Interface Interaction State Recognition Method, Device, Terminal, and Storage Medium

Abstract

The present invention provides a method, apparatus, terminal and storage medium for identifying an interface interaction state, relating to the technical field of material deformation measurement. The method includes: obtaining multiple images of a metal sample at different tensile stages during in-situ tensile; wherein, the metal sample has been surface modified and fluorescence labeled; identifying the interface region between carbide and metal matrix in each image; identifying the interface interaction state between carbide and metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, so as to obtain the monitoring result of carbide evolution of the metal sample based on the interface interaction state between carbide and metal matrix in each image. The present invention can realize the monitoring of the dynamic evolution of the interface interaction affected by carbide during the tensile process.

Description

Technical Field

[0001] The present invention relates to the technical field of material deformation measurement, and particularly to a method, device, terminal and storage medium for identifying the interface interaction state. Background Art

[0002] In-situ tensile testing is a novel experimental method currently. By applying force and measuring the in-situ strain of materials, it reveals the behavior of materials during the tensile process. This method is widely used in the research of materials science, engineering and other related fields. In an in-situ tensile test, not only can the morphological characteristics of the sample at the moment of fracture be recorded and photographed, but also the physical quantities of the sample during the tensile process can be controlled in real time, and the force applied to the surface of the sample can be read in real time through a mechanical sensor, and the stress-strain curve can be plotted to explore the sample properties from multiple dimensions.

[0003] However, currently, for the bonding state between carbides and the matrix, the stress-strain curve and the fracture surface state in the in-situ tensile test are usually analyzed to indirectly infer the interface state. For the behaviors such as deformation, cracking and interaction with the matrix of materials under tensile external forces, there is still a lack of intuitive and accurate in-situ observation means. Traditional tensile tests can only obtain macroscopic mechanical property data, and it is difficult to observe the real-time evolution at the microscopic level of carbides, which limits the in-depth understanding of the failure mechanism of carbide-containing materials. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, terminal and storage medium for identifying the interface interaction state to solve the problem of real-time monitoring of the evolution at the microscopic level of carbides.

[0005] In a first aspect, embodiments of the present invention provide a method for identifying the interface interaction state, including:

[0006] Obtaining multiple images at different tensile stages during the in-situ tensile process of a metal sample; wherein, the metal sample has been surface modified and fluorescently labeled;

[0007] Identifying the interface region between carbides and the metal matrix in each image;

[0008] Based on the fluorescence intensity of the interface region in each image, identifying the interface interaction state between carbides and the metal matrix in the metal sample, so as to obtain the monitoring result of the carbide evolution of the metal sample based on the interface interaction state between carbides and the metal matrix in each image.

[0009] In a possible implementation manner, identifying the interface region between carbides and the metal matrix in each image includes:

[0010] Identifying the interface line between carbides and the metal matrix in each image through an edge detection algorithm;

[0011] Expand the interface lines between the carbides and the metal matrix in each image to both sides to obtain the interface regions between the carbides and the metal matrix in each image.

[0012] In a possible implementation, identifying the fluorescence intensity of the interface region in each image includes:

[0013] For each image, perform grayscale processing on the image, and use the grayscale value of each pixel in the processed image as the fluorescence intensity of the pixel. Use the average grayscale value of the interface region in each image as the fluorescence intensity of the interface region in the image.

[0014] In a possible implementation, based on the fluorescence intensity of the interface region in each image, identifying the interface interaction state between the carbides and the metal matrix in the image includes:

[0015] Based on the fluorescence intensity of the interface region in each image and the shooting time, draw a curve of the fluorescence intensity changing with time;

[0016] Based on the curve of the fluorescence intensity changing with time, calculate the change amplitude of the fluorescence intensity, the fluorescence change rate, and the fluorescence fluctuation frequency in the target period, and perform weighted summation to obtain the interface interaction state index of the metal sample in the target period;

[0017] Based on the interface interaction state index of the metal sample in the target period and the preset interface interaction state index range, determine the interface interaction state of the metal sample in the target period.

