Nondestructive testing method, device, equipment and medium for carbon content of silver-carbon composite coating, and product
By analyzing terahertz wave reflection signals and using teaching and learning optimization algorithms, the problem of non-destructive testing of carbon doping in silver-carbon composite coatings was solved, achieving efficient and accurate carbon doping assessment. This method is applicable to high-dimensional non-convex optimization problems in complex systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA SPECIAL EQUIP INSPECTION & RES INST
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-07
AI Technical Summary
Existing technologies make it difficult to achieve rapid and non-destructive detection of carbon doping in silver-carbon composite coatings. Traditional methods are inefficient and cannot reflect overall uniformity. The accuracy and reliability of traditional inversion methods are limited by the high reflectivity of silver, the non-uniform distribution of the carbon phase, and the multiple coupling effects of dispersion.
Terahertz waves were used for irradiation. An analytical model was constructed based on Fresnel's law of reflection and Kirchhoff's scattering theory. By correcting the surface roughness and compensating for the dispersion effect, and combining the teaching and learning optimization algorithm, the refractive index of the coating was obtained to evaluate the carbon doping content.
It enables rapid and non-destructive testing of carbon doping in silver-carbon composite coatings, avoids dependence on calibration samples, improves testing accuracy and reliability, and is applicable to high-dimensional non-convex optimization problems in complex systems.
Smart Images
Figure CN122345587A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of nondestructive testing technology and material characterization, and in particular to a method, apparatus, equipment, medium and product for nondestructive testing of carbon doping in silver-carbon composite coatings. Background Technology
[0002] Silver-carbon (graphene) composite coatings, combining the excellent conductivity of silver with the high strength, lubricity, and arc erosion resistance of graphene, have shown great application potential in critical power equipment such as high-voltage disconnector contacts. The core properties of these coatings, such as contact resistance, wear resistance, and resistance to welding, largely depend on the content and distribution of graphene (carbon phase). Therefore, achieving rapid and accurate assessment of the carbon doping content (graphene content) of the coating has become a crucial prerequisite for optimizing its preparation process and ensuring its service reliability. However, effective characterization of the carbon doping content in silver-carbon coatings still faces significant challenges. Traditional metallographic analysis methods require sample destruction, are inefficient, and fail to reflect overall uniformity; while electrical performance testing can indirectly reflect material properties, it cannot directly characterize the spatial distribution of the carbon phase. These methods are insufficient to meet the needs of rapid, non-destructive testing of large batches of samples in industrial production.
[0003] Terahertz waves (0.1-10 THz) are sensitive to dielectrics, have strong penetrating power, and low photon energy, providing unique advantages for non-destructive probing of internal material parameters. Currently, methods for measuring terahertz material parameters mainly include: one category is the open-cavity method and free-space method based on a vector network analyzer; the other is the time-domain spectroscopy method and frequency-domain spectroscopy method based on spectral principles. The former relies on an external vector network analyzer, and the terahertz frequency band has certain limitations; therefore, methods based on the terahertz time-domain spectroscopy principle are often used in practical research. Among these, the time-of-flight method is simple in principle and has high measurement efficiency, but it requires prior knowledge of the material thickness; machine learning methods construct a mapping relationship between terahertz signals and material thickness through neural networks, offering higher accuracy than the time-of-flight method; however, this method relies on large-scale datasets for training, resulting in lower applicability when the sample size is limited; model inversion methods do not require prior knowledge of material thickness and other parameters, and can achieve dielectric parameter extraction by reconstructing the reference signal through an analytical model, demonstrating strong adaptability in practical applications.
[0004] However, applying model inversion methods to the quantitative detection of carbon doping in silver-carbon composite coatings still faces fundamental bottlenecks. On the one hand, due to the spatial non-uniformity of graphene distribution in the electroplating process, it is difficult to prepare "calibration parts" with precisely known parameters and uniform distribution, making it difficult to implement compensation or calibration schemes that rely on standard samples. More importantly, when terahertz waves propagate in the coating, they are affected by the multiple coupling effects of silver's high reflectivity, the non-uniform distribution of the carbon phase, surface roughness, and dispersion effects, resulting in significant signal distortion, which severely limits the accuracy and reliability of traditional inversion methods. The strong coupling between material parameters makes the inversion problem essentially a high-dimensional, non-convex optimization problem, which is also the fundamental reason why calibration methods are difficult to apply in such complex systems.
