Method and device for monitoring internal damage of metal based on flexible electronic sensing probe

Abstract

The application discloses a metal internal damage monitoring method and device based on flexible electronic sensing detection, relates to the technical field of structural health monitoring, and comprises the following steps: attaching a flexible electronic sensor array to the surface of a metal to be measured, and obtaining an electrical response signal associated with the internal damage state of the metal to be measured based on the flexible electronic sensor array by adopting a preset electrical response signal acquisition mode; collecting the electrical response signal at a preset sampling frequency, and extracting a plurality of electrical characteristic parameters of the metal to be measured from the electrical response signal; inputting the plurality of electrical characteristic parameters of the metal to be measured into a metal internal damage prediction model to obtain a damage field prediction value of the metal to be measured; and generating an internal damage distribution map of the metal to be measured based on the damage field prediction value of the metal to be measured by adopting a preset two-dimensional visualization algorithm. The application realizes online monitoring of internal defects of a metal structure and improves the monitoring accuracy.

Description

Technical Field

[0001] This application relates to the field of structural health monitoring technology, and in particular to a method and device for monitoring internal damage in metals based on flexible electronic sensing detection. Background Technology

[0002] Metal structures are subject to factors such as loads, vibrations, and temperature changes during operation, which may lead to hidden defects such as internal microcracks, fatigue damage, and voids. Traditional inspection methods rely on periodic testing using ultrasonic, eddy current, or X-ray equipment, requiring downtime or disassembly for inspection, making long-term online monitoring of the structural condition difficult. While flexible electronics technology can be attached to metal surfaces to sense structural conditions through changes in resistance or impedance, existing flexible sensors are mainly used for surface strain or temperature monitoring, lacking sufficient sensitivity to internal metal damage and lacking a reliable mapping relationship between internal damage and electrical response.

[0003] Therefore, it is necessary to provide a method for monitoring internal damage in metals based on flexible electronic sensing detection to solve the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for monitoring internal damage in metals based on flexible electronic sensing and detection, which enables online monitoring of internal defects in metal structures and improves the accuracy of monitoring.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a method for monitoring internal metal damage based on flexible electronic sensing detection. The flexible electronic sensor array includes, from bottom to top, a flexible substrate layer, a conductive layer, and an encapsulation layer. The method for monitoring internal metal damage based on flexible electronic sensing detection includes:

[0007] A flexible electronic sensor array is attached to the surface of the metal under test, and an electrical response signal acquisition method is adopted. Based on the flexible electronic sensor array, an electrical response signal associated with the internal damage state of the metal structure under test is obtained.

[0008] The electrical response signal is acquired at a preset sampling frequency, and multiple electrical characteristic parameters of the metal under test are extracted from the electrical response signal.

[0009] Multiple electrical characteristic parameters of the metal to be tested are input into the metal internal damage prediction model to obtain the damage field prediction value of the metal to be tested; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field.

[0010] Using a pre-defined two-dimensional visualization algorithm, an internal damage distribution map of the metal under test is generated based on the predicted damage field value of the metal under test.

[0011] In one embodiment, the preset electrical response signal acquisition method is either an active excitation method or a passive response method.

[0012] In one embodiment, when the preset electrical response signal acquisition method is an active excitation method, the preset electrical response signal acquisition method is used to obtain an electrical response signal associated with the internal damage state of the metal structure under test based on the flexible electronic sensor array, specifically including:

[0013] An excitation is applied to the flexible electronic sensor array to induce it to generate an electrical response signal associated with the internal damage state of the metal structure under test; the excitation is an applied current excitation or a mechanical strain excitation.

[0014] In one embodiment, when the preset electrical response signal acquisition method is a passive response method, the preset electrical response signal acquisition method is used to obtain an electrical response signal associated with the internal damage state of the metal structure under test based on the flexible electronic sensor array, specifically including:

[0015] The flexible electronic sensor array is excited to generate an electrical response signal associated with the internal damage state of the metal structure under test by utilizing the self-excited vibration or stress wave propagation of the metal structure under test.

