calculation process takes into account the spatial distribution characteristics and temporal characteristics of the signal.

[0121] S450: Generate a gain adjustment instruction based on the amplitude deviation.

[0122] Gain adjustment commands are used to compensate for systematic deviations in signal amplitude. Based on the magnitude and direction of the amplitude deviation, the system calculates the required gain compensation and generates the corresponding adjustment command. Gain adjustment uses a gradual strategy to avoid signal distortion caused by overcompensation.

[0123] S460: Generate a time delay compensation adjustment instruction based on the time deviation.

[0124] The time delay compensation adjustment instruction is used to correct the timing relationship of the signal. The system converts the time deviation into the offset of the sampling point and achieves signal alignment by adjusting the sampling timing.

[0125] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0126] In the second aspect, the present application provides an adaptively controlled TOFD ultrasonic detection system. The adaptively controlled TOFD ultrasonic detection system of the present application is described below in combination with the above-mentioned adaptively controlled TOFD ultrasonic detection method.

[0127] Reference Figure 6 , an adaptively controlled TOFD ultrasonic detection system, comprising:

[0128] a system parameter information generation module, configured to collect detection configuration information and generate system parameter information based on the detection configuration information, wherein the detection configuration information includes detection range, material type, and expected defect type;

[0129] A partition detection parameter generation module is used to collect information about the inspected area and generate partition detection parameters based on the inspected area information and system parameter information. The inspected area is divided into several overlapping sub-areas for independent control. The partition detection parameters include the transmit gain, receive gain, threshold value, and time delay compensation value of each sub-area. The inspected area information includes material thickness and surface condition.

[0130] A detection signal acquisition module is used to perform TOFD detection and obtain detection signals based on partition detection parameters and system parameter information;

[0131] A comparison module is used to collect partition signal feature information based on the detection signal, compare the partition signal feature information with a preset standard, obtain a feature deviation value, compare the feature deviation value with a preset change threshold value, and generate a parameter optimization instruction if the feature deviation value is outside the preset change threshold range;

[0132] The parameter adjustment module is used to generate parameter correction information based on the parameter optimization instruction, and perform partition parameter adjustment based on the parameter correction information.

[0133] In one embodiment, the system parameter information generation module specifically includes:

[0134] A material acoustic characteristic parameter acquisition unit, configured to acquire material acoustic characteristic parameters based on material type;

[0135] A probe combination parameter acquisition unit, used to acquire probe combination parameters based on the detection range and expected defect type;

[0136] The system parameter association unit is used to associate the material acoustic characteristic parameters with the probe combination parameters to generate system parameter information.

[0137] In one embodiment, the material acoustic characteristic parameters include sound velocity and attenuation coefficient, the probe combination parameters include probe frequency and probe arrangement parameters, and the system parameter association unit specifically includes:

[0138] An incident angle determination unit, used for determining the incident angle of the probe according to the sound velocity;

[0139] A probe spacing determination unit, for determining the probe spacing according to the probe incident angle and the sound velocity;

[0140] The scanning speed determination unit is used to determine the scanning speed according to the probe frequency and the probe spacing.

[0141] In one embodiment, the partition detection parameter generation module specifically includes:

[0142] The area division unit is used to divide the inspected area into several overlapping sub-areas according to the material thickness and surface condition;

[0143] a gain determination unit, configured to determine a transmission gain and a reception gain of each sub-area based on a surface condition of each sub-area;

[0144] The threshold compensation determination unit is used to determine the threshold value and time delay compensation value of each sub-region based on the material thickness of each sub-region.

[0145] In one embodiment, the threshold compensation determination unit specifically includes:

[0146] an attenuation compensation calculation unit, configured to obtain thickness distribution information of the sub-region and calculate an acoustic wave attenuation compensation coefficient based on the thickness distribution information;

[0147] a threshold value determining unit, configured to determine a threshold value of a sub-region based on an acoustic wave attenuation compensation coefficient;

[0148] an acoustic path difference compensation calculation unit, configured to obtain thickness change gradient information of the sub-region and calculate an acoustic path difference compensation coefficient based on the thickness change gradient information;

[0149] The time delay compensation determining unit is configured to determine a time delay compensation value of the sub-region based on the sound path difference compensation coefficient.

[0150] In one embodiment, the comparison module specifically includes:

[0151] A trend acquisition unit, used to obtain partition signal change trend information based on the characteristic deviation value;

[0152] A calibration trigger unit is used to obtain signal stability status information and trigger a detection distance calibration instruction based on the signal stability status information;

[0153] A state determination unit is used to obtain a detection distance calibration result and determine a current detection state based on the detection distance calibration result and the partition signal change trend information;

[0154] A deviation generating unit, configured to generate an amplitude deviation and a time deviation according to a current detection state;

[0155] a gain adjustment instruction generating unit, configured to generate a gain adjustment instruction based on the amplitude deviation;

[0156] The time compensation instruction generating unit is used to generate a time delay compensation adjustment instruction based on the time deviation.

[0157] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an adaptively controlled TOFD ultrasonic detection method is implemented.