[0018] In a possible implementation, before identifying the interface interaction state between the carbides and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, it further includes:

[0019] Perform in-situ tensile experiments on multiple different metal samples, and record the corresponding interface interaction state index values under each interface interaction state;

[0020] Based on the interface interaction state index values corresponding to each interface interaction state, determine the interface interaction state index range corresponding to each interface interaction state.

[0021] In a possible implementation, after identifying the interface interaction state between the carbides and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, it further includes:

[0022] Identify the stress distribution nephogram, mechanical property curve, and crack information on the surface of the metal sample in each image;

[0023] Input the stress distribution nephogram, interface interaction data, mechanical property curve, and crack information corresponding to each image into the strain deviation model to evaluate the degree of local strain concentration caused by carbides in the metal sample.

[0024] In a second aspect, an embodiment of the present invention provides an interface interaction state recognition device, including:

[0025] An acquisition module, configured to acquire multiple images at different tensile stages during the in-situ tensile process of the metal sample; wherein, the metal sample has undergone surface modification and fluorescence labeling;

[0026] An identification module, configured to identify the interface region between the carbide and the metal matrix in each image;

[0027] An analysis module, configured to identify the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity in the interface region of each image, so as to obtain the monitoring result of the carbide evolution of the metal sample based on the interface interaction state between the carbide and the metal matrix in each image.

[0028] In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0029] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0030] An embodiment of the present invention provides a method, device, terminal, and storage medium for recognizing the interface interaction state. By using the characteristics that the interface region between the carbide and the metal matrix will produce relative displacement, microcrack initiation and other phenomena under force, resulting in special changes in the fluorescence signal marked on the metal matrix side, the distribution of interface fluorescence intensity in multiple images during the in-situ tensile process of the metal sample is extracted. The interface interaction state between the carbide and the metal matrix is recognized through the change characteristics of the fluorescence intensity, realizing the monitoring of the dynamic evolution of the interface interaction affected by the carbide during the tensile process. Finally, the real-time visualization of the carbide evolution process and its influence on the matrix and mechanical properties can be achieved, breaking through the limitations of previous ex-post analysis. Description of the Drawings

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for description in the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 is a flowchart of the implementation of the interface interaction state recognition method provided by an embodiment of the present invention;

[0033] Figure 2 is a schematic structural diagram of the interface interaction state recognition device provided by an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Detailed implementation manners

[0035] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0036] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0037] In-situ tensile testing is a novel experimental method that reveals the behavior of materials during the tensile process by applying force and measuring the in-situ strain of the materials. This method is widely used in the research of materials science, engineering, and other related fields. In an in-situ tensile test, not only can the morphological characteristics of the sample at the moment of fracture be recorded and photographed, but also the physical quantities of the sample during the tensile process can be controlled in real time, and the force applied to the surface of the sample can be read in real time through a mechanical sensor to plot the stress-strain curve, exploring the properties of the sample from multiple dimensions.

[0038] Fluorescence probe microscopy is a microscopy technique that uses fluorescence probes to observe and analyze samples. Fluorescence probes are a class of fluorescent molecules that have characteristic fluorescence in the ultraviolet-visible-near-infrared region, and their fluorescence properties (excitation and emission wavelengths, intensity, lifetime, polarization, etc.) can change sensitively with the properties of the surrounding environment.

[0039] When materials are subjected to tensile external forces, there is still a lack of intuitive and precise in-situ observation methods for the deformation, cracking, and interaction with the matrix of carbides. Traditional tensile tests can only obtain macroscopic mechanical property data, making it difficult to observe the real-time evolution at the microscopic level of carbides, which limits the in-depth understanding of the failure mechanism of carbide-containing materials.

