Copper wire defect detection method and equipment

Abstract

The invention is suitable for the technical field of copper wire defect detection, and particularly relates to a copper wire defect detection method and equipment, and the method comprises the steps: obtaining an instantaneous phase difference and a temperature drift signal of a surface acoustic wave on the surface of a copper wire; wherein the surface acoustic wave is a standing wave type surface acoustic wave formed by interference on the surface of the copper wire through a double-pulse laser beam with a phase difference of 90 degrees; determining defect sensitive parameters based on the instantaneous phase difference and the temperature drift signal; wherein the defect sensitive parameter is used for representing the surface acoustic wave propagation characteristic change caused by defects in the copper wire; the defect equivalent depth of the copper wire is calculated based on the defect sensitive parameters, and the defect type of the copper wire is determined based on the defect sensitive parameters; wherein the defect equivalent depth is the average depth of all defects. According to the method, the precision and reliability of copper wire defect detection can be greatly improved, and interference or damage to the copper wire in the detection process can be avoided.

Description

Technical Field

[0001] This application belongs to the field of copper wire defect detection technology, and particularly relates to copper wire defect detection methods and equipment. Background Technology

[0002] Copper wire, as a crucial material for electrical connections, is widely used in power, communications, and electronics industries. With technological advancements, copper wire plays an increasingly vital role in transmitting electrical energy and signals. However, due to environmental factors, wear and tear during use, or defects in the manufacturing process, copper wire can develop various defects, such as cracks, corrosion, oxidation, and breakage. These defects lead to decreased performance of electrical equipment and can even pose safety hazards. Therefore, copper wire defect detection technology is particularly important for ensuring power system safety and improving equipment reliability.

[0003] In existing technologies, copper wires may have extremely fine cracks or scratches on their surface, and these tiny defects cannot be clearly detected by optical inspection methods. Many existing non-destructive testing methods, such as ultrasonic testing and eddy current testing, usually require the use of coupling agents or direct contact with the surface being tested, which may lead to poor physical contact, damage, or contamination of the copper wire. For defects such as tiny cracks, scratches, and oxide layers on the surface of copper wires, optical inspection methods can often only provide qualitative detection and cannot quantify the specific size of these defects, resulting in a lack of accurate data in the assessment of copper wire defects.

[0004] In summary, during the detection of copper wire defects, there are problems such as insufficient accuracy of the detection method and the influence of detection requirements on the copper wire, leading to inaccurate defect detection. Summary of the Invention

[0005] This application provides a method and device for detecting copper wire defects, which can solve the problem in related technologies where the detection of copper wire defects is inaccurate due to insufficient accuracy of the detection method and the influence of detection requirements on the copper wire.

[0006] In a first aspect, embodiments of this application provide a method for detecting copper wire defects, including: The instantaneous phase difference and temperature drift signal of surface acoustic waves on the surface of copper wire are obtained; wherein, the surface acoustic waves are standing wave type surface acoustic waves formed by interference of a double-pulse laser beam with a 90° phase difference on the surface of copper wire. Based on the instantaneous phase difference and the temperature drift signal, a defect-sensitive parameter is determined; wherein, the defect-sensitive parameter is used to characterize the change in the surface acoustic wave propagation characteristics in the copper wire caused by the defect; The equivalent depth of the defect in the copper wire is calculated based on the defect sensitivity parameter, and the defect type of the copper wire is determined based on the defect sensitivity parameter; wherein, the equivalent depth of the defect is the average depth of all defects.

[0007] The technical solutions described in this application embodiment have at least the following technical effects: The copper wire defect detection method provided in this application first acquires the instantaneous phase difference and temperature drift signals of surface acoustic waves (SAWs) (standing wave SAWs formed by the interference of a double-pulse laser beam with a 90° phase difference on the copper wire surface). Then, based on the instantaneous phase difference and temperature drift signals, it determines defect-sensitive parameters to characterize the changes in SAW propagation characteristics caused by defects in the copper wire. Finally, it calculates the equivalent depth of the defects in the copper wire based on the defect-sensitive parameters, and determines the defect type of the copper wire based on the defect-sensitive parameters. The equivalent depth of the defects is the average depth of all defects. This method can capture subtle propagation changes of surface acoustic waves (SAWs) through instantaneous phase difference and temperature drift signals, and can accurately identify phase disturbances caused by small defects (such as microcracks and pores). It uses laser interference to generate standing waves, achieving nanometer-level wavelength resolution, which significantly improves detection accuracy. This method can not only detect defects and identify their types, but also quantify their severity, providing data for subsequent repair or rejection. This method can not only significantly improve the accuracy and reliability of copper wire defect detection, but also completely avoid interference or damage to the copper wire itself during the detection process, effectively solving the core pain points of traditional detection methods in terms of accuracy and material adaptability.

[0008] In one possible implementation of the first aspect, the method further includes: The instantaneous phase difference and temperature drift signal of the surface acoustic wave at three spatial points on the surface of the copper wire are obtained by using dual wavelengths; wherein, the dual wavelengths include a 633nm wavelength and a 785nm wavelength, the 633nm wavelength is used to measure the surface displacement caused by the surface acoustic wave, and the 785nm wavelength is used to measure the thermal deformation of the copper wire, and the three spatial points include a center point and two points symmetrically distributed around the center point.

[0009] In one possible implementation of the first aspect, the method further includes, prior to acquiring the instantaneous phase difference and temperature drift signal of the surface acoustic wave at the copper wire surface: Obtain the diameter data and surface temperature of the copper wire; The operating frequency of the surface acoustic wave is determined based on the diameter data. The ideal sound velocity is corrected based on the surface temperature to obtain the corrected sound velocity; wherein, the ideal sound velocity is used to characterize the ideal propagation speed of the surface acoustic wave on the surface of the copper wire. The target grating period is calculated based on the operating frequency and the corrected sound velocity; wherein the grating is used to control the wavelength of the surface acoustic wave, and the target grating period is equal to the wavelength of the surface acoustic wave; The driving voltage is determined based on the target grating period; wherein the driving voltage is used to adjust the period of the grating to the target grating period.

[0010] In one possible implementation of the first aspect, the method further includes: The first frequency of the surface acoustic wave is determined based on the diameter data; The second frequency of the surface acoustic wave is determined based on a pre-set defect detection depth. The operating frequency of the surface acoustic wave is determined based on the first frequency and the second frequency.

[0011] In one possible implementation of the first aspect, determining the defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: Based on the instantaneous phase difference and temperature drift signal of the three points in space, the corrected phase of each point is calculated, and the surface displacement of each point is calculated based on the corrected phase of each point. The velocity of the particle at each point is obtained by differentiating the surface displacement at each point with respect to time. The surface acoustic wave propagation function is fitted based on the particle velocity at each point, and the actual sound velocity is extracted from the surface acoustic wave propagation function. The wave velocity offset is calculated based on the actual sound velocity and the corrected sound velocity; wherein the defect-sensitive parameters include the wave velocity offset, the resonance frequency shift, and the attenuation coefficient.

[0012] In one possible implementation of the first aspect, determining the defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: Perform a Fourier transform on the surface displacement of the center point to obtain the spectrum of the center point; Extract the frequency corresponding to the maximum amplitude in the spectrum of the center point to obtain the actual resonant frequency; The resonance frequency shift is calculated based on the actual resonance frequency and the reference resonance frequency; wherein the reference resonance frequency is the resonance frequency corresponding to the defect-free, calibrated sample copper wire.

[0013] In one possible implementation of the first aspect, determining the defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: By performing Hilbert transforms on the surface displacements of the two points symmetrically distributed around the center point, the envelope amplitudes of the two points symmetrically distributed around the center point are obtained. Extract the peak amplitudes of the two points symmetrically distributed around the center point from their envelope amplitudes; The attenuation coefficient is calculated based on the peak amplitude of the two points symmetrically distributed around the center point and the distance between the two points symmetrically distributed around the center point.

[0014] In one possible implementation of the first aspect, calculating the equivalent depth of the defect in the copper wire based on the defect sensitivity parameter includes: Calculate the relative wave velocity offset based on the wave velocity offset and the corrected sound velocity; The equivalent depth of the defect in the copper wire is calculated based on the relative offset of the wave velocity, the resonant frequency shift, and the attenuation coefficient.

[0015] In one possible implementation of the first aspect, determining the defect type of the copper wire based on the defect sensitivity parameter includes: The wave velocity offset, the resonance frequency shift, and the attenuation coefficient are combined to form a defect feature vector; Based on the defect feature vector, the most similar defect combinations are selected from the defect fingerprint database to obtain a candidate defect set; wherein, the defect fingerprint database includes various defect type combinations and defect feature thresholds corresponding to each defect type combination; If there is only one defect combination in the candidate defect set and the defect feature vector completely matches the defect feature threshold corresponding to the candidate defect set, the defect type in the candidate defect set is determined as the defect type of the copper wire. If there are multiple defect combinations in the candidate defect set, the weights of each type of defect in the candidate defect set are calculated based on the defect feature vectors and the defect response matrix, and the defect type of the copper wire is determined based on the weights of each type of defect in the candidate defect set; wherein, the defect response matrix is ​​constructed by the response feature vectors corresponding to each type of defect in the candidate defect set, and the response feature vectors include wave velocity offset, resonant frequency shift, and attenuation coefficient.