[0016] In one embodiment, the electrical characteristic parameters include impedance magnitude, frequency offset, and propagation delay;

[0017] Multiple electrical characteristic parameters of the metal under test are extracted from the electrical response signal, specifically including:

[0018] The electrical response signal is filtered to obtain a filtered electrical response signal;

[0019] Feature extraction is performed on the filtered electrical response signal to obtain multiple electrical characteristic parameters of the metal under test.

[0020] In one embodiment, the metal internal damage prediction model is constructed based on the finite element simulation method or trained using a training set to train a neural network model.

[0021] In one embodiment, the process of constructing a metal internal damage prediction model based on the finite element simulation method specifically includes:

[0022] Construct an initial finite element model that includes the geometry of the metal under test, material properties, and the placement of the flexible electronic sensor array;

[0023] In the initial finite element model, different preset virtual damages are defined and applied respectively;

[0024] Electro-mechanical coupling field analysis was performed on the initial finite element model with preset virtual damage. The electrical response signal generated by the flexible electronic sensor array under each preset virtual damage condition was simulated and calculated, and the corresponding virtual electrical characteristic parameters were extracted from it.

[0025] The description parameters of each preset virtual damage are associated with its corresponding virtual electrical feature parameters to construct a virtual damage-electrical feature parameter mapping dataset;

[0026] A preset algorithm is used to obtain the metal internal damage prediction model based on the virtual damage-feature parameter mapping dataset.

[0027] In one embodiment, the process of training a neural network model using a training set to obtain a metal internal damage prediction model specifically includes:

[0028] Construct a training set; the training set includes the electrical characteristic parameters of metal specimens with known internal damage states and the corresponding true values ​​of the loss field;

[0029] Build a neural network model;

[0030] The electrical characteristic parameters of a metal sample with known internal damage state are used as input, and the corresponding known true value of the loss field is used as output to train the neural network model. Training is stopped when the loss function reaches its minimum value or the number of training rounds reaches its maximum value, thus obtaining the metal internal damage prediction model.

[0031] In one embodiment, the flexible electronic sensor array is a conductive composite network structure.

[0032] Secondly, this application provides a metal internal damage monitoring device based on flexible electronic sensing detection. The metal internal damage monitoring device based on flexible electronic sensing detection is used to implement the metal internal damage monitoring method based on flexible electronic sensing detection. The metal internal damage monitoring device based on flexible electronic sensing detection includes: a flexible electronic sensor array, a signal acquisition and conditioning module, a prediction module, and a visualization output module.

[0033] A flexible electronic sensor array is attached to the surface of the metal to be tested. The flexible electronic sensor array is connected to a signal acquisition and conditioning module, and the signal acquisition and conditioning module, prediction module, and visualization output module are connected in sequence. The flexible electronic sensor array includes a flexible substrate layer, a conductive layer, and an encapsulation layer from bottom to top.

[0034] The flexible electronic sensor array is used to generate an electrical response signal that is associated with the internal damage state of the metal structure under test, under a preset electrical response signal acquisition method.

[0035] The signal acquisition and conditioning module is used to acquire the electrical response signal at a preset sampling frequency and extract multiple electrical characteristic parameters of the metal under test from the electrical response signal;

[0036] The prediction module is used to input multiple electrical characteristic parameters of the metal under test into the metal internal damage prediction model to obtain the damage field prediction value of the metal under test; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field, and the metal internal damage prediction model is deployed in the prediction module;

[0037] The visualization output module is used to generate an internal damage distribution map of the metal under test based on the predicted damage field value of the metal under test using a preset two-dimensional visualization algorithm.

[0038] According to the specific embodiments provided in this application, this application has the following technical effects:

[0039] This application discloses a method and device for monitoring internal damage in metals based on flexible electronic sensing. First, by directly attaching a flexible electronic sensor array to the surface of the metal structure, it can operate without shutdown or disassembly, achieving in-situ online monitoring. This enables long-term, continuous online monitoring, avoiding time window omissions and safety risks associated with periodic offline detection. Second, by constructing a metal internal damage prediction model, the extracted electrical characteristic parameters are mapped to the spatial distribution (damage field) of internal damage. This not only qualitatively determines the existence of damage but also quantitatively reflects the location, extent (spatial coordinates), and severity (damage intensity / probability) of the damage, improving the accuracy and comprehensiveness of internal damage monitoring and location, and providing precise quantitative evidence for structural health assessment. Attached Figure Description

[0040] 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.