[0040] To address the above problems, the present invention includes the design of a fluorescence probe-assisted in-situ tensile machine for precise analysis of the dynamic characteristics of carbides during tensile processes. The present invention aims to provide a new analysis method and device that utilize the specific labeling and optical signal feedback characteristics of fluorescence probes, combined with the real-time loading function of the in-situ tensile machine, to achieve dynamic microscopic analysis of carbides throughout the tensile process, accurately capture key changes such as their structure and stress distribution, reduce errors caused by manual identification, and add a video recording function to better evaluate the positive and negative effects of carbides on mechanical properties at different deformation stages and different strains. At the same time, the influence of carbides on the matrix structure can be observed through the fluorescence points of the matrix. Finally, a model is used to automatically evaluate the evolution process of carbides and their influence on the matrix and mechanical properties. Ultimately, in-situ and real-time visualization of the carbide tensile process is achieved, breaking through the limitations of ex-post analysis in the past.

[0041] See Figure 1 , which shows the implementation flowchart of the interface interaction state recognition method provided by the embodiment of the present invention, and is described in detail as follows:

[0042] Step 101, obtain multiple images at different tensile stages during the in-situ tensile process of a metal sample; wherein, the metal sample has been surface-modified and fluorescence-labeled.

[0043] In this embodiment, the metal surface is modified in the early stage of monitoring, and a fluorescence probe is used to label the metal matrix to highlight the position, shape, distribution, etc. of the carbides, achieving intuitive positioning of the carbides and replacing traditional manual macroscopic judgment. The specific implementation process can be as follows:

[0044] 1. Surface modification design

[0045] a) Prepare a 4% nitric acid alcohol etching solution and etch the sample surface for 30 s;

[0046] b) Put the etched sample into a beaker containing absolute ethanol for ultrasonic cleaning. After cleaning, take it out and dry it for plasma treatment for surface modification. Use oxygen plasma to treat the sample for 20 minutes to increase the surface polarity and hydrophilicity, which is beneficial for the subsequent attachment of 3-aminopropyltriethoxysilane (APTES). Immerse the treated sample in an ethanol solution containing APTES for surface functionalization to introduce amino groups, and there is no reaction or a low degree of reaction on the carbide surface.

[0047] c) Heat to 25 °C and keep warm for 20 minutes. Take out the sample, wash it repeatedly with absolute ethanol and dry it in an oven at 80 °C for 30 minutes of aging to ensure complete curing of APTES.

[0048] d) Add fluorescein isothiocyanate (FITC) solution to the prepared 1-(3-methylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) solution with a concentration of 5 mM and N-hydroxysuccinimide (NHS) solution with a concentration of 10 mM. The ratio of FITC to EDC / NHS is 1:10. Heat to 25 °C in a beaker and stir ultrasonically for 20 minutes for activation. Activating the fluorescein enables the isothiocyanate group in FITC to react with the functionalized metal matrix surface with amino groups to form covalent bonds.

[0049] e) Conduct fluorescein labeling using fluorescein isothiocyanate (FITC). Put the functionalized sample into the activated FITC solution, stir ultrasonically in a beaker at 25 °C for 1 hour for labeling. Finally, take out the sample, wash the surface with absolute ethanol to remove unreacted fluorescein, and dry it in an oven at 25 °C.

[0050] 2. Conduct in-situ tensile testing on the metal sample, equipped with an in-situ tensile machine, a high-precision force sensor and a displacement sensor, and precisely control the tensile rate and load magnitude. Around the loading platform of the tensile machine, arrange a high-sensitivity fluorescence microscope and a spectral analyzer. During the tensile process, the fluorescence microscope captures the fluorescence imaging of the fluorescent probe in real time, and the spectral analyzer synchronously records the peak positions and intensity changes of the fluorescence spectra. Connect the fluorescence microscope and the in-situ tensile machine through a computer to build a platform to collect images during the tensile process.

[0051] 3. Analysis of the carbide evolution process

[0052] Images or videos of carbide evolution in the initial stage, elastic deformation stage, yield stage, strengthening stage, maximum strength stage, and necking stage can be collected according to the tensile stages, or image information under different stresses (stresses: 5%, 10%, 15%... until fracture) can be collected. Before identification, these images can also be denoised to remove noise and the miscellaneous points generated by ambient light interference, and at the same time enhance the image contrast to make the boundaries of the fluorescent regions clearer.