[0016] In one possible implementation of the first aspect, the method further includes: If there is only one defect combination in the candidate defect set but the defect feature vector does not completely match the defect feature threshold corresponding to the candidate defect set, a conflict parameter is determined based on the defect feature vector and the defect feature threshold corresponding to the candidate defect set, and a supplementary defect type is determined based on the conflict parameter. Extract the response feature vector corresponding to each defect type in the candidate defect set and the response feature vector corresponding to the supplementary defect type from the response data table; wherein, the response data table includes the response feature vector corresponding to each defect type; Based on the response feature vectors corresponding to each defect type in the candidate defect set and the response feature vectors corresponding to the supplementary defect types, the defect response matrix is ​​constructed. Then, based on the defect response matrix and the defect feature vectors, the weights of each type of defect in the candidate defect set and the weights of the supplementary defect types are solved using the non-negative matrix factorization method.

[0017] Secondly, embodiments of this application provide a copper wire defect detection device, comprising: The acquisition unit is used to acquire the instantaneous phase difference and temperature drift signal of the surface acoustic wave on the copper wire surface; wherein, the surface acoustic wave is a standing wave type surface acoustic wave formed by the interference of a double-pulse laser beam with a 90° phase difference on the copper wire surface. A defect-sensitive parameter determination unit is used to determine defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal; wherein, the defect-sensitive parameters are used to characterize the changes in the surface acoustic wave propagation characteristics caused by defects in the copper wire; The defect depth calculation and defect type identification unit is used to calculate the equivalent defect depth of the copper wire based on the defect sensitivity parameter, and to determine the defect type of the copper wire based on the defect sensitivity parameter; wherein, the equivalent defect depth is the average depth of all defects.

[0018] Thirdly, embodiments of this application provide a copper wire defect detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in any of the embodiments of the first aspect.

[0019] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a copper wire defect detection method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the implementation process of calculating the equivalent depth of defects and identifying the defect type in the copper wire defect detection method provided in this application embodiment; Figure 3 This is a schematic diagram of the copper wire defect detection device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the copper wire defect detection device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] In related technologies, optical inspection methods (such as microscopic examination or visual inspection) have certain limitations in detecting minute scratches, cracks, or oxide spots, especially in detecting nanoscale defects, which may not be clearly displayed in optical images. This is because the surface of copper wires may contain extremely fine cracks or scratches, and these tiny defects cannot be clearly captured by optical inspection methods, especially under low contrast conditions. As the copper wire is used over time, these minute defects may gradually expand, eventually affecting the conductivity of the copper wire and leading to potential malfunctions or safety hazards.

[0029] Many existing non-destructive testing methods, such as ultrasonic testing and eddy current testing, typically require the use of a coupling agent or direct contact with the surface being tested. This means that the copper wire surface must be in contact with the testing equipment, which can lead to poor physical contact, damage, or contamination. For certain high-precision, high-reliability applications, especially on high-speed production lines, any possible contact can affect the original state of the copper wire or introduce errors.

[0030] Traditional inspection methods have limitations in terms of accuracy, especially in the quantitative detection of minute defects. For defects such as tiny cracks, scratches, and oxide layers on the surface of copper wires, optical inspection methods can often only provide qualitative detection, but cannot quantify the specific size of these defects. This results in a lack of accurate data in the assessment of copper wire defects, making it impossible to effectively determine the impact of defects on the long-term performance of the copper wire.

[0031] To address the aforementioned issues, this application provides a method and apparatus for detecting defects in copper wires. The method first acquires the instantaneous phase difference and temperature drift signals of surface acoustic waves (SAWs) on the copper wire surface (standing wave SAWs formed by the interference of a dual-pulse laser beam with a 90° phase difference on the copper wire surface). Then, based on the instantaneous phase difference and temperature drift signals, defect-sensitive parameters are determined to characterize the changes in SAW propagation characteristics caused by defects in the copper wire. Finally, the equivalent depth of the defects in the copper wire is calculated based on the defect-sensitive parameters, and the defect type is determined based on the defect-sensitive parameters. The equivalent depth of the defects is the average depth of all defects. This method can capture subtle propagation changes of surface acoustic waves (SAWs) through instantaneous phase difference and temperature drift signals, accurately identifying phase disturbances caused by minute defects (such as microcracks and pores). The use of laser interference to generate standing waves provides nanometer-level wavelength resolution, significantly improving detection accuracy. This method not only detects defects and identifies their types but also quantifies their severity, providing data for subsequent repair or rejection. This method can not only significantly improve the accuracy and reliability of copper wire defect detection, but also completely avoid interference or damage to the copper wire itself during the detection process, effectively solving the core pain points of traditional detection methods in terms of accuracy and material adaptability.

[0032] The copper wire defect detection method provided in this application embodiment can be applied to copper wire defect detection equipment. In this case, the copper wire defect detection equipment is the executing entity of the copper wire defect detection method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of copper wire defect detection equipment.

[0033] For example, a copper wire defect detection device may include a laser emitting device, a detection device, and a control device that is communicatively connected to the laser emitting device and the detection device. The laser emitting device is a device capable of emitting a laser beam and exciting standing-wave surface acoustic waves (SAWs). It may include a pulsed laser (wavelength 532nm) and an acousto-optic modulator (AOM), a dual-pulse laser and a beam splitter, a coherent dual-beam laser interferometer system, etc. The detection device is capable of measuring parameters such as the phase change and temperature drift of the SAWs on the copper wire surface. It may include a dual-frequency laser interferometer, a laser Doppler interferometer (LDV), a phase-shifting interferometer, etc. The control device is capable of controlling the laser emitting device, the detection device, and performing data processing. The control device may be a tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), desktop computer, computing device or other processing device connected to a wireless modem, computer, laptop computer, handheld computing device, customer premises equipment (CPE), and / or other devices used for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or future evolved Public Land Mobile networks. Mobile terminals in a network (PLMN), etc.

[0034] To better understand the copper wire defect detection method provided in the embodiments of this application, the specific implementation process of the copper wire defect detection method provided in the embodiments of this application will be described by way of example below.

[0035] Figure 1 This illustration shows a schematic flowchart of a copper wire defect detection method provided in an embodiment of this application. The copper wire defect detection method includes: S100 acquires the instantaneous phase difference and temperature drift signals of the surface acoustic wave (SAW) on the copper wire surface. The SAW is a standing wave type SAW formed by the interference of a dual-pulse laser beam with a 90° phase difference on the copper wire surface.

[0036] For example, a pulsed laser (e.g., 532 nm wavelength, 10 ns pulse width, 50 mJ single pulse energy) can be used as the excitation source. Pulsed lasers have high power density and extremely short duration, making them suitable for exciting transient thermoelastic responses. After the laser output, an acousto-optic modulator (AOM) can be used to split the laser beam into two spatially independent, time-synchronized laser pulses. Laser beam 1 can be used as the reference beam, with its phase set to 0°. Laser beam 2 is modulated by the AOM to introduce a 90° phase lag, i.e., a phase difference of π / 2. This phase difference is a key condition for realizing interferometric standing waves.

[0037] Two laser beams are spatially combined and focused by a precision lens system, acting together on the same location on the copper wire surface. The diameter of the focused laser spot can be controlled to ≤20μm, which is beneficial for local excitation with high spatial resolution. The laser pulse irradiates the copper wire surface, causing local instantaneous thermal expansion, i.e., the thermoelastic effect. Due to the 90° phase difference between the two laser beams, their spatial and temporal superposition constitutes a standing wave heat source distribution. The standing wave heat source further excites a standing wave surface acoustic wave (SAW) on the copper wire surface. The wave characteristics of the SAW can include the initial phase, frequency, and propagation direction.

[0038] High-precision laser Doppler interferometers (LDVs) or optical phase interferometers can be used to detect surface displacement or velocity changes caused by surface acoustic waves (SAWs) propagating on a copper wire surface. The detection device can acquire the interference signal reflected from the copper wire surface and simultaneously acquire the response for each excitation cycle using a high-speed sampling card. The acquired data is a time-domain signal, including the displacement and velocity oscillation curves of the SAW. Algorithms such as Hilbert transform or short-time Fourier transform (STFT) can be used to perform envelope and phase demodulation on the interference signal to obtain the instantaneous phase (an actual phase curve) at each moment during propagation. A defect-free region on the copper wire can be selected as a reference region. Under the same temperature and excitation conditions, the instantaneous phase curve of the reference region can be extracted as a normal reference for propagation. The actual phase curve can be compared with the instantaneous phase curve of the reference region to calculate the phase difference change (phase difference curve). The phase difference change reflects the influence of defects or structural disturbances on the surface acoustic wave during propagation.