[0041] Figure 1 A schematic flowchart of a metal internal damage monitoring method based on flexible electronic sensing detection provided in an embodiment of this application;

[0042] Figure 2 A schematic diagram showing the positional relationship of the multilayer structure of a flexible electronic sensor array provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram of a flexible electronic sensor array attached to a metal substrate according to an embodiment of this application;

[0044] Figure 4 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A1 provided in an embodiment of this application;

[0045] Figure 5 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A2 provided in an embodiment of this application;

[0046] Figure 6 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A3 provided in an embodiment of this application;

[0047] Figure 7 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A4 provided in an embodiment of this application;

[0048] Figure 8 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A5 provided in an embodiment of this application;

[0049] Figure 9 A comparative schematic diagram of the spatial distribution of internal cracks or pores in metal under damage condition A6 provided in an embodiment of this application;

[0050] Figure 10 This is a schematic diagram of typical damage monitoring results provided in an embodiment of this application. Detailed Implementation

[0051] 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.

[0052] 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.

[0053] In one exemplary embodiment, such as Figure 1 As shown, a method for monitoring internal damage in metals based on flexible electronic sensing detection is provided, wherein, as Figure 2As shown, the flexible electronic sensor array comprises, from bottom to top, a flexible substrate layer, a conductive layer, and an encapsulation layer. The flexible substrate layer is located at the bottom and provides flexible support. The conductive layer is located in the middle and contains conductive particles or a conductive network to form electrical signal channels. The encapsulation layer is located at the top and protects the conductive layer from external humidity, oxidation, and mechanical friction. The three layers are bonded together through lamination to form the overall flexible electronic sensor structure. The conductive layer uses silver nanowires or graphene composite conductive networks; the flexible substrate material can be polyimide or an elastomer film; the encapsulation layer protects the conductive network and improves its adaptability to environmental factors such as humidity and oxidation. The flexible electronic sensor array can be a two-dimensional matrix structure, with the number of channels ranging from 4×4 to 16×16, determined according to the monitoring area. The flexible electronic sensor array is attached to the metal surface via a flexible adhesive layer, which can use silicone adhesive or flexible epoxy adhesive to maintain interface stability. The flexible electronic sensor array can cover the monitoring area and ensure the reliability of interface signal transmission. Preferably, the flexible electronic sensor array is a conductive composite network structure.

[0054] The method for monitoring internal metal damage based on flexible electronic sensing detection includes the following steps:

[0055] Step S1: A flexible electronic sensor array is attached to the surface of the metal to be tested. Using a preset electrical response signal acquisition method, an electrical response signal correlated with the internal damage state of the metal structure is obtained based on the flexible electronic sensor array. After the flexible electronic sensor array is attached to the surface of the metal to be tested and electrically connected and fixed, a schematic diagram of the flexible electronic sensor array attached to the metal substrate is shown below. Figure 3 As shown.

[0056] As an optional implementation, in step S1, the preset electrical response signal acquisition method is either an active excitation method or a passive response method.

[0057] As an optional implementation, in step S1, when the preset electrical response signal acquisition method is an active excitation method, the preset electrical response signal acquisition method is used to obtain an electrical response signal associated with the internal damage state of the metal structure under test based on the flexible electronic sensor array, specifically including:

[0058] An excitation is applied to the flexible electronic sensor array to induce it to generate an electrical response signal associated with the internal damage state of the metal structure under test; the excitation is an applied current excitation or a mechanical strain excitation.