[0005] Therefore, there is an urgent need for a model inversion method suitable for the carbon doping content of silver-carbon composite coatings, so as to realize the rapid and non-destructive detection of the carbon doping content of silver-carbon composite coatings. Summary of the Invention
[0006] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for non-destructive testing of carbon content in silver-carbon composite coatings, which can achieve rapid non-destructive testing of carbon content in silver-carbon composite coatings.
[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a non-destructive testing method for the carbon doping content of a silver-carbon composite coating, comprising: irradiating a silver-carbon composite coating sample with a terahertz wave to obtain the reflection signal of the terahertz wave at each measuring point on the silver-carbon composite coating sample.
[0008] A basic reflection signal model of terahertz waves at the air-coating interface is constructed based on Fresnel's law of reflection.
[0009] A terahertz analytical model is obtained by correcting the surface roughness and compensating for the dispersion effect of the basic reflection signal model of terahertz waves at the air-coating interface. The terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring point on the silver-carbon composite coating sample and the refractive index of the measuring point on the silver-carbon composite coating sample.
[0010] For any given measurement point, with the goal of minimizing the fitness function, the terahertz analytical model is inverted using a teaching and learning optimization algorithm based on the reflection signal of the measurement point on the silver-carbon composite coating sample to obtain the refractive index of the measurement point on the silver-carbon composite coating sample. The fitness function is constructed based on the simulated reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave and the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave.
[0011] The carbon content or uniformity of the silver-carbon composite coating sample is qualitatively evaluated based on the refractive index of each measuring point on the sample.
[0012] Secondly, this application provides a non-destructive testing device for the carbon doping content of a silver-carbon composite coating, comprising: a terahertz experimental module, used to irradiate a silver-carbon composite coating sample with terahertz waves and obtain the reflection signals of the terahertz waves at each measuring point on the silver-carbon composite coating sample.
[0013] The physics module is used to construct a basic reflection signal model of terahertz waves at the air-coating interface based on Fresnel's reflection law. Surface roughness correction and dispersion effect compensation are applied to the basic reflection signal model of terahertz waves at the air-coating interface to obtain a terahertz analytical model. The terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring points on the silver-carbon composite coating sample and the refractive index of the measuring points on the silver-carbon composite coating sample.
[0014] The refractive index determination module is used to minimize the fitness function at any given measurement point. Based on the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave, the module uses a teaching and learning optimization algorithm to invert the terahertz analytical model and obtain the refractive index of the measurement point on the silver-carbon composite coating sample. The fitness function is constructed based on the simulated reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave and the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave.
[0015] The carbon doping content determination module is used to qualitatively evaluate or evaluate the uniformity of carbon doping content in the silver-carbon composite coating sample based on the refractive index of each measuring point on the sample.
[0016] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described non-destructive testing method for the carbon doping content of silver-carbon composite coatings.
[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described non-destructive testing method for the carbon doping content of silver-carbon composite coatings.
[0018] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described non-destructive testing method for the carbon doping content of silver-carbon composite coatings.
[0019] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a non-destructive testing method, apparatus, equipment, medium, and product for the carbon doping content of silver-carbon composite coatings. This application does not require compensation or calibration based on standard samples. Furthermore, the terahertz analytical model is obtained by correcting the surface roughness and compensating for the dispersion effect of the basic reflection signal model of terahertz waves at the air-coating interface. This can solve the problem that the signal is significantly distorted due to the multiple coupling effects of the high reflectivity of silver, the non-uniform distribution of the carbon phase, surface roughness, and dispersion effect, which severely limits the accuracy and reliability of traditional inversion methods. By inverting the terahertz analytical model through a teaching and learning optimization algorithm, the strong coupling between material parameters is solved, making the inversion problem essentially a high-dimensional, non-convex optimization problem. In summary, this achieves rapid non-destructive testing of the carbon doping content of silver-carbon composite coatings. Attached Figure Description
[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments 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 flowchart illustrating a non-destructive testing method for the carbon content of a silver-carbon composite coating, provided in one embodiment of this application.