[0039] Without altering the laser excitation conditions, a small-range temperature variation (e.g., ±2~5°C) can be applied to the copper wire using an environmental temperature control device to repeatedly excite surface acoustic waves (SAWs) and collect multiple sets of phase response signals under temperature disturbances. Since temperature changes cause variations in parameters such as the elastic modulus, sound velocity, and coefficient of thermal expansion of the copper wire, leading to changes in SAW propagation speed and phase response, the temperature drift coefficient (temperature drift signal), Δφ / ΔT, can be extracted by comparing the phase response curves at different temperatures. Here, Δφ represents the phase difference, and ΔT represents the temperature difference. In practical defect analysis, the temperature drift component can be separated from the total phase change to obtain the phase change purely caused by the defect, improving the accuracy of defect identification.

[0040] This step enables highly sensitive, high-resolution, non-contact detection of changes in propagation characteristics caused by minute defects in copper wires, providing reliable data support for subsequent quantitative defect assessment and type identification, and significantly improving detection accuracy and anti-interference capability.

[0041] In one possible implementation, the copper wire defect detection method also includes: S10 acquires the instantaneous phase difference and temperature drift signal of three spatial points on the surface of the copper wire by using dual wavelengths. The dual wavelengths include a 633nm wavelength and a 785nm wavelength. The 633nm wavelength is used to measure the surface displacement caused by the surface acoustic wave, and the 785nm wavelength is used to measure the thermal deformation of the copper wire. The three spatial points include the center point and two points symmetrically distributed around the center point.

[0042] For example, a dual-frequency laser interferometer can be used, configured with two laser probe beams of different wavelengths. The laser probe beams can employ dual wavelengths: 633nm (helium-neon laser) and 785nm (semiconductor laser). The 633nm wavelength is used to measure nanoscale surface vibrations or displacements caused by surface acoustic waves; the 785nm wavelength is used to measure the slow structural deformation of copper wires caused by thermal expansion. The two laser beams are transmitted coaxially through an optical coupling device or a wavelength division multiplexer (such as a dichroic mirror), and then combined and interfered by a confocal interferometer (such as a white light confocal or dual-wavelength confocal structure), ensuring that the two wavelength beams are focused at the same position on the probe surface, forming a unified interference signal source.

[0043] Three measurement points can be set on the copper wire surface. The center point is the main reference point, which can be the area directly opposite the surface acoustic wave excitation source, and is numbered X0. The other two points (X1 and X2) are symmetrically distributed around X0. X1 can be located to the left of X0 at a distance of -Δd (e.g., -1mm) and is used to measure the wave propagation delay and initial amplitude. X2 can be located to the right of X0 at a distance of +Δd (e.g., +1mm) and is used to measure the post-propagation state. Being symmetrical with X1, it facilitates fitting. The spatial arrangement of the three points is beneficial for identifying the wavefront propagation direction, symmetry perturbations, and defect interference modes.

[0044] An acousto-optic deflector (AOD) can be set in the laser output channel to precisely adjust the exit angle of the laser beam. The control system can provide real-time feedback on minute displacements of the copper wire (such as vibration or wire slippage) and drive the AOD to automatically fine-tune the angle, so that the laser is always focused on three points in space (tracking accuracy controlled within ±2μm). This is beneficial for the laser to consistently and stably acquire data at the correct position even when the copper wire vibrates or drifts slightly.

[0045] High-speed sampling of the 633nm interference fringes at each point can be performed to obtain the interference signal that changes over time. Phase demodulation algorithms (such as Hilbert transform, Fourier transform, or interference fringe envelope analysis) can be used to extract the instantaneous phase difference of each point from the 633nm interference fringes. That is, the phase difference between the reflected light and the reference light at each point, which represents the instantaneous surface displacement caused by the surface acoustic wave at the corresponding point.

[0046] Similarly, the interference phase signal at a wavelength of 785 nm corresponding to each point is extracted. Low-pass filtering or time-moving averaging is then applied to the 785 nm phase signal to remove the surface acoustic wave component while preserving the thermal deformation trend, resulting in the temperature drift phase curve of each point as a function of temperature, i.e., the temperature drift signal. The temperature drift signal can be used in subsequent analysis to compensate for the thermal effects in the 633 nm phase difference and to extract surface acoustic wave phase anomalies purely caused by defects.

[0047] This step involves simultaneously acquiring the instantaneous displacement phase difference (633nm) caused by surface acoustic waves and the thermal deformation phase signal (785nm) caused by temperature changes at three points on the copper wire surface using dual-wavelength laser interference. Combined with confocal interferometry synthesis and high-precision dynamic tracking by an acousto-optic deflector, this achieves high-resolution, spatially distributed, temperature-compensated, and separable non-contact detection of the response to microstructures on the copper wire surface, providing key technical support for accurately extracting defect features and improving detection stability.

[0048] In one possible implementation, the process includes, before acquiring the instantaneous phase difference and temperature drift signal of the surface acoustic wave at the copper wire surface, the following steps: Traditional fixed-period gratings require changing to match the diameter or surface characteristics of copper wires of different specifications when inspecting them. Each changeover necessitates equipment downtime and alignment operations, leading to production interruptions and reduced efficiency. Furthermore, during actual inspection, thermal expansion of the copper wire due to ambient temperature changes causes frequency drift in the surface acoustic wave (SAW). The fixed grating cannot adapt in real time, easily resulting in detuning between the SAW and defect response, leading to missed or false detections. When inspecting fine copper wires, the fixed grating period is limited by the structural design, preventing the excitation of the required high-frequency SAW (e.g., >1GHz), resulting in insufficient detection resolution and inability to identify submicron-level defects.

[0049] To overcome the aforementioned problems, this application designs an adjustable periodic grating structure based on electrostrictive materials (such as PMN-PT crystals). This structure integrates a regularly arranged array of gold nanopillars on its surface, forming a micro / nano grating. When a voltage is applied, the electrostrictive substrate deforms, causing a linear change in the spacing between the nanopillars, thus achieving dynamic adjustment of the grating period. Through electronic control, period adjustment can be completed within 10ms, enabling rapid switching between different copper wire specifications without requiring device replacement or zero downtime. Simultaneously, the adjustable periodic grating structure can integrate a temperature monitoring module to sense changes in the thermal expansion of the copper wire in real time and adjust the applied voltage accordingly, allowing the grating period to dynamically follow the SAW frequency changes, achieving temperature drift compensation and effectively avoiding mistuning and false detections. For micro-copper wires, the grating period can be reduced to the nanometer scale, exciting surface acoustic waves with frequencies exceeding 1 GHz, improving detection resolution to the hundred-nanometer scale, thereby achieving accurate identification of extremely small defects (<100 nm).

[0050] Therefore, constructing an adjustable periodic grating not only solves the problems of poor adaptability to multiple specifications, large thermal drift impact, and limited resolution of fixed gratings, but also significantly improves the flexibility, stability, and ultra-high resolution capability of the detection system.

[0051] S101, obtain the diameter data and surface temperature of the copper wire.

[0052] For example, a non-contact optical measurement method can be used to measure the diameter of copper wire using a high-precision laser diameter gauge. The laser diameter gauge is based on the laser shading method or laser triangulation principle, achieving sub-micron resolution. One or more dual-axis laser diameter gauges can be deployed on the production line, forming vertically intersecting laser beams. When the copper wire passes through the measurement area, it blocks the laser beam. The width of the blockage is recorded in real time, converted into an electrical signal, and then converted into a diameter value. Averaging or filtering algorithms are used to suppress jitter, outputting a stable and continuous diameter data stream, which is the diameter of the copper wire. The diameter sampling frequency can be ≥1kHz, and the measurement accuracy can reach ±0.5μm.

[0053] The surface temperature of copper wire can be measured using non-contact infrared thermometry, which converts the intensity of infrared radiation emitted from the copper wire surface into a surface temperature value. An infrared fiber optic thermometer or infrared thermal imager can be positioned directly over the copper wire surface, selecting an appropriate wavelength (e.g., mid-wave 3~5μm) to accommodate the high reflectivity of the metal surface. If the copper wire moves at a high speed, an infrared sensor with autofocus and high-speed response can be used. Infrared radiation signals can be acquired in real time, and the actual surface temperature can be calculated using an emissivity correction model. If there is significant environmental interference, multi-point temperature measurement and calibration algorithms can be employed to improve measurement stability. The temperature sampling frequency can be ≥100Hz, and the measurement accuracy can reach ±1°C (after surface emissivity compensation).

[0054] The diameter data and surface temperature can be synchronized by timestamp to ensure that they correspond to the same copper line segment. The diameter and surface temperature data pairs at each time point can be cached and analyzed for subsequent calculation of surface acoustic wave parameters.