[0059] Specifically, when using active excitation, by applying an external current to the flexible electronic sensor array, the electrical response of the array's resistance or impedance to changes in internal metal damage can be obtained; when applying mechanical strain excitation, the resistance change signal of the array due to the piezoresistive effect is recorded. Both types of active excitation can reflect the impact of internal damage on the array's conductive network.

[0060] As an optional implementation, in step S1, when the preset electrical response signal acquisition method is a passive response method, the preset electrical response signal acquisition method is used to obtain an electrical response signal associated with the internal damage state of the metal structure under test based on the flexible electronic sensor array, specifically including:

[0061] The flexible electronic sensor array is excited to generate an electrical response signal associated with the internal damage state of the metal structure under test by utilizing the self-excited vibration or stress wave propagation of the metal structure under test.

[0062] Specifically, when using a passive response method, the dynamic electrical response signal of the flexible electronic sensor array is obtained by utilizing the self-excited vibration or stress wave propagation process of the metal structure, and the characteristics of the resistance or impedance changes over time due to changes in the propagation path are recorded. The passive response method can also reflect the disturbances caused by internal metal defects to the signal propagation path and conductive channels.

[0063] Step S2: Acquire the electrical response signal at a preset sampling frequency, and extract multiple electrical characteristic parameters of the metal under test from the electrical response signal.

[0064] As an optional implementation, in step S2, the electrical characteristic parameters include impedance amplitude, frequency offset, and propagation delay.

[0065] Then, multiple electrical characteristic parameters of the metal under test are extracted from the electrical response signal, specifically including:

[0066] Step S21: Filter the electrical response signal to obtain a filtered electrical response signal;

[0067] Step S22: Feature extraction is performed on the filtered electrical response signal to obtain multiple electrical characteristic parameters of the metal under test.

[0068] Specifically, electrical response signals are acquired at sampling frequencies ranging from several kHz to hundreds of kHz to meet the monitoring needs of different metal structures. These signals are then filtered to extract multiple electrical characteristic parameters, which characterize the impact of internal damage on the signal path. These electrical characteristic parameters form a multidimensional feature matrix, providing input data for subsequent prediction models.

[0069] Step S3: Input multiple electrical characteristic parameters of the metal to be tested into the metal internal damage prediction model to obtain the damage field prediction value of the metal to be tested; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field.

[0070] Specifically, the output of the metal internal damage prediction model is damage field data that characterizes the spatial distribution of damage, including damage intensity or damage probability at each location. Based on the output damage field, the spatial coordinates, probability distribution characteristics, or morphological parameters of the damage can be further extracted to characterize the extent and variation of internal cracks or pores.

[0071] The metal internal damage prediction model is constructed based on the mechanical-electrical coupled multiphysics field governing equations and their finite element method. The model consists of three parts: First, the mechanical field governing equations are used to solve for the displacement, stress, and strain distributions of the structure under loading conditions, characterizing the local mechanical response changes caused by internal metal damage. Second, the electromechanical coupling equations describe the changes in equivalent conductivity of the conductive network caused by strain evolution, further leading to changes in the overall structural resistance or impedance. Finally, a global mechanical-electrical coupled finite element equation is established within the finite element framework to achieve a unified coupled solution between the mechanical field state variables and the electrical response.

[0072] 1) Control equations of the mechanical field

[0073] (1)

[0074] (2)

[0075] (3)

[0076] in, For gradient operators; For stress; It is a volume density vector; The density of metallic materials; For displacement; For time; Elastic stiffness; In response to the situation; For displacement gradient; This is the transpose of the matrix.

[0077] 2) Electromechanical coupling equations

[0078] When analyzing flexible electrons, the following deformation-conductivity coupling model is typically used to describe them:

[0079] (4)

[0080] That is, the conductivity changes linearly or nonlinearly with strain ε.

[0081] in, Equivalent conductivity; The initial conductivity; is the coupling coefficient.

[0082] The change in total impedance is caused by the combination of geometric deformation:

[0083] (5)

[0084] in, This is the total impedance; It is an equivalent mapping function; These are geometric parameters that vary with stress.