[0022] Figure 2 This is a flowchart illustrating a non-destructive testing method for the carbon content of a silver-carbon composite coating, provided as another embodiment of this application.
[0023] Figure 3 This is a waveform diagram of the reflected signal from a terahertz experiment.
[0024] Figure 4 This is a schematic diagram of the interaction between terahertz waves and the coating structure.
[0025] Figure 5 This is a flowchart of the refractive index inversion process based on the teaching and learning optimization algorithm.
[0026] Figure 6 The image shows the fitting plot of the experimental reflection signal and the simulated reflection signal.
[0027] Figure 7 This is a schematic diagram of the functional modules of a non-destructive testing device for the carbon content of a silver-carbon composite coating, provided in an embodiment of this application.
[0028] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0030] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0031] In one exemplary embodiment, such as Figure 1 As shown, a non-destructive testing method for carbon doping in a silver-carbon composite coating is provided, including: Step 201: Irradiating the silver-carbon composite coating sample with terahertz waves to obtain the reflection signals of the terahertz waves at each measuring point on the silver-carbon composite coating sample.
[0032] Step 202: Construct a basic reflection signal model of terahertz waves at the air-coating interface based on Fresnel's reflection law.
[0033] Step 203: Perform surface roughness correction and dispersion effect compensation on the basic reflection signal model of terahertz waves at the air-coating interface to obtain a terahertz analytical model; the terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring point on the silver-carbon composite coating sample and the refractive index of the measuring point on the silver-carbon composite coating sample.
[0034] Step 204: For any measurement point, with the goal of minimizing the fitness function, the terahertz analytical model is inverted using the teaching and learning optimization algorithm based on the reflection signal of the measurement point on the silver-carbon composite coating sample to obtain the refractive index of the measurement point on the silver-carbon composite coating sample. The fitness function is constructed based on the simulated reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave and the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave.
[0035] Step 205: Qualitatively evaluate or evaluate the carbon doping content or uniformity of the silver-carbon composite coating sample based on the refractive index of each measuring point on the sample.
[0036] In another exemplary embodiment of this application, a terahertz analytical model is obtained by correcting the surface roughness and compensating for the dispersion effect of the basic reflection signal model of the terahertz wave at the air-coating interface. Specifically, this includes applying Kirchhoff scattering theory to correct the surface roughness of the basic reflection signal model of the terahertz wave at the air-coating interface to obtain a corrected reflection signal model.
[0037] A terahertz analytical model is obtained by using a frequency domain filter to compensate for the dispersion effect of the modified reflection signal model.
[0038] In another exemplary embodiment of this application, the fitness function Specifically: Where q represents the weighting coefficient and N represents the total number of sampling points. This represents the reflection signal of the terahertz wave from the measurement point on the silver-carbon composite coating sample after normalization of the maximum value corresponding to the nth sampling point. This represents the simulated reflection signal of the terahertz wave at the measurement point on the silver-carbon composite coating sample after normalization of the maximum value corresponding to the nth sampling point. This represents the amplitude of the terahertz wave reflection signal at the measuring point on the silver-carbon composite coating sample after maximum value normalization within the main echo peak region. The value represents the amplitude of the simulated reflection signal of the terahertz wave at the measuring point on the silver-carbon composite coating sample after the maximum value normalization process, in the main echo peak region. || represents taking the absolute value.
[0039] In another exemplary embodiment of this application, in order to minimize the fitness function, the population in the teaching and learning optimization algorithm is initialized using the Latin hypercube sampling method during the process of inverting the terahertz analytical model based on the reflection signal of the terahertz wave at the measurement point on the silver-carbon composite coating sample. The population includes multiple individuals, and each individual includes the refractive index of the measurement point on the silver-carbon composite coating sample.
[0040] In another exemplary embodiment of this application, the amount of carbon doping in the silver-carbon composite coating sample is qualitatively evaluated or its uniformity is evaluated based on the refractive index of each measuring point on the sample. Specifically, the correlation between the amount of carbon doping and the refractive index is determined based on the effective medium theory.