[0055] This step achieves real-time dynamic acquisition of the copper wire diameter and temperature through high-precision, non-contact measurement methods, providing reliable basic data for subsequent surface acoustic wave frequency setting and grating period adjustment.

[0056] S102, determine the operating frequency of surface acoustic waves based on diameter data.

[0057] For example, a functional relationship between the diameter of the copper wire and the optimal operating frequency of the surface acoustic wave can be established through a large amount of experimental data or finite element simulation. , This indicates the operating frequency of surface acoustic waves. This represents a function of the copper wire diameter. Thicker copper wires can be used to match lower frequencies to avoid instability in the excitation mode or waveguide failure; thinner copper wires can excite higher-frequency SAWs to improve resolution and excite short-wavelength standing waves. For example, the functional relationship could be... , where the unit is Hz or GHz, D represents the diameter of the copper wire in mm or μm, and a and b represent empirical coefficients obtained by fitting.

[0058] The copper wire diameter measured in the previous step can be substituted into the functional relationship to calculate the operating frequency of the surface acoustic wave that matches the diameter. This operating frequency can be used to set the periodic modulation parameters, grating period, synchronous sampling frequency, etc. of the laser excitation system.

[0059] A rule engine can be built based on extensive experimental data or finite element simulations. The engine can then be used to find the corresponding operating frequency of the surface acoustic wave based on the copper wire diameter. For example, the rule engine can... =50MHz, D>200μm; =200MHz, 100μm <D≤200μm; =800MHz, 50μm <D≤100μm; >1GHz, D≤50μm.

[0060] This step enables adaptive matching of surface acoustic wave frequency to copper wire size, avoiding false excitation or standing wave formation failure, and preventing detection blind spots or low efficiency caused by improper frequency setting. It provides a foundation for subsequent steps such as dynamic adjustment of grating period, wave velocity matching, and defect response enhancement.

[0061] S103, the ideal sound velocity is corrected according to the surface temperature to obtain the corrected sound velocity. The ideal sound velocity is used to characterize the ideal propagation speed of surface acoustic waves on the copper wire surface.

[0062] It is understandable that the propagation speed of surface acoustic waves (SAWs) on the surface of copper wire is affected by the elastic properties of the material, which are significantly dependent on temperature. When the surface temperature of the copper wire increases, the elastic modulus of copper decreases, leading to a decrease in the sound speed. Therefore, to ensure the accuracy of the operating frequency setting and wavelength calculation of SAWs, the ideal sound speed defined at the standard temperature can be corrected for temperature to obtain the actual propagation sound speed at the current temperature.

[0063] The ideal speed of sound is the reference propagation speed of surface acoustic waves on the surface of a copper wire at a standard temperature (e.g., 25°C) (e.g., ≈3000m / s).

[0064] For example, the temperature difference between the surface temperature and the standard temperature can be calculated. Based on this temperature difference and a temperature correction factor, the ideal sound speed is corrected using a linear approximation model to obtain the actual sound speed (corrected sound speed). The temperature correction factor is a temperature-sensitive coefficient for the sound speed, with units of... A negative value indicates that the speed of sound decreases as temperature increases; the temperature correction factor for copper is... A linear approximation model can be... ,in, This indicates a correction for the speed of sound. Represents the ideal speed of sound. This represents the temperature correction factor. Indicates surface temperature. Indicates the standard temperature.

[0065] This step effectively compensates for the influence of temperature on the propagation speed of surface acoustic waves, improving the accuracy and robustness of frequency setting, defect identification, and grating control under different working environments.

[0066] S104, calculate the target grating period based on the operating frequency and the corrected sound velocity. The grating is used to control the wavelength of the surface acoustic wave, and the target grating period is equal to the wavelength of the surface acoustic wave.

[0067] It is understandable that the grating structure acts as a spatial modulator in the excitation of surface acoustic waves (SAWs). The grating period determines the excitation wavelength, which in turn determines whether the frequency of the excited SAW can resonate with the wavelength. To ensure excitation efficiency and standing wave stability, the grating period must be strictly equal to the wavelength of the SAW.

[0068] For example, based on the fundamental propagation relationship of surface acoustic waves (SAWs), the wavelength of a SAW is the ratio of the corrected velocity of sound to the operating frequency. Since the grating period must be equal to the wavelength of the excited SAW, the target grating period is the ratio of the corrected velocity of sound to the operating frequency, i.e. Where P represents the target grating period. This represents the wavelength of surface acoustic waves.

[0069] This step ensures precise matching between the grating period and the surface acoustic wave wavelength, which is a key step in achieving high-efficiency and high-stability SAW excitation and directly affects the defect detection sensitivity and frequency response consistency.

[0070] S105, determine the driving voltage based on the target grating period. The driving voltage is used to adjust the grating period to the target grating period.

[0071] For example, the grating period has an approximately linear relationship with the driving voltage, i.e. ,in, The initial grating period is represented when there is no voltage, and k represents the voltage response coefficient of the grating, with units of μm / V, which can be obtained through device calibration. This represents the driving voltage. The driving voltage can be calculated by inversely solving this linear relationship, i.e. Real-time displacement measurement or optical feedback systems can be used to verify whether the actual grating period has reached the set value, and fine-tuning closed-loop control can be performed (e.g., by judging through changes in optical diffraction peak positions).

[0072] This step achieves a crucial closed loop that transforms detection conditions (frequency, temperature) into control commands for optical devices, ensuring that surface acoustic wave excitation is always under optimal resonance conditions, thereby improving detection sensitivity and system response speed.

[0073] In one possible implementation, the copper wire defect detection method also includes: Besides determining the operating frequency of surface acoustic waves (SAWs) directly by the diameter of the copper wire, the operating frequency can also be determined by the resolution requirement (the minimum defect depth to be detected). By simultaneously constraining the operating frequency of SAWs by both the copper wire diameter and the minimum defect depth to be detected, the adaptability of the copper wire structure and the defect detection capability are taken into account. This ensures that SAWs can propagate stably while also possessing the required penetration depth and resolution.

[0074] S1001, determine the first frequency of the surface acoustic wave based on the diameter data.

[0075] For example, the implementation method of this step is the same as that of step S102, and will not be described again here.

[0076] This step ensures that surface acoustic waves can propagate stably on structures of this size without mode distortion or excitation failure.

[0077] S1002, determine the second frequency of the surface acoustic wave based on the preset defect detection depth.

[0078] It is understandable that although surface acoustic waves (SAWs) primarily propagate along the material surface, their energy also has a limited penetration depth below the surface. This penetration depth determines the sensitivity range of SAWs to internal or near-surface defects in copper wires. To ensure effective identification of target defects, a suitable second frequency can be selected based on a pre-set defect detection depth (the minimum defect depth to be detected), allowing the SAWs to have the required energy distribution range.

[0079] For example, the effective penetration depth of surface acoustic waves (SAWs) is directly proportional to their wavelength. Higher frequency SAWs have shorter wavelengths and shallower penetration depths; lower frequency SAWs have longer wavelengths and deeper penetration depths. Where h represents the effective penetration depth of surface acoustic waves (the pre-set defect detection depth), and c is an empirical coefficient that can be adjusted based on system sensitivity and experience, and can be taken from 0.5 to 1.0, indicating that the energy is mainly distributed at 0.5. ~1 Within the range.

[0080] From step S104, it can be determined that the wavelength of the surface acoustic wave is the ratio of the corrected sound velocity to the operating frequency. Based on the preset defect detection depth and the wavelength of the surface acoustic wave, the second frequency of the surface wave is calculated using the following formula: ,in, Indicates the second frequency.

[0081] This step ensures that the surface acoustic waves have sufficient penetration depth to effectively identify target defects.

[0082] S1003, determine the operating frequency of the surface acoustic wave based on the first frequency and the second frequency.

[0083] For example, when the first frequency and the second frequency are similar (e.g., | - |≤Δf, where When Δf represents the first frequency and Δf is the set tolerance (e.g., 10%), the average of the two can be directly taken. This method balances structural adaptability and penetration performance and is suitable for general detection conditions.

[0084] If the second frequency is greater than the first frequency and the frequency difference is large, it indicates that a higher frequency may be needed to meet the penetration depth requirements. However, if the high frequency exceeds the structure's propagation capability (too small a diameter), it may lead to a decrease in surface acoustic wave excitation efficiency or mode instability. To ensure successful excitation, the highest frequency within the structure's support range can be selected. ,in, This indicates the highest frequency range that the structure can support.

[0085] The operating frequency of surface acoustic waves can be calculated using a weighted average method. ,in, Priority weights If more emphasis is placed on the stability of transmission, A value of 0.7 to 0.9 can be used; if more emphasis is placed on defect penetration capability, You can take a value of 0.3 to 0.5.