[0158] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0159] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0160] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0161] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An adaptively controlled TOFD ultrasonic detection method, characterized in that: The steps include: Collecting detection configuration information, and generating system parameter information based on the detection configuration information, wherein the detection configuration information includes detection range, material type, and expected defect type; Collecting information about the inspected area and generating partitioned detection parameters based on the inspected area information and the system parameter information; wherein the inspected area is divided into a plurality of overlapping sub-areas for independent control, the partitioned detection parameters include a transmission gain, a receiving gain, a threshold value, and a time delay compensation value for each sub-area; and the inspected area information includes material thickness and surface condition; Based on the partition detection parameters and the system parameter information, perform TOFD detection to obtain a detection signal; Collecting partition signal characteristic information based on the detection signal, comparing the partition signal characteristic information with a preset standard to obtain a characteristic deviation value, comparing the characteristic deviation value with a preset change threshold value for a range, and generating a parameter optimization instruction if the characteristic deviation value is outside the preset change threshold range; generating parameter correction information based on the parameter optimization instruction, and performing partition parameter adjustment based on the parameter correction information; The process of collecting detection configuration information and generating system parameter information based on the detection configuration information specifically includes: Based on the material type, obtaining material acoustic characteristic parameters; Acquiring probe combination parameters based on the detection range and the expected defect type; The material acoustic characteristic parameters and the probe combination parameters are associated to generate the system parameter information.

2. The TOFD ultrasonic detection method with adaptive control according to claim 1, characterized in that: The material acoustic characteristic parameters include sound velocity and attenuation coefficient, and the probe combination parameters include probe frequency and probe arrangement parameters. Correlating the material acoustic characteristic parameters with the probe combination parameters to generate the system parameter information specifically includes the following steps: determining the probe incident angle according to the sound velocity; determining a probe spacing according to the probe incident angle and the sound velocity; The scanning speed is determined according to the probe frequency and the probe spacing.

3. The adaptively controlled TOFD ultrasonic detection method according to claim 1, characterized in that: Collecting the inspected area information and generating the partition detection parameters based on the inspected area information and the system parameter information specifically includes the following steps: Dividing the inspected area into a plurality of overlapping sub-areas according to the material thickness and the surface condition; Determine the transmission gain and the receiving gain of each sub-area based on the surface conditions of each sub-area; Based on the material thickness of each sub-region, a threshold value and a time delay compensation value of each sub-region are determined.

4. The adaptively controlled TOFD ultrasonic detection method according to claim 3, characterized in that: Based on the material thickness of each sub-region, the threshold value and time delay compensation value of each sub-region are determined, specifically including the following steps: Obtaining thickness distribution information of the sub-region, and calculating an acoustic wave attenuation compensation coefficient based on the thickness distribution information; determining a threshold value of the sub-region based on the sound wave attenuation compensation coefficient; Obtaining thickness change gradient information of the sub-region, and calculating an acoustic path difference compensation coefficient based on the thickness change gradient information; Based on the sound path difference compensation coefficient, a time delay compensation value of the sub-region is determined.

5. The adaptively controlled TOFD ultrasonic detection method according to claim 1, characterized in that: Comparing the characteristic deviation value with a preset change threshold value, and if the characteristic deviation value is outside the preset change threshold value range, generating a parameter optimization instruction, specifically comprising the following steps: Obtaining partition signal change trend information according to the characteristic deviation value; Acquiring signal stability status information, and triggering a detection distance calibration instruction according to the signal stability status information; Obtaining a detection distance calibration result, and determining a current detection state based on the detection distance calibration result and the partition signal change trend information; generating an amplitude deviation and a time deviation according to the current detection state; generating a gain adjustment instruction based on the amplitude deviation; A time delay compensation adjustment instruction is generated based on the time deviation.

6. An adaptively controlled TOFD ultrasonic detection system, characterized in that: include: a system parameter information generating module, configured to collect detection configuration information and generate system parameter information based on the detection configuration information, wherein the detection configuration information includes detection range, material type, and expected defect type; a partition detection parameter generation module, configured to collect information about the inspected area and generate partition detection parameters based on the inspected area information and the system parameter information; wherein the inspected area is divided into a plurality of overlapping sub-areas for independent control, and the partition detection parameters include the transmission gain, receiving gain, threshold value, and time delay compensation value of each sub-area; and the inspected area information includes material thickness and surface condition; A detection signal acquisition module, configured to perform TOFD detection based on the partition detection parameters and the system parameter information to acquire a detection signal; a comparison module, configured to collect partition signal characteristic information based on the detection signal, compare the partition signal characteristic information with a preset standard to obtain a characteristic deviation value, compare the characteristic deviation value with a preset change threshold value for a range, and generate a parameter optimization instruction if the characteristic deviation value is outside the preset change threshold range; A parameter adjustment module, configured to generate parameter correction information based on the parameter optimization instruction, and perform partition parameter adjustment based on the parameter correction information; The system parameter information generation module specifically includes: a material acoustic characteristic parameter acquisition unit, configured to acquire the material acoustic characteristic parameters based on the material type; a probe combination parameter acquisition unit, configured to acquire probe combination parameters based on the detection range and the expected defect type; The system parameter associating unit is used to associate the material acoustic characteristic parameters with the probe combination parameters to generate the system parameter information.

7. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the adaptively controlled TOFD ultrasonic detection method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptively controlled TOFD ultrasonic detection method according to any one of claims 1 to 5 are implemented.

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