[0041] The carbon doping content or uniformity of the silver-carbon composite coating sample is qualitatively evaluated based on the correlation between carbon doping and refractive index, as well as the refractive index at various measuring points on the sample. The refractive index at each measuring point is obtained, and its average value is calculated. Based on the average value and the correlation between carbon doping and refractive index, the average carbon doping content of the sample is obtained. A qualitative evaluation is then performed based on the average carbon doping content to determine whether the carbon doping content is high or low. The smaller the variance or range (fluctuation) calculated from the refractive index at each measuring point, the more uniform the distribution, thus assessing the uniformity of the coating preparation process.
[0042] In another exemplary embodiment of this application, the terahertz analytical model is: ,in, , This represents the simulated reflection signal of terahertz waves at the measurement points on the silver-carbon composite coating sample. This represents the Fresnel reflection coefficient of terahertz waves at the interface between the air and the silver-carbon composite coating sample. Denotes the base of the natural logarithm. Represents pi (π). This indicates the root mean square surface roughness of the silver-carbon composite coating sample. This indicates the wavelength of a terahertz wave. The terahertz incident reference signal represents the terahertz wave. This represents the time-domain standard deviation of the frequency-domain filter. express angular frequency, The complex refractive index of air; κ represents the refractive index at the measurement point on the silver-carbon composite coating sample, i represents the extinction coefficient, and i represents the imaginary unit.
[0043] This application also provides a more specific embodiment, detailing the non-destructive testing method for the carbon doping content of the above-mentioned silver-carbon composite coating, such as... Figure 2 As shown, the specific steps include: Step 1: Obtain the terahertz experimental reflection signal of the silver-carbon composite coating sample.
[0044] A reflective terahertz time-domain spectroscopy system was used (waveform diagram shown). Figure 3 As shown, measurements were taken on a silver-carbon composite coating sample to obtain the reflection signals of terahertz waves at each measuring point on the sample. (Terahertz experiment reflected signal).
[0045] Step 2: Establish a terahertz analytical model describing the interaction between the terahertz wave and the sample.
[0046] A schematic diagram of the interaction between terahertz waves and the coating structure is shown below. Figure 4 As shown, based on Fresnel's reflection law, Kirchhoff's scattering theory, and dispersion effect compensation mechanism, a terahertz analytical model is established to describe the interaction between terahertz waves and silver-carbon composite coatings, taking into account the effects of surface roughness and dispersion on terahertz waves, and is used to simulate the reflected signal.
[0047] Step 2.1: Based on Fresnel's law of reflection, establish a basic reflection signal model of the air-coating interface. (Based on Fresnel's law of reflection...) , ,in and Let F represent the Fresnel reflection coefficient and transmission coefficient of the terahertz wave at the interface between medium m and medium j, respectively. and Let m and j be the complex refractive indices of mediums m and j, respectively. A basic reflection signal model of the terahertz wave at the air-coating interface is established, and its frequency domain expression is: ,in, Represents the frequency domain reflected signal. is the terahertz incident reference signal, and is the reflection signal of the terahertz wave at the measuring point on the silver-carbon composite coating sample with zero carbon doping.
[0048] Step 2.2: Apply Kirchhoff scattering theory to the surface roughness correction of the basic reflection signal model. The corrected reflection signal model is expressed as follows: .
[0049] Step 2.3: Introduce a frequency domain filter to compensate for the dispersion effect in the roughness-corrected model to obtain the terahertz analytical model. To compensate for the pulse broadening caused by the dispersion effect of the terahertz wave propagating in the coating, a Gaussian broadening filter function is introduced in the frequency domain. As a frequency domain filter, the corrected reflection signal model is compensated. Combining surface roughness correction and dispersion effect compensation, a terahertz analytical model is obtained, whose frequency domain expression is: Among them, complex refractive index The real part n represents the speed of light in the medium and is related to the ratio of the speed of light to the speed of light. It is usually called the refractive index. The imaginary part κ is related to the attenuation of light in the medium and reflects the absorption characteristics of light. It is usually called the extinction coefficient. i is the imaginary unit. This model is used to generate the corresponding simulated reflection signal based on the given refractive index during the parameter inversion process.
[0050] Step 3: Using the equivalent refractive index of the sample (i.e., the refractive index of the silver-carbon composite coating material to be solved) To optimize the variables, a fitness function is constructed to evaluate the difference between the experimental reflected signal and the simulated reflected signal generated by the terahertz analytical model.