[0086] This step achieves a dynamic balance between structural adaptability and detection penetration capability, which is beneficial for surface acoustic waves to be stably excited while also having the ability to detect target defects.

[0087] S200 determines the defect sensitivity parameters based on instantaneous phase difference and temperature drift signals. These parameters characterize the changes in surface acoustic wave propagation characteristics caused by defects in the copper wire.

[0088] For example, several regions to be analyzed can be divided on the surface of the copper wire, and the instantaneous phase difference curve corresponding to each region can be calibrated, and the corresponding temperature drift signal can be extracted for subsequent correction. Each region to be analyzed (such as an equally spaced sliding window) is divided in the phase difference curve, and the phase difference curve segment extracted from each region to be analyzed is the instantaneous phase difference curve corresponding to each region to be analyzed.

[0089] By utilizing the temperature drift signal corresponding to each region to be analyzed, temperature compensation processing can be performed on the instantaneous phase difference curve of each region to eliminate the phase influence caused by temperature changes, thus obtaining the net phase difference curve caused by structural disturbance. Based on the net phase difference curves of all regions to be analyzed, regions with abrupt changes, peaks, or steep slopes in the net phase difference curves can be identified. These regions are areas where surface acoustic waves are interfered with by defects during propagation, i.e., abnormal regions.

[0090] The phase abrupt change amplitude can be extracted from the net phase difference curve of each anomalous region. This amplitude, representing the maximum abrupt change value in the net phase curve, characterizes the direct impact of the defect on propagation. The phase change rate for each anomalous region can be calculated by differentiating the net phase difference curve for that region. This rate, represented by the average phase gradient, characterizes the degree of phase perturbation caused by the defect. ,in, Indicates the average phase gradient. Represents the net phase difference curve. This represents the spatial coordinates of the copper wire surface along the direction of surface acoustic wave propagation. The width of each anomalous region, i.e., its spatial length, can be calculated based on its spatial coordinates in the phase difference curve, indirectly reflecting the defect scale. The temperature drift signals of anomalous and normal regions can be compared to calculate the abnormal temperature drift between them, thus determining the degree of abnormality in the impact of structural integrity on temperature.

[0091] This step extracts the physical quantity most sensitive to the defect response by accurately measuring the phase change behavior, thereby achieving quantitative characterization and classification of the defect's impact and providing a foundation for subsequent defect depth estimation and type determination.

[0092] In one possible implementation, S200, based on the instantaneous phase difference and temperature drift signal, determines the defect-sensitive parameters, including: S210: Based on the instantaneous phase difference and temperature drift signal of the three points in space, calculate the corrected phase of each point, and calculate the surface displacement of each point based on the corrected phase of each point.

[0093] It can be understood that the instantaneous phase difference (i.e., the instantaneous phase difference curve) is obtained by demodulating the interference signal, representing the total phase change caused by the surface acoustic wave at that point; the temperature drift signal represents the change in optical path length caused by thermal expansion, which originates from the second wavelength (e.g., 785nm) channel.

[0094] For example, surface acoustic waves (SAWs) are affected by thermal expansion due to temperature changes during propagation, resulting in phase drift. Therefore, to obtain a true mechanical response, the portion caused by thermal expansion can be removed from the total phase. The correction formula is as follows: ,in, Indicates the corrected phase curve. Represents the instantaneous phase difference curve. This represents the temperature drift signal. Based on the correction formula, the corrected phase at the three spatial points is calculated respectively. Corrected phase curve , Corrected phase curve , Corrected phase curve ).

[0095] There is a fixed linear relationship between the corrected phase and the perpendicular displacement of the copper wire surface. The surface displacement curve of each point with respect to time can be calculated using the interferometric measurement principle. The formula for calculating the surface displacement is as follows: ,in, This represents the surface displacement curve. Based on the surface displacement calculation formula, calculate the surface displacement of three points in space respectively. Surface displacement curve , Surface displacement curve , Surface displacement curve This is used for subsequent calculations of particle velocity, propagation characteristic analysis, or defect identification.

[0096] This step effectively eliminates the interference of temperature drift on phase measurement, which is beneficial for subsequent displacement-based velocity extraction and wave velocity fitting to achieve high accuracy and physical consistency.

[0097] S220, based on the derivative of the surface displacement of each point with respect to time, obtains the particle velocity of each point.

[0098] It is understandable that during the propagation of surface acoustic waves (SAWs), the particles on the surface of the copper wire undergo periodic vibrations with the wave, and the instantaneous velocity is an important dynamic parameter describing the wave energy and propagation behavior. By differentiating the surface displacement signal over time, the corresponding particle velocity can be obtained, providing fundamental dynamic characteristics for subsequent sound velocity fitting, waveform recognition, and defect detection.

[0099] For example, the particle velocity is the rate of change of displacement of a point on the surface of the copper wire per unit time during its motion over time, that is, the derivative of the surface displacement with respect to time. ,in, Indicates the first The velocity of a point mass. Since real signals are discretely sampled, the time derivative can be approximated using numerical methods. For discrete-time displacement data (surface displacement curves), the velocity can be estimated using the central difference method, i.e. ,in, Indicates time The velocity of the particle below, In time The surface displacement below, In time The surface displacement below, Indicates time With time The time interval between them.

[0100] For high-frequency signals or waveforms with a lot of noise, a smoothing differential filter can be used for differential calculation. This can smooth the noise while preserving the main signal waveform, improving the stability and accuracy of speed estimation.

[0101] The velocities of the three particles in space are calculated using the method described above. particle velocity , particle velocity , particle velocity ).

[0102] This step transforms the displacement characteristics of surface acoustic waves into dynamic velocity characteristics, laying the physical foundation for subsequent descriptions of the propagation function and energy behavior of surface acoustic waves.

[0103] S230, fits the surface acoustic wave propagation function according to the particle velocity at each point, and extracts the actual sound velocity from the surface acoustic wave propagation function.

[0104] For example, when a surface acoustic wave propagates on the surface of a copper wire, the change in particle velocity with time and spatial position can be described by the surface acoustic wave function, which is: ,in, This represents the velocity of a particle at a point in space and time. Indicates the initial amplitude. Indicates the spatial attenuation coefficient. Wave number, in rad / m. , Indicates spatial location ( , , ), This indicates the initial phase offset.

[0105] We can subtract the particle velocities at three points in space from the surface acoustic wave propagation function, and define the fitting error (objective function). Objective function: The fitting parameters include , , , A nonlinear least squares fitting algorithm (such as Levenberg-Marquardt) can be used to fit the objective function onto the dataset (particle velocity, spatial position, and operating frequency of the surface acoustic wave for each point), find the set of parameters corresponding to the minimum error, and obtain the optimal wavenumber after fitting. The actual sound velocity can be calculated based on the optimal wave velocity obtained from the fitting, combined with the operating frequency of the surface acoustic wave. ,in, This represents the actual speed of sound.

[0106] This step obtains the local propagation velocity characteristics of the copper wire by fitting the actual propagation behavior of surface acoustic waves, providing a key basis for determining whether there are wave velocity anomalies or energy scattering on the surface of the copper wire.

[0107] S240 calculates the wave velocity offset based on the actual sound velocity and the corrected sound velocity. Among them, the defect-sensitive parameters include the wave velocity offset, the resonance frequency shift, and the attenuation coefficient.

[0108] For example, the wave speed offset can be obtained by subtracting the corrected sound speed from the actual sound speed.

[0109] These steps enable high-precision dynamic characterization of surface acoustic wave propagation behavior, achieving technical effects such as high sensitivity to wave velocity changes caused by minute defects, strong spatial resolution, and good temperature robustness.

[0110] In another possible implementation, S200 determines defect-sensitive parameters based on instantaneous phase difference and temperature drift signals, including: S201, perform a Fourier transform on the surface displacement of the center point to obtain the spectrum of the center point.

[0111] It is understandable that surface acoustic waves cause periodic vibrations on the surface of the copper wire during propagation, which manifest as an oscillating displacement signal in the time domain. To analyze the frequency composition of the vibration, the time signal can be converted into a frequency domain representation.

[0112] For example, the center point can be obtained using the Fast Fourier Transform (FFT) algorithm. Displacement-time signal (surface displacement curve) Converting it to a frequency domain signal, i.e. ,in, Represents a complex sequence in the frequency domain. The frequency is represented by the total time of the surface displacement curve at the center point. , =0,1,2,...,N / 2, because FFT is symmetric, only the first half of the frequency components are retained. For each frequency... Calculate the corresponding spectral amplitude, i.e. ,in, Represents frequency The corresponding spectral amplitude. A graph can be plotted by pairing the frequency with its corresponding spectral amplitude, resulting in a spectrum diagram of the center point, with the horizontal axis representing frequency (Hz) and the vertical axis representing amplitude (μm). The spectrum diagram shows the dominant frequency (resonance frequency) with the strongest response at the center point and its frequency distribution characteristics.