[0085] 3) Overall machine-electric coupling finite element equation

[0086] The coupled differential equations in COMSOL / ABAQUS co-simulation are usually written as:

[0087] (6)

[0088] (7)

[0089] (8)

[0090] The final mapping is formed:

[0091] (9)

[0092] in, For mechanical field finite element operators; The strain field is calculated from the displacement field; The equivalent conductivity field varies with stress; It is the set of mechanical field state variables; It is a set of electrical response variables.

[0093] The above principles form the basis for training the metal internal damage prediction model.

[0094] As an optional implementation, in step S3, the metal internal damage prediction model is constructed based on the finite element simulation method or trained using a training set to train a neural network model.

[0095] As an optional implementation, step S3, the process of constructing a metal internal damage prediction model based on the finite element simulation method, specifically includes:

[0096] Step S311: Construct an initial finite element model that includes the geometry of the metal under test, material properties, and the placement of the flexible electronic sensor array.

[0097] Step S312: In the initial finite element model, different preset virtual damages are defined and applied respectively.

[0098] Step S313: Perform electro-mechanical coupling field analysis on the initial finite element model with preset virtual damage, simulate and calculate the electrical response signal generated by the flexible electronic sensor array under each preset virtual damage condition, and extract the corresponding virtual electrical characteristic parameters from it.

[0099] Step S314: Associate the description parameters of each preset virtual damage with its corresponding virtual electrical feature parameters to construct a virtual damage-electrical feature parameter mapping dataset.

[0100] Step S315: Using a preset algorithm, based on the virtual damage-feature parameter mapping dataset, the metal internal damage prediction model is obtained.

[0101] As an optional implementation, step S3, the process of training the neural network model using a training set to obtain a metal internal damage prediction model, specifically includes:

[0102] Step S321: Construct a training set; the training set includes the electrical characteristic parameters of metal samples with known internal damage states and the corresponding true values ​​of the loss field.

[0103] Step S322: Build the neural network model.

[0104] Specifically, the neural network model can be any one of a multilayer feedforward neural network, a convolutional neural network, or a residual neural network, preferably a multilayer feedforward neural network.

[0105] The neural network model consists of an input layer, hidden layers, and an output layer. The input layer and hidden layers are fully connected, as are the hidden layers themselves, and the last hidden layer is fully connected to the output layer. Specifically, the input layer receives the electrical characteristic parameters of the metal under test; the hidden layers extract the nonlinear relationship between the electrical characteristic parameters and internal damage; and the output layer outputs the predicted damage field values ​​for each location within the monitoring area of ​​the metal under test.

[0106] Preferably, the hidden layers consist of 2 to 4 layers, with each layer containing 32 to 128 neurons, and the ReLU activation function is used. The number of nodes in the output layer corresponds to the number of grid cells in the damage field, enabling prediction of the spatial distribution of internal damage. During training, the backpropagation algorithm is used to update the network parameters, the mean squared error loss function is used, and the Adam algorithm is used for optimization.

[0107] Step S323: The electrical characteristic parameters of the metal sample with known internal damage state are used as input, and the corresponding known true value of the loss field is used as output to train the neural network model until the loss function reaches the minimum value or the training rounds reach the maximum value, then the training stops to obtain the metal internal damage prediction model.

[0108] Step S4: Using a preset two-dimensional visualization algorithm, an internal damage distribution map of the metal under test is generated based on the predicted damage field value of the metal under test.

[0109] Specifically, the damage field can be represented as a two-dimensional grid corresponding to the sensing array, with each grid cell corresponding to a damage intensity or damage probability value. By mapping the damage values ​​to color gradients and using pseudo-color maps, contour maps, or other two-dimensional graphics rendering methods, a spatial distribution map of internal cracks or holes can be obtained to show the location, extent, and trend of damage. Figures 4-9 This diagram illustrates the spatial distribution of internal cracks or pores in metals under different damage conditions (A1~A6). Each damage condition is represented by a "simulation result" (i.e.,...). Figure 4 (a) ~ Figure 9 (a) and "experimental results" (i.e. Figure 4 (b) ~ Figure 9 It consists of two parts (b) to verify the accuracy and consistency of the established metal internal damage prediction model in damage identification.