[0051] Step 3.1: Perform maximum value normalization on the experimental reflection signal and the simulated reflection signal to obtain the normalized experimental reflection signal. With simulated reflection signal This is to eliminate the influence of absolute amplitude on residual calculation.
[0052] Step 3.2: Calculate the global amplitude error of the two signals after normalization over the entire time domain. Calculate the average absolute error between the normalized experimental and simulated reflected signals at all sampling points over the entire time domain of the signals, and use this as the global amplitude error. Its expression is: .
[0053] Step 3.3: Locate the main echo peak region of the experimental reflected signal and calculate the two signals ( and The local amplitude error within the main echo peak region is used to focus the calculation of the local amplitude error on the main echo peak region of the normalized experimental reflected signal.
[0054] position The main echo peak is identified, and a key feature region containing this main echo peak is defined to obtain the main echo peak region. The absolute error of the amplitude between the normalized experimental reflection signal and the normalized simulated reflection signal within this main echo peak region is calculated. Its expression is: in, , The amplitudes of the normalized experimental and simulated reflection signals within the main echo peak region are used to capture key signal features most sensitive to changes in refractive index.
[0055] Step 3.4: Weight and fuse the global amplitude error and the local amplitude error to form a comprehensive fitness function.
[0056] global magnitude error Amplitude error with the main echo peak region Weighted fusion is performed to form the final comprehensive fitness function used to guide the optimization algorithm search, and its expression is: In one embodiment of this application, the weight coefficient q is set to 0.8. This weight allocation takes into account both the overall waveform matching and the attention to key feature regions, effectively balancing global convergence and feature sensitivity, and is used to balance the contribution ratio between global amplitude error and feature point amplitude error.
[0057] This application normalizes the experimental and simulated reflection signals, uses the refractive index of the coating as the core inversion parameter, constructs a physics-driven optimization problem, and constructs a comprehensive fitness function that integrates the global amplitude error and the amplitude error of the main echo peak region by focusing on the main echo peak region of the terahertz experimental reflection signal, so as to enhance the sensitivity to refractive index changes and the robustness of parameter identification.
[0058] Step 4: For any measurement point, an optimization algorithm is used to solve the terahertz analytical model. By minimizing the fitness function, the refractive index of the measurement point on the sample is obtained through inversion. The Teaching-Learning-Based Optimization (TLBO) algorithm, guided by the fitness function constructed in Step 3, is used to perform parameter inversion on the model in Step 2, achieving a high-precision solution for the coating refractive index. For example... Figure 5As shown. Specifically, it includes: Step 4.1: Determine the search space boundary of the refractive index to be inverted, and generate an initial population in the search space using the Latin hypercube sampling method. This method ensures that the individuals in the population are uniformly distributed in the parameter space through stratified sampling. Each individual in the population represents a candidate parameter solution vector X=[n1, κ, δ, σ]. t ].
[0059] Step 4.2: Calculate the fitness value of all individuals in the current population and select the individual with the best fitness as the teacher; for each individual (student) in the population, based on the difference between themselves and the teacher and the population average, according to the formula... Update its location, where, , , Let represent the knowledge state of the i-th student, the knowledge state of the optimal individual (teacher), and the average knowledge state (position vector) of all individuals, respectively. This represents the new knowledge state of the i-th student after instruction. Here, is the inertia weight, and r is a random number in the range [0,1]. This is the teaching intensity coefficient. It is used to calculate the new individual... Then, compare it with the original individual. The fitness value; if the fitness value of the new individual is better than that of the original individual, then use... replace (i.e., update the population location); otherwise, retain the original individual. .
[0060] Step 4.3: Simulate random interaction among students to enhance population diversity: For the i-th student, randomly select the j-th student, and update the position of the i-th student by comparing their fitness according to the following rules: , where f() is the fitness function value of an individual, and the fitness function is constructed in step 3 above.
[0061] Step 4.4: Repeat steps 4.2 to 4.3 above, evaluating the fitness value of the best individual in the population generation by generation, until the preset maximum number of iterations is reached or the fitness value meets the convergence accuracy requirements. At this point, the parameter value corresponding to the globally optimal individual is output as the optimal refractive index n obtained through inversion. Figure 6 As shown, the simulated reflection signal generated based on the analytical model with optimal parameters exhibits a high degree of consistency with the experimental reflection signal, verifying the effectiveness and accuracy of this inversion method. Figure 6 Part (a) shows a comparison diagram of the simulated reflected signal (analog signal) and the experimental reflected signal (experimental signal). Figure 6 Part (b) shows the amplitude residual diagram of the two signals.