[0113] This step enables the conversion from the time domain to the frequency domain, allowing identification of the dominant response frequency of the surface acoustic wave at the center point, providing a frequency basis for subsequent extraction of the resonance frequency and determination of frequency shift.

[0114] S202, extract the frequency corresponding to the maximum amplitude in the spectrum of the center point to obtain the actual resonant frequency.

[0115] For example, the frequency component corresponding to the maximum spectral amplitude can be searched in the spectrum diagram. This frequency is the actual resonant frequency of the center point, reflecting the main response frequency of the surface acoustic wave of the current copper wire at that point.

[0116] S203, calculate the resonance frequency shift based on the actual resonance frequency and the reference resonance frequency. The reference resonance frequency is the resonance frequency corresponding to a defect-free, calibrated sample copper wire.

[0117] For example, the difference between the actual resonant frequency and the reference resonant frequency can be calculated as the resonant frequency shift, which is one of the sensitive parameters for measuring the influence of factors such as changes in copper wire structure, local defects, or stress disturbances on the resonant behavior of surface acoustic waves.

[0118] The above steps enable highly sensitive identification of changes in the local structural state of copper wires. The resonant frequency shift, as a defect-sensitive parameter, can be used to determine the existence and type of defects, and has technical advantages such as non-contact, high resolution, high frequency accuracy, and strong comparability.

[0119] In the third possible implementation, S200, based on the instantaneous phase difference and temperature drift signal, determines the defect-sensitive parameters, including: S2001, perform Hilbert transform on the surface displacements of two points symmetrically distributed around the center point to obtain the envelope amplitude of the two points symmetrically distributed around the center point.

[0120] It is understandable that surface acoustic waves (SAWs) cause vibrations in the particles on the surface of the copper wire during propagation, manifesting as periodic displacement signals. To analyze the energy changes of the wave during propagation, the envelope amplitude of the wave can be extracted, that is, the trajectory of the maximum vibration trend at each measuring point throughout the entire vibration process.

[0121] For example, two points symmetrically distributed around the center point ( and Perform Hilbert transform on the surface displacement curves of the samples respectively to obtain... and The complex analytic signal consists of the original signal and its 90° phase offset signal, which together form a complex signal that can describe the local envelope.

[0122] They can be calculated separately. and The magnitude (amplitude) of the complex analytic signal is used to obtain two envelope curves that vary with time, which are the... and The envelope amplitude. and The envelope curve depicts the changing trend of local vibration intensity at each point.

[0123] This step allows for the accurate capture of vibration intensity changes in surface acoustic waves along their propagation path, laying the foundation for further analysis of energy changes caused by attenuation, symmetry disturbances, and defects.

[0124] S2002, extract the peak amplitudes of the two points symmetrically distributed around the center point from the envelope amplitudes of the two points.

[0125] For example, it is possible to obtain from and The maximum value is extracted from the envelope amplitude (envelope curve) as... and The peak amplitude.

[0126] S2003, calculate the attenuation coefficient based on the peak amplitude of two points symmetrically distributed around the center point and the distance between the two points symmetrically distributed around the center point.

[0127] For example, the amplitude of surface acoustic waves decreases exponentially with propagation distance, i.e., the theoretical model is as follows: ,in, This indicates that the surface acoustic wave is at a distance of from the excitation point. The amplitude of the fluctuation envelope at the location, Indicates the location of the surface acoustic wave at the excitation source ( The theoretical maximum amplitude of (=0), This represents the attenuation coefficient. As can be seen from step S10... The distance from the center point is -Δd. If the distance from the center point is +Δd, then The envelope amplitude can be expressed as , The envelope amplitude can be expressed as According to the envelope amplitude calculation formula , and and From the peak amplitude, the attenuation coefficient can be calculated, i.e. The unit is Np / m (Nepal per meter), which represents the energy decay rate of surface acoustic waves per unit distance.

[0128] This step enables the quantitative extraction of surface acoustic wave energy attenuation characteristics. The obtained attenuation coefficient is an important defect-sensitive parameter that characterizes the dissipation behavior of defects or structures, and helps to identify local attenuation anomalies in copper wires or determine the type of defect.

[0129] S300 calculates the equivalent depth of defects in the copper wire based on defect sensitivity parameters, and determines the defect type of the copper wire based on the defect sensitivity parameters. The equivalent depth of defects is the average depth of all defects.

[0130] For example, a functional model relating defect-sensitive parameters to defect depth can be established through experimental calibration or numerical simulation. ,in, Indicates the first The local defect depth corresponding to each abnormal region. It can be an empirical model such as linear regression, multinomial fitting, or neural networks. Indicates the first The amplitude of phase abrupt changes in each anomalous region Indicates the first The phase change rate of each abnormal region Indicates the first The width of each abnormal region Indicates the first The abnormal temperature drift of each abnormal region can be calculated. The defect sensitivity parameter of each abnormal region can be substituted into the function model to calculate the defect depth of the corresponding abnormal region. The arithmetic mean of the defect depths of all detected abnormal regions can be taken to obtain the equivalent defect depth of the copper wire.

[0131] Based on existing experimental data, simulation, or historical testing experience, typical characteristic patterns of defect types on defect-sensitive parameters can be established (i.e., defect type feature library). The defect type feature library includes each defect type and the corresponding parameter characteristics (phase change amplitude, phase change rate, width, and temperature drift anomaly).

[0132] The phase abruptness amplitude, phase change rate, width, and temperature drift anomaly of each abnormal region are used to construct a defect-sensitive parameter vector. This vector can be matched with the parameter features of each defect type in a defect type feature library, calculating the distance or similarity (e.g., Euclidean distance) between them. Existing defect data can be used to train a multi-classifier (e.g., support vector machine, decision tree, neural network). The defect-sensitive parameter vector is then input into the model, automatically outputting the corresponding defect type label. Based on this method, the defect type corresponding to each abnormal region can be obtained. If multiple adjacent regions belong to the same type, they can be merged and classified to improve judgment stability. Finally, the location distribution, type, and proportion of different types of defects in the entire copper wire are output.

[0133] This step achieves a quantitative to qualitative transformation from local physical response to overall defect characteristics, truly converting the previously obtained signal data into a basis for structural evaluation. This makes the detection results more engineering interpretable and applicable, and has high practicality and automation potential. In one possible implementation, please refer to Figure 2 In step S300, the equivalent depth of defects in the copper wire is calculated based on the defect sensitivity parameter, including: S301, calculate the relative offset of wave velocity based on the wave velocity offset and the corrected sound velocity.

[0134] For example, the ratio between the wave velocity offset and the corrected sound velocity can be calculated, which is the relative wave velocity offset. It represents the proportion of change relative to the ideal sound velocity and is an intensity index reflecting the influence of defects on the propagation characteristics of surface acoustic waves.

[0135] The reference sound velocity (corrected sound velocity) of copper wires of different specifications may be different (such as thick wire versus thin wire, temperature changes, etc.). The same wave velocity offset has different meanings of relative change under different reference sound velocity backgrounds. Therefore, in order to compare the intensity of the impact of defects under different conditions, the proportional change (relative wave velocity offset) can be used to make the evaluation results universal and comparable.

[0136] The relative offset of wave velocity can more directly reflect the change in the propagation speed of surface acoustic waves relative to the ideal state. For the same type of defect, if they exhibit the same wave velocity offset under different sound velocity backgrounds (such as high temperature and normal temperature), their structural influence intensity is consistent. Using relative offset is more in line with the physical meaning of defect equivalence.

[0137] In the weighted linear model, the three parameters (relative wave velocity offset, resonant frequency shift, and attenuation coefficient) come from different physical domains. Only by unifying them into relative or dimensionless quantities can they be reasonably weighted and combined. If the wave velocity offset is used directly, the numerical dimensions are large and dependent on units, which may lead to an imbalance in the weighting ratio and affect the stability of the results.

[0138] S302, calculate the equivalent depth of defects in copper wire based on the relative offset of wave velocity, the resonant frequency shift, and the attenuation coefficient.

[0139] For example, the equivalent depth of defects in copper wire can be calculated using a weighted linear model based on the relative wave velocity shift, resonant frequency shift, and attenuation coefficient. Weighted linear model: ,in, Indicates the equivalent depth of the defect. Indicates the wave velocity offset. This indicates the relative offset of the wave velocity. Indicates the resonant frequency shift. , , These are empirical or calibration coefficients. The weighted linear model can be determined through experimental calibration or data fitting and is suitable for evaluating the equivalent depth of various defect types.

[0140] The above steps realize the quantitative conversion from surface acoustic wave signal characteristics to defect structural parameters, and have the technical advantages of non-contact, quantitative, and rapid estimation of defect depth, which is suitable for automated copper wire quality inspection systems.