[0110] Overall, the simulation results can reproduce the stress wave propagation characteristics and morphological evolution of the damaged area well. For example, under the A1~A3 conditions, the damage is mainly in the form of initial local failure. Both the simulation and the experiment show obvious central impact zone and outward diffusion of microcracks or particle ejection. The two have a high degree of consistency in damage range and distribution trend.

[0111] As the damage severity increases (A4~A6), more complex damage morphologies can be observed, such as annular propagation zones, multi-layered fracture structures, and obvious radial crack distribution. The typical concentric annular damage structure in condition A5 is clearly visible in both simulation and experiments, indicating that the metal internal damage prediction model can effectively capture the shock wave reflection and superposition effects. The multidirectional crack propagation and fragmentation characteristics exhibited in condition A6 also highly agree with the experimental results, verifying the applicability of the metal internal damage prediction model under complex damage conditions.

[0112] Overall, the simulation results and experimental observations show good consistency in terms of damage morphology, energy distribution, and crack propagation trend, indicating that this application can accurately reflect the evolution process of internal damage in metals, providing a reliable basis for subsequent damage identification and structural health monitoring.

[0113] Based on the same inventive concept, this application also provides a metal internal damage monitoring device based on flexible electronic sensing detection for implementing the aforementioned metal internal damage monitoring method based on flexible electronic sensing detection. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more metal internal damage monitoring device embodiments based on flexible electronic sensing detection provided below can be found in the limitations of the metal internal damage monitoring method based on flexible electronic sensing detection described above, and will not be repeated here.

[0114] In one exemplary embodiment, a metal internal damage monitoring device based on flexible electronic sensing detection is provided, comprising: a flexible electronic sensor array, a signal acquisition and conditioning module, a prediction module, and a visualization output module.

[0115] The flexible electronic sensor array is attached to the surface of the metal to be tested. The flexible electronic sensor array is connected to the signal acquisition and conditioning module, and the signal acquisition and conditioning module, the prediction module, and the visualization output module are connected in sequence. The flexible electronic sensor array includes a flexible substrate layer, a conductive layer, and an encapsulation layer from bottom to top.

[0116] Flexible electronic sensor arrays are used to generate electrical response signals that are associated with the internal damage state of the metal structure under test, under a preset electrical response signal acquisition method.

[0117] The signal acquisition and conditioning module is used to acquire the electrical response signal at a preset sampling frequency and extract multiple electrical characteristic parameters of the metal under test from the electrical response signal.

[0118] The prediction module is used to input multiple electrical characteristic parameters of the metal under test into the metal internal damage prediction model to obtain the damage field prediction value of the metal under test; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field, and the metal internal damage prediction model is deployed in the prediction module.

[0119] The visualization output module is used to generate an internal damage distribution map of the metal under test based on the predicted damage field value of the metal under test using a preset two-dimensional visualization algorithm.

[0120] Furthermore, to verify the effectiveness of the metal internal damage monitoring method based on flexible electronic sensing detection proposed in this application, experimental tests were conducted. A flexible electronic sensing array was attached to a metal sample exhibiting typical damage, a load was applied, and its electrical response signal was recorded. Figure 10 The electrical response curves corresponding to typical damage monitoring are shown. The horizontal axis represents the normalized position of the sensing path, and the vertical axis represents the signal amplitude (such as the change in normalized impedance). Figure 10 The solid line represents the baseline signal when no internal damage has occurred, while the dashed line represents the test signal under the condition of preset internal damage (such as cracks or pores). A comparison shows that the curve under the damage condition shows a difference in the local area corresponding to the damage location (such as...). Figure 10 Significant deviations are observed at the peaks / troughs of the dashed lines. This deviation directly reflects the alteration of signal path characteristics caused by internal defects disturbing the conductive network, effectively characterizing the impact of internal cracks or pores on the electrical channels. Experiments demonstrate that the metal internal damage monitoring method based on flexible electronic sensing detection proposed in this application can effectively identify the initiation and propagation of internal damage, with positioning errors within engineering tolerances, meeting the application requirements for metal structure health monitoring.