[0062] Step 5: Based on the refractive index obtained by inversion, perform a qualitative assessment and a uniformity assessment of the carbon doping content of the sample.
[0063] Step 5.1: Based on the effective medium theory, determine the negative correlation between carbon doping content and equivalent refractive index. According to the effective medium theory, the calculation formula is as follows: ,in, and The refractive indices of silver and graphene are respectively. This represents the equivalent refractive index of the coating material. and These are the volume fractions of silver and graphene, respectively, calculated from the initial mass and density. ,in and The masses of silver and graphene are respectively. and The material densities of silver and graphene are respectively; for the tested silver-carbon composite coating sample; the equivalent refractive index of the composite material is related to the volume fraction of each component; for the silver-carbon composite coating, its refractive index is negatively correlated with the amount of carbon doping in graphene, that is, the higher the carbon doping, the lower the equivalent refractive index of the coating.
[0064] Step 5.2: Compare the relative magnitudes of the refractive indices obtained from inversion at different sample points or different measurement points of the same sample to qualitatively distinguish the amount of carbon doping or assess the uniformity of distribution.
[0065] By comparing the relative values of the average refractive indices obtained from the inversion of different samples, it can be seen from the relationship obtained in step 5.1 that the sample with the lower refractive index obtained from the inversion indicates that it has a higher carbon doping content; thus, the carbon doping content of different samples can be qualitatively evaluated, and the uniformity of the sample distribution can be evaluated based on the refractive index of different measurement points of the same sample.
[0066] This application first establishes an analytical model that comprehensively considers surface roughness and dispersion effects to simulate reflected signals. Second, using the coating refractive index as the core inversion parameter, a physics-driven optimization problem is constructed. By focusing on the main echo peak region, a fitness function that integrates global and local characteristic errors is designed to improve the robustness of the inversion process. Next, a teaching and learning optimization algorithm is used to solve the terahertz analytical model, achieving high-precision solution for the refractive index. Finally, based on the effective medium theory, the inverted refractive index is mapped to the carbon doping content of the coating, enabling the evaluation of the uniformity of the coating composition. By establishing an analytical model of terahertz wave-coating interaction that comprehensively considers surface roughness and dispersion effects, and employing a teaching and learning optimization algorithm with an improved fitness function, high-precision inversion of coating refractive index is achieved without relying on destructive sampling, prior knowledge of thickness, or calibration samples. This method effectively overcomes signal interference caused by the high reflectivity of silver-based coatings and the inhomogeneity of carbon phases, exhibiting advantages such as strong anti-interference capability and high consistency. It is suitable for rapid non-destructive testing and process optimization of silver-carbonene coating quality, providing technical support for coating quality assessment and process optimization. This application provides a reliable technical means for rapid, non-destructive assessment and process optimization of coating quality for key components such as power contacts, and has significant engineering application value for improving the reliability of power equipment and extending its service life.
[0067] This application achieves rapid, non-destructive testing without calibration or prior thickness information. Addressing the bottleneck of existing methods that rely on destructive sampling, standard samples, or known thicknesses, this application uses refractive index as the sole core inversion parameter, directly solving for it through physical modeling and optimization algorithms. This approach decouples complex multi-parameter influences and reduces them to the identification of a single physical quantity, thereby eliminating dependence on standard samples and thickness information and significantly improving the method's versatility and engineering applicability.
[0068] This application overcomes multi-physics coupling interference, achieving high-precision and robust parameter inversion. Addressing the severe signal distortion caused by high reflectivity, roughness, and dispersion in silver-carbon composite coatings, this application contributes in three ways: First, it constructs an analytical model integrating surface scattering and dispersion compensation, reducing simulation errors at the physical level; second, it designs a fitness function that integrates global and local peaks, enhancing the sensitivity of the optimization process to key refractive index features; finally, it employs a teach-and-learn optimization algorithm for global optimization, effectively avoiding local optima. These three aspects work synergistically to ensure the accuracy and stability of the refractive index inversion results under strong interference environments.