[0141] In one possible implementation, please refer to Figure 2 In step S300, the defect type of the copper wire is determined based on the defect sensitivity parameter, including: S310 combines wave velocity offset, resonance frequency shift, and attenuation coefficient into a defect feature vector.

[0142] For example, wave velocity shift, resonant frequency shift, and attenuation coefficient can be used as feature dimensions to form a defect feature vector. =[ , , ].

[0143] S320: Based on the defect feature vector, the most similar defect combinations are selected from the defect fingerprint database to obtain a candidate defect set. The defect fingerprint database includes various defect type combinations and the corresponding defect feature threshold for each defect type combination.

[0144] For example, a defect fingerprint database can be pre-established, which may include multiple typical defect type combinations and the corresponding defect feature thresholds (i.e., feature vector ranges) for each defect combination, to describe the typical response range of that type of defect combination. See the table below: For each defect combination in the defect fingerprint database, the median value of its threshold interval can be calculated as the central feature vector of that defect combination. Euclidean distance (or other similarity metrics) can be used to calculate the distance between the defect feature vector and the central feature vector of each defect combination. All calculated distances are sorted from smallest to largest, and the defect combination corresponding to the minimum value is the defect type combination most similar to the existing defects in the current copper wire. A distance threshold can be set to filter defect combinations across all distances, forming the final candidate defect set.

[0145] If there is no defect feature threshold that completely matches the defect feature vector for each defect combination, the defect combination that completely matches one or two of the three parameters in the defect feature vector (one or two parameters in the defect feature vector are completely in the defect feature threshold of some defect combinations) can be selected as the closest defect combination.

[0146] This step completes the initial screening and classification of defects, laying the foundation for more accurate identification and analysis in the future.

[0147] S330, if there is only one defect combination in the candidate defect set and the defect feature vector completely matches the defect feature threshold corresponding to the candidate defect set, the defect type in the candidate defect set is determined as the defect type of copper wire.

[0148] For example, if there is only one defect combination in the candidate defect set, and all three parameters in the defect feature vector are within the threshold of all defect features of that combination, the defect combination can be directly determined to be a defect type of copper wire.

[0149] S340, if there are multiple defect combinations in the candidate defect set, calculate the weights of each type of defect in the candidate defect set based on the defect feature vectors and the defect response matrix, and determine the defect type of the copper wire based on the weights of each type of defect in the candidate defect set. The defect response matrix is ​​constructed using the response feature vectors corresponding to each type of defect in the candidate defect set, and the response feature vectors include wave velocity shift, resonant frequency shift, and attenuation coefficient.

[0150] For example, a response feature vector corresponding to each defect type can be pre-constructed. =[ , , (Response Data Table), where Indicates the first Response feature vectors for various defect types Indicates the first Wave velocity offset corresponding to each defect type Indicates the first The resonant frequency shift corresponding to each defect type Indicates the first The attenuation coefficients corresponding to different defect types, such as the response feature vector of a crack, are [ , , The response feature vector of oxidation is [] , , The response feature vector of the hole is [] , , ].

[0151] The response feature vectors (candidate response feature vectors) corresponding to all defect types in the candidate defect set can be filtered from the response data table above and stacked row by row to form a defect response matrix. For example, if the candidate defect set contains two defect combinations: cracks and oxidation, and cracks and voids, then the defect response matrix could be... .

[0152] The defect feature vector can be represented as a weighted linear combination of the candidate response feature vectors, i.e. ,in, Represents the defect response matrix. This represents the proportion or similarity weight of each defect type in the candidate defect set within the current response. Due to the equation... It is a small linear system, possibly an underdetermined or overdetermined system of equations. The least squares method can be used to solve the equations to obtain the weight of each defect type in the candidate defect set. A weight threshold can be set, and the weight of each defect type in the candidate defect set can be compared with the weight threshold. Defect types that exceed the weight threshold are taken as the defect types of the copper wire. The defect type with the largest weight can be selected as the primary defect type of the copper wire, and the others are secondary defect types.

[0153] This method enables intelligent defect identification and classification based on multi-dimensional physical features, taking into account both precise matching and fuzzy judgment capabilities, and possesses high adaptability, scalability and engineering applicability.

[0154] Optionally, please refer to Figure 2 Copper wire defect detection methods also include: S3401 If there is only one defect combination in the candidate defect set but the defect feature vector does not completely match the defect feature threshold corresponding to the candidate defect set, determine the conflict parameter based on the defect feature vector and the defect feature threshold corresponding to the candidate defect set, and determine the supplementary defect type based on the conflict parameter.

[0155] For example, each parameter in the defect feature vector can be compared with the defect feature threshold corresponding to the candidate defect set to determine whether the defect feature threshold is met, and parameters that do not meet the threshold are identified as conflict parameters. Alternatively, the defect fingerprint database can be used to find which defect type combinations have defect feature thresholds with conflict parameter value ranges similar to those of the conflict parameters, and these similar defect type combinations can be used as supplementary defect types.

[0156] For example, if the defect feature vector is [-6, 45, 0.5], and the defect combination in the candidate defect set is cracks and voids, querying the defect fingerprint database reveals that the defect feature threshold for this defect combination is... <−8m / s, >40MHz, >0.4dB / mm, comparing each parameter in the defect feature vector with the corresponding value, it can be determined that there is a conflict in the wave velocity offset in the defect feature vector. The defect type combination corresponding to the defect feature threshold with a wave velocity offset value close to the value in the defect fingerprint database is scratch and dent (the wave velocity offset threshold is -6 <). <-3), scratches and dents are considered as supplementary defect types.

[0157] S3402, extract the response feature vector corresponding to each defect type in the candidate defect set and the response feature vector corresponding to the supplementary defect types from the response data table. The response data table includes the response feature vector corresponding to each defect type.

[0158] For example, similar to step S340, the response feature vector corresponding to each defect type in the candidate defect set and the response feature vector corresponding to the supplementary defect types can be retrieved from the response data table. For example, the response feature vector of the crack in the candidate defect set is [ , , The response feature vector of the hole is [] , , The response feature vector for scratches in the supplementary defect type is [ ]. , , The response feature vector of the depression is [ , , ].

[0159] S3403. Based on the response feature vectors corresponding to each defect type in the candidate defect set and the response feature vectors corresponding to the supplementary defect types, construct a defect response matrix. Based on the defect response matrix and defect feature vectors, use the non-negative matrix factorization method to solve for the weights of each type of defect in the candidate defect set and the weights of the supplementary defect types.

[0160] For example, similar to step S340, the response feature vectors corresponding to all defect types in the candidate defect set and the response feature vectors corresponding to supplementary defect types can be stacked row-wise to form a defect response matrix. For example, .

[0161] According to the equation To establish the relationship between the defect response matrix and the defect feature vector, and to obtain the optimal weights for each type of defect, an optimization objective can be established: minimizing the defect feature vector. The product of the defect response matrix and the weights of each defect The difference between them, namely ,in, This represents the Frobenius norm, which is the sum of squares of the errors in each dimension. This means that all defect weights must be non-negative, which is consistent with physical meaning.

[0162] Since obtaining the optimal weights for each type of defect is a linear fitting problem with non-negative constraints, it can be solved using the multiplicative iterative update method in Non-negative Matrix Factorization (NMF). The initial weight vectors for each type of defect can be set to all-one vectors or small positive random numbers. Starting from the initial weight vectors for each type of defect, the weight vectors for each type of defect are updated progressively (element-by-element multiplication (Hadamard product)) until the product between the defect response matrix and each defect weight is obtained. Closer to the defect feature vector The formula is updated at each step as follows: ,in, This indicates the similarity between the defect response and the defect feature vector. This represents the projection of the current estimate into the defect space. This represents a small positive number that is prevented from being divided by zero. If ,in This indicates that a threshold is set, or the iteration reaches the maximum number of iterations, then it stops, and finally outputs the weights of various types of defects in the candidate defect set and the weights of supplementary defect types.

[0163] The situation in step S340 can be solved by weighting using the nonnegative matrix factorization method in this step, and similarly, this step can also be solved using the least squares method in step S340.

[0164] This step enables multi-source interpretation of defects. By identifying conflicting parameters that do not match the candidate defect combination, it automatically supplements potential defect types and constructs a defect response matrix by combining response features. It then uses non-negative matrix factorization to quantitatively solve the weights of various defects, thereby achieving combination identification and contribution assessment of complex defects and improving the accuracy and intelligence level of fault diagnosis.

[0165] It should be understood that the sequence number of each step in the above embodiments does not imply 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.

[0166] Corresponding to the copper wire defect detection method described in the above embodiments, this application also provides a copper wire defect detection device, the various units of which can implement the various steps of the copper wire defect detection method. Figure 3 A structural block diagram of the copper wire defect detection device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0167] Reference Figure 3 The device includes: The acquisition unit is used to acquire the instantaneous phase difference and temperature drift signal of the surface acoustic wave (SAW) on the copper wire surface. The SAW is a standing wave type SAW formed by the interference of a dual-pulse laser beam with a 90° phase difference on the copper wire surface.