[0121] Beneficial effects:

[0122] 1) The flexible electronic sensor array can be directly attached to the surface of the metal structure and can work without stopping or disassembling, realizing in-situ online monitoring. It can achieve long-term and continuous online monitoring, capture the dynamic process of damage initiation and expansion, significantly improve the timeliness and convenience of monitoring, and avoid the time window omission and safety risks caused by periodic offline detection.

[0123] 2) By constructing a metal internal damage prediction model, the extracted electrical characteristic parameters are mapped to the spatial distribution of internal damage (damage field). This not only qualitatively determines whether damage exists, but also quantitatively reflects the location, extent (spatial coordinates), severity (damage intensity / probability), and even morphological parameters of the damage, providing accurate quantitative basis for structural health assessment.

[0124] 3) By using flexible substrates (such as polyimide, elastomer film) and flexible adhesive layers (such as silicone, flexible epoxy resin), the flexible electronic sensor array can fit metal surfaces with different curvatures, adapt to the deformation of the structure under load, ensure the tightness of the interface between the sensor and the monitoring structure and the stability of signal transmission, and improve the reliability and durability in complex working conditions and dynamic environments.

[0125] 4) It supports two electrical signal acquisition methods: active excitation (applying current or strain) and passive response (collecting self-excited vibration or stress wave signals). The active mode can provide controlled damage response characteristics with a high signal-to-noise ratio; the passive mode can capture the natural response of the structure under the actual working state. The modes can be flexibly selected or combined according to different monitoring scenarios and needs, which enhances the universality and robustness of the method.

[0126] 5) The output damage field data is used to generate an intuitive damage distribution map through two-dimensional visualization algorithms such as pseudo-color maps and contour maps. This clearly shows the location, range and trend of internal cracks or holes, which greatly facilitates engineering technicians to quickly assess the damage level, judge the structural safety status, and guide maintenance decisions.

[0127] 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.

[0128] 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.

[0129] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the methods, apparatus, 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 method for monitoring internal damage in metals based on flexible electronic sensing and detection, characterized in that, The flexible electronic sensor array comprises a flexible substrate, a conductive layer, and an encapsulation layer from bottom to top; The conductive layer employs a silver nanowire or graphene composite conductive network; The method for monitoring internal metal damage based on flexible electronic sensing detection includes: A flexible electronic sensor array is attached to the surface of the metal under test, and an electrical response signal acquisition method is adopted. Based on the flexible electronic sensor array, an electrical response signal associated with the internal damage state of the metal structure under test is obtained; the preset electrical response signal acquisition method is a passive response method. The electrical response signal is acquired at a preset sampling frequency, and multiple electrical characteristic parameters of the metal under test are extracted from the electrical response signal; the electrical characteristic parameters include impedance amplitude, frequency offset and propagation delay; Multiple electrical characteristic parameters of the metal to be tested are input into the metal internal damage prediction model to obtain the damage field prediction value of the metal to be tested; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field; the damage field is a two-dimensional grid corresponding to the sensing array, and each grid cell corresponds to a damage probability value. Using a pre-defined two-dimensional visualization algorithm, an internal damage distribution map of the metal under test is generated based on the predicted damage field value of the metal under test. Using a preset electrical response signal acquisition method, based on the flexible electronic sensor array, an electrical response signal associated with the internal damage state of the metal structure under test is obtained, specifically including: The flexible electronic sensor array is excited to generate an electrical response signal associated with the internal damage state of the metal structure under test by utilizing the self-excited vibration or stress wave propagation of the metal structure under test.

2. The method for monitoring internal metal damage based on flexible electronic sensing detection according to claim 1, characterized in that, Multiple electrical characteristic parameters of the metal under test are extracted from the electrical response signal, specifically including: The electrical response signal is filtered to obtain a filtered electrical response signal; Feature extraction is performed on the filtered electrical response signal to obtain multiple electrical characteristic parameters of the metal under test.