[0069] This application presents a complete technical solution suitable for rapid on-site evaluation, with significant potential for engineering applications. Based on the reliability and convenience guaranteed by the aforementioned technical features, this application provides an automated process from signal acquisition and model calculation to optimization and inversion. This method eliminates the need for complex sample preparation or large-scale data training, enabling direct non-destructive measurement and consistency analysis of production line samples. This achieves a leap from laboratory analysis to on-site online evaluation, providing an efficient means for coating process optimization and quality control.
[0070] Based on the same inventive concept, this application also provides a non-destructive testing device for carbon doping in silver-carbon composite coatings, used to implement the aforementioned non-destructive testing method for carbon doping in silver-carbon composite coatings. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the non-destructive testing device for carbon doping in silver-carbon composite coatings provided below can be found in the limitations of the non-destructive testing method for carbon doping in silver-carbon composite coatings described above, and will not be repeated here.
[0071] In one exemplary embodiment, such as Figure 7 As shown, a non-destructive testing device for carbon doping in a silver-carbon composite coating is provided, comprising: a terahertz experimental module, used to irradiate a silver-carbon composite coating sample with terahertz waves and obtain the reflection signals of the terahertz waves at each measuring point on the silver-carbon composite coating sample.
[0072] The physics module is used to construct a basic reflection signal model of terahertz waves at the air-coating interface based on Fresnel's reflection law. Surface roughness correction and dispersion effect compensation are applied to the basic reflection signal model of terahertz waves at the air-coating interface to obtain a terahertz analytical model. The terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring points on the silver-carbon composite coating sample and the refractive index of the measuring points on the silver-carbon composite coating sample.
[0073] The refractive index determination module is used to minimize the fitness function at any given measurement point. Based on the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave, the module uses a teaching and learning optimization algorithm to invert the terahertz analytical model and obtain the refractive index of the measurement point on the silver-carbon composite coating sample. The fitness function is constructed based on the simulated reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave and the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave.
[0074] The carbon doping content determination module is used to qualitatively evaluate or evaluate the uniformity of carbon doping content in the silver-carbon composite coating sample based on the refractive index of each measuring point on the sample.
[0075] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores non-destructive testing data on the carbon doping content of silver-carbon composite coatings. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a non-destructive testing method for the carbon doping content of silver-carbon composite coatings.
[0076] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0077] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.
[0078] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.
[0079] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.
[0080] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0081] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0082] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.
[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0084] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A non-destructive testing method for the carbon content in a silver-carbon composite coating, characterized in that, The non-destructive testing method for the carbon doping content of the silver-carbon composite coating includes: The silver-carbon composite coating sample was irradiated with terahertz waves to obtain the reflection signals of the terahertz waves at each measuring point on the silver-carbon composite coating sample. A basic reflection signal model of terahertz waves at the air-coating interface is constructed based on Fresnel's law of reflection. A terahertz analytical model is obtained by correcting the surface roughness and compensating for the dispersion effect of the basic reflection signal model of terahertz waves at the air-coating interface. The terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring point on the silver-carbon composite coating sample and the refractive index of the measuring point on the silver-carbon composite coating sample. For any given measurement point, with the goal of minimizing the fitness function, the terahertz analytical model is inverted using a teaching and learning optimization algorithm based on the reflection signal of the measurement point on the silver-carbon composite coating sample to obtain the refractive index of the measurement point on the silver-carbon composite coating sample. The fitness function is constructed based on the simulated reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave and the reflection signal of the measurement point on the silver-carbon composite coating sample to the terahertz wave. The carbon content or uniformity of the silver-carbon composite coating sample is qualitatively evaluated based on the refractive index of each measuring point on the sample.
2. The non-destructive testing method for carbon doping content in silver-carbon composite coatings according to claim 1, characterized in that, A terahertz analytical model is obtained by correcting the surface roughness and compensating for the dispersion effect of the basic reflection signal model of terahertz waves at the air-coating interface, specifically including: By applying Kirchhoff scattering theory to correct the surface roughness of the basic reflection signal model of terahertz waves at the air-coating interface, a corrected reflection signal model is obtained. A terahertz analytical model is obtained by using a frequency domain filter to compensate for the dispersion effect of the modified reflection signal model.