[0168] The defect-sensitive parameter determination unit is used to determine defect-sensitive parameters based on instantaneous phase difference and temperature drift signals. These defect-sensitive parameters characterize the changes in surface acoustic wave propagation characteristics caused by defects in the copper wire.

[0169] The defect depth calculation and defect type identification unit is used to calculate the equivalent defect depth of the copper wire based on the defect sensitivity parameters, and to determine the defect type of the copper wire based on the defect sensitivity parameters. The equivalent defect depth is the average depth of all defects.

[0170] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0171] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0172] This application also provides a copper wire defect detection device. Figure 4 This is a schematic diagram of a copper wire defect detection device according to an embodiment of this application. The copper wire defect detection device includes a laser emitting device, a detection device, and a control device communicatively connected to the laser emitting device and the detection device. Figure 4 As shown, the control device 6 of the copper wire defect detection equipment in this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown in the image), at least one memory 61 ( Figure 4 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, wherein when the processor 60 executes the computer program 62, it causes the copper wire defect detection device to perform the steps in any of the above copper wire defect detection method embodiments, or causes the copper wire defect detection device to perform the functions of each unit in the above device embodiments.

[0173] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 62 in the control device 6 of the copper wire defect detection equipment.

[0174] The control device 6 of the copper wire defect detection equipment can be a desktop computer, laptop, handheld computer, or cloud server, etc. The copper wire defect detection equipment may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 4 This is merely an example of a copper wire defect detection device and does not constitute a limitation on copper wire defect detection devices. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0175] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0176] In some embodiments, the memory 61 may be an internal storage unit of the control device 6 of the copper wire defect detection equipment, such as the hard disk or memory of the copper wire defect detection equipment. In other embodiments, the memory 61 may be an external storage device of the copper wire defect detection equipment, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the copper wire defect detection equipment. Furthermore, the memory 61 may include both internal and external storage units of the copper wire defect detection equipment. The memory 61 is used to store the operating system, application programs, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0177] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0178] This application provides a computer program product that, when run on a copper wire defect detection device, enables the copper wire defect detection device to implement the steps in any of the above method embodiments.

[0179] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a copper wire defect detection device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0180] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0181] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein 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 use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0182] In the embodiments provided in this application, it should be understood that the disclosed copper wire defect detection apparatus, device, and method can be implemented in other ways. For example, the copper wire defect detection apparatus and device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for detecting defects in copper wires, characterized in that, include: The instantaneous phase difference and temperature drift signal of surface acoustic waves on the surface of copper wire are obtained; wherein, the surface acoustic waves are standing wave type surface acoustic waves formed by interference of a double-pulse laser beam with a 90° phase difference on the surface of copper wire. Based on the instantaneous phase difference and the temperature drift signal, a defect-sensitive parameter is determined; wherein, the defect-sensitive parameter is used to characterize the change in the surface acoustic wave propagation characteristics in the copper wire caused by the defect; The equivalent depth of the defect in the copper wire is calculated based on the defect sensitivity parameter, and the defect type of the copper wire is determined based on the defect sensitivity parameter; wherein, the equivalent depth of the defect is the average depth of all defects.

2. The copper wire defect detection method as described in claim 1, characterized in that, The method further includes: The instantaneous phase difference and temperature drift signal of the surface acoustic wave at three spatial points on the surface of the copper wire are obtained by using dual wavelengths; wherein, the dual wavelengths include a 633nm wavelength and a 785nm wavelength, the 633nm wavelength is used to measure the surface displacement caused by the surface acoustic wave, and the 785nm wavelength is used to measure the thermal deformation of the copper wire, and the three spatial points include a center point and two points symmetrically distributed around the center point.

3. The copper wire defect detection method as described in claim 2, characterized in that, Before acquiring the instantaneous phase difference and temperature drift signal of the surface acoustic wave on the copper wire surface, the following steps are also included: Obtain the diameter data and surface temperature of the copper wire; The operating frequency of the surface acoustic wave is determined based on the diameter data. The ideal sound velocity is corrected based on the surface temperature to obtain the corrected sound velocity; wherein, the ideal sound velocity is used to characterize the ideal propagation speed of the surface acoustic wave on the surface of the copper wire. The target grating period is calculated based on the operating frequency and the corrected sound velocity; wherein the grating is used to control the wavelength of the surface acoustic wave, and the target grating period is equal to the wavelength of the surface acoustic wave; The driving voltage is determined based on the target grating period; wherein the driving voltage is used to adjust the period of the grating to the target grating period.

4. The copper wire defect detection method as described in claim 3, characterized in that, The method further includes: The first frequency of the surface acoustic wave is determined based on the diameter data; The second frequency of the surface acoustic wave is determined based on a pre-set defect detection depth. The operating frequency of the surface acoustic wave is determined based on the first frequency and the second frequency.

5. The copper wire defect detection method as described in claim 3, characterized in that, The determination of defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: Based on the instantaneous phase difference and temperature drift signal of the three points in space, the corrected phase of each point is calculated, and the surface displacement of each point is calculated based on the corrected phase of each point. The velocity of the particle at each point is obtained by differentiating the surface displacement at each point with respect to time. The surface acoustic wave propagation function is fitted based on the particle velocity at each point, and the actual sound velocity is extracted from the surface acoustic wave propagation function. The wave velocity offset is calculated based on the actual sound velocity and the corrected sound velocity; wherein the defect-sensitive parameters include the wave velocity offset, the resonance frequency shift, and the attenuation coefficient.

6. The copper wire defect detection method as described in claim 5, characterized in that, The determination of defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: Perform a Fourier transform on the surface displacement of the center point to obtain the spectrum of the center point; Extract the frequency corresponding to the maximum amplitude in the spectrum of the center point to obtain the actual resonant frequency; The resonance frequency shift is calculated based on the actual resonance frequency and the reference resonance frequency; wherein the reference resonance frequency is the resonance frequency corresponding to the defect-free, calibrated sample copper wire.

7. The copper wire defect detection method as described in claim 5, characterized in that, The determination of defect-sensitive parameters based on the instantaneous phase difference and the temperature drift signal includes: By performing Hilbert transforms on the surface displacements of the two points symmetrically distributed around the center point, the envelope amplitudes of the two points symmetrically distributed around the center point are obtained. Extract the peak amplitudes of the two points symmetrically distributed around the center point from their envelope amplitudes; The attenuation coefficient is calculated based on the peak amplitude of the two points symmetrically distributed around the center point and the distance between the two points symmetrically distributed around the center point.

8. The copper wire defect detection method as described in claim 5, characterized in that, The calculation of the equivalent depth of defects in the copper wire based on the defect sensitivity parameter includes: Calculate the relative wave velocity offset based on the wave velocity offset and the corrected sound velocity; The equivalent depth of the defect in the copper wire is calculated based on the relative offset of the wave velocity, the resonant frequency shift, and the attenuation coefficient.

9. The copper wire defect detection method as described in claim 5, characterized in that, Determining the defect type of the copper wire based on the defect sensitivity parameter includes: The wave velocity offset, the resonance frequency shift, and the attenuation coefficient are combined to form a defect feature vector; Based on the defect feature vector, the most similar defect combinations are selected from the defect fingerprint database to obtain a candidate defect set; wherein, the defect fingerprint database includes various defect type combinations and defect feature thresholds corresponding to each defect type combination; If there is only one defect combination in the candidate defect set and the defect feature vector completely matches the defect feature threshold corresponding to the candidate defect set, the defect type in the candidate defect set is determined as the defect type of the copper wire. If there are multiple defect combinations in the candidate defect set, the weights of each type of defect in the candidate defect set are calculated based on the defect feature vectors and the defect response matrix, and the defect type of the copper wire is determined based on the weights of each type of defect in the candidate defect set; wherein, the defect response matrix is ​​constructed by the response feature vectors corresponding to each type of defect in the candidate defect set, and the response feature vectors include wave velocity offset, resonance frequency shift, and attenuation coefficient; The method further includes: If there is only one defect combination in the candidate defect set but the defect feature vector does not completely match the defect feature threshold corresponding to the candidate defect set, a conflict parameter is determined based on the defect feature vector and the defect feature threshold corresponding to the candidate defect set, and a supplementary defect type is determined based on the conflict parameter. Extract the response feature vector corresponding to each defect type in the candidate defect set and the response feature vector corresponding to the supplementary defect type from the response data table; wherein, the response data table includes the response feature vector corresponding to each defect type; Based on the response feature vectors corresponding to each defect type in the candidate defect set and the response feature vectors corresponding to the supplementary defect types, the defect response matrix is ​​constructed. Then, based on the defect response matrix and the defect feature vectors, the weights of each type of defect in the candidate defect set and the weights of the supplementary defect types are solved using the non-negative matrix factorization method.

10. A copper wire defect detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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