3. The method for monitoring internal metal damage based on flexible electronic sensing detection according to claim 1, characterized in that, The metal internal damage prediction model is constructed based on the finite element simulation method or trained using a training set to train a neural network model.

4. The method for monitoring internal metal damage based on flexible electronic sensing detection according to claim 3, characterized in that, The process of constructing a metal internal damage prediction model based on the finite element method specifically includes: Construct an initial finite element model that includes the geometry of the metal under test, material properties, and the placement of the flexible electronic sensor array; In the initial finite element model, different preset virtual damages are defined and applied respectively; Electro-mechanical coupling field analysis was performed on the initial finite element model with preset virtual damage. The electrical response signal generated by the flexible electronic sensor array under each preset virtual damage condition was simulated and calculated, and the corresponding virtual electrical characteristic parameters were extracted from it. The description parameters of each preset virtual damage are associated with its corresponding virtual electrical feature parameters to construct a virtual damage-electrical feature parameter mapping dataset; A preset algorithm is used to obtain the metal internal damage prediction model based on the virtual damage-feature parameter mapping dataset.

5. The method for monitoring internal metal damage based on flexible electronic sensing and detection according to claim 4, characterized in that, The process of training a neural network model using a training set to obtain a metal internal damage prediction model specifically includes: Construct a training set; the training set includes the electrical characteristic parameters of metal specimens with known internal damage states and the corresponding true values ​​of the loss field; Build a neural network model; The electrical characteristic parameters of a metal sample with known internal damage state are used as input, and the corresponding known true value of the loss field is used as output to train the neural network model. Training is stopped when the loss function reaches its minimum value or the number of training rounds reaches its maximum value, thus obtaining the metal internal damage prediction model.

6. The method for monitoring internal metal damage based on flexible electronic sensing detection according to claim 1, characterized in that, The flexible electronic sensor array is a conductive composite network structure.

7. A metal internal damage monitoring device based on flexible electronic sensing detection, characterized in that, The metal internal damage monitoring device based on flexible electronic sensing detection is used to implement the metal internal damage monitoring method based on flexible electronic sensing detection as described in any one of claims 1-6. The metal internal damage monitoring device based on flexible electronic sensing detection includes: a flexible electronic sensor array, a signal acquisition and conditioning module, a prediction module, and a visualization output module. A flexible electronic sensor array is attached to the surface of the metal to be tested. The flexible electronic sensor array is connected to a signal acquisition and conditioning module, and the signal acquisition and conditioning module, prediction module, and visualization output module are connected in sequence. The flexible electronic sensor array includes a flexible substrate layer, a conductive layer, and an encapsulation layer from bottom to top. The conductive layer adopts a silver nanowire or graphene composite conductive network. The flexible electronic sensor array is used to generate an electrical response signal associated with the internal damage state of the metal structure under test, under a preset electrical response signal acquisition method; the preset electrical response signal acquisition method is a passive response method. The signal acquisition and conditioning module is used to acquire the electrical response signal at a preset sampling frequency and extract multiple electrical characteristic parameters of the metal under test from the electrical response signal; the electrical characteristic parameters include impedance amplitude, frequency offset and propagation delay; The prediction module is used to input multiple electrical characteristic parameters of the metal to be tested into the metal internal damage prediction model to obtain the damage field prediction value of the metal to be tested; the metal internal damage prediction model is a model about the mapping relationship between electrical characteristic parameters and damage field, and the metal internal damage prediction model is deployed in the prediction module; the damage field is a two-dimensional grid corresponding to the sensing array, and each grid cell corresponds to a damage probability value; The visualization output module is used to generate an internal damage distribution map of the metal under test based on the damage field prediction value of the metal under test using a preset two-dimensional visualization algorithm. Using a preset electrical response signal acquisition method, based on the flexible electronic sensor array, an electrical response signal associated with the internal damage state of the metal structure under test is obtained, specifically including: The flexible electronic sensor array is excited to generate an electrical response signal associated with the internal damage state of the metal structure under test by utilizing the self-excited vibration or stress wave propagation of the metal structure under test.

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