3. The non-destructive testing method for carbon doping content in silver-carbon composite coatings according to claim 1, characterized in that, The fitness function Specifically: Where q represents the weighting coefficient and N represents the total number of sampling points. This represents the reflection signal of the terahertz wave from the measurement point on the silver-carbon composite coating sample after normalization of the maximum value corresponding to the nth sampling point. This represents the simulated reflection signal of the terahertz wave at the measurement point on the silver-carbon composite coating sample after normalization of the maximum value corresponding to the nth sampling point. This represents the amplitude of the terahertz wave reflection signal at the measuring point on the silver-carbon composite coating sample after maximum value normalization within the main echo peak region. The value represents the amplitude of the simulated reflection signal of the terahertz wave at the measuring point on the silver-carbon composite coating sample after the maximum value normalization process, in the main echo peak region. || represents taking the absolute value.
4. The non-destructive testing method for carbon doping content in silver-carbon composite coatings according to claim 1, characterized in that, In the process of minimizing the fitness function and obtaining the refractive index of the terahertz waves at the measurement points on the silver-carbon composite coating sample by inverting the terahertz analytical model using the teaching and learning optimization algorithm, the population in the teaching and learning optimization algorithm is initialized using the Latin hypercube sampling method. The population includes multiple individuals, and each individual includes the refractive index of the measurement points on the silver-carbon composite coating sample.
5. The non-destructive testing method for carbon doping content in silver-carbon composite coatings according to claim 1, characterized in that, The carbon doping content or uniformity of the silver-carbon composite coating sample is qualitatively evaluated based on the refractive index of each measuring point on the sample. Specifically: The correlation between carbon doping and refractive index was determined based on the effective medium theory; The carbon content or uniformity of the silver-carbon composite coating sample is qualitatively evaluated based on the correlation between carbon doping and refractive index and the refractive index at each measuring point on the sample.
6. The non-destructive testing method for carbon doping content in silver-carbon composite coatings according to claim 2, characterized in that, The terahertz analytical model is as follows: ,in, , This represents the simulated reflection signal of terahertz waves at the measurement points on the silver-carbon composite coating sample. This represents the Fresnel reflection coefficient of terahertz waves at the interface between the air and the silver-carbon composite coating sample. Denotes the base of the natural logarithm. Represents pi (π). This indicates the root mean square surface roughness of the silver-carbon composite coating sample. This indicates the wavelength of a terahertz wave. The terahertz incident reference signal represents the terahertz wave. This represents the time-domain standard deviation of the frequency-domain filter. express angular frequency, The complex refractive index of air; κ represents the refractive index at the measurement point on the silver-carbon composite coating sample, i represents the extinction coefficient, and i represents the imaginary unit.
7. A non-destructive testing device for the carbon content of a silver-carbon composite coating, characterized in that, The non-destructive testing device for the carbon content of the silver-carbon composite coating includes: The terahertz experimental module is used to irradiate silver-carbon composite coating samples with terahertz waves and obtain the reflection signals of terahertz waves at each measuring point on the silver-carbon composite coating sample. The physics module is used to construct a basic reflection signal model of terahertz waves at the air-coating interface based on Fresnel's reflection law. Surface roughness correction and dispersion effect compensation are applied to the basic reflection signal model of terahertz waves at the air-coating interface to obtain a terahertz analytical model. The terahertz analytical model is used to represent the mapping relationship between the simulated reflection signal of terahertz waves at the measuring points on the silver-carbon composite coating sample and the refractive index of the measuring points on the silver-carbon composite coating sample. The refractive index determination module is used to determine the refractive index of any measurement point by minimizing the fitness function. Based on the reflection signals of terahertz waves from the measurement points on the silver-carbon composite coating sample, a teaching and learning optimization algorithm is used to invert the terahertz analytical model. The fitness function is constructed based on the simulated reflection signals of terahertz waves from the measurement points on the silver-carbon composite coating sample. The carbon doping content determination module is used to qualitatively evaluate or evaluate the uniformity of carbon doping content in the silver-carbon composite coating sample based on the refractive index of each measuring point on the sample.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the non-destructive testing method for the carbon doping content of a silver-carbon composite coating as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the non-destructive testing method for the carbon content of silver-carbon composite coatings as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the non-destructive testing method for the carbon content of silver-carbon composite coatings as described in any one of claims 1-6.