[0053] Step 102, identify the interface regions between carbides and the metal matrix in each image.

[0054] In this embodiment, during the stretching process, relative displacement, microcrack initiation and other phenomena occur in the interface region between the carbide and the metal matrix due to the applied force, resulting in special changes in the fluorescence signal marked on the metal matrix side. The image recognition algorithm can be used to accurately lock the interface contour, extract the curve of the fluorescence intensity near the interface changing with time, and identify the interface interaction state between the carbide and the metal matrix through the change characteristics of the fluorescence intensity, so as to monitor the dynamic evolution of the interface interaction affected by the carbide during the stretching process.

[0055] Step 103: Based on the fluorescence intensity of the interface region in each image, identify the interface interaction state between the carbide and the metal matrix in the metal sample, so as to obtain the monitoring result of the carbide evolution of the metal sample based on the interface interaction state between the carbide and the metal matrix in each image.

[0056] In this embodiment, during normal stretching, the interface is well bonded and the fluorescence intensity changes smoothly; if the carbide is debonded from the matrix, the fluorescence intensity at the interface will drop suddenly or the fluctuation will intensify; when tiny cracks or local slip begin to appear at the interface, the change rate of the fluorescence intensity will increase.

[0057] Compare the changes in the interface fluorescence intensity of the same material sample at different stretching stages. During the elastic deformation stage, the change in fluorescence intensity is usually small and regular; when entering the plastic deformation stage, especially when approaching the yield point of the material, the interface interaction may change and the fluctuation of the fluorescence intensity will increase significantly. Through this comparison, the dynamic evolution of the interface interaction affected by the carbide during the stretching process can be clearly observed.

[0058] By comparing the interface fluorescence curves at different stretching times and different regions, the change amplitude, change rate and fluctuation frequency of the fluorescence intensity can be selected as the determination factors for the interface interaction state.

[0059] The embodiment of the present invention utilizes the characteristics that relative displacement, microcrack initiation and other phenomena occur in the interface region between the carbide and the metal matrix due to the applied force, resulting in special changes in the fluorescence signal marked on the metal matrix side, extracts the interface fluorescence intensity distribution in multiple images during the in-situ stretching process of the metal sample, and identifies the interface interaction state between the carbide and the metal matrix through the change characteristics of the fluorescence intensity, so as to monitor the dynamic evolution of the interface interaction affected by the carbide during the stretching process. Finally, the evolution process of the carbide and its influence on the matrix and mechanical properties can be realized in real-time visualization, breaking through the limitations of previous ex-post analysis.

[0060] In a possible implementation manner, identifying the interface region between the carbide and the metal matrix in each image includes:

[0061] Identifying the interface line between the carbide and the metal matrix in each image through an edge detection algorithm;

[0062] Expand the interface lines between carbides and the metal matrix in each image to both sides to obtain the interface regions between carbides and the metal matrix in each image.

[0063] In this embodiment, the interface lines between carbides and the metal matrix are identified in each image through the Canny edge detection algorithm, and then the interface region range can be determined by expanding 5 - 10 pixel distances to both sides along the interface lines. The expanded distance can be adjusted according to the actual situation.

[0064] In a possible implementation, identifying the fluorescence intensity of the interface region in each image includes:

[0065] For each image, perform grayscale processing on the image, and use the grayscale value of each pixel in the processed image as the fluorescence intensity of the pixel, and use the grayscale mean value of the interface region in each image as the fluorescence intensity of the interface region in the image.

[0066] In this embodiment, the collected images can be imported into ImageJ, and the images are converted to grayscale mode, and the grayscale values can reflect the fluorescence intensity.

[0067] In a possible implementation, based on the fluorescence intensity of the interface region in each image, identifying the interface interaction state between carbides and the metal matrix in the image includes:

[0068] Based on the fluorescence intensity of the interface region in each image and the shooting time, draw a curve of the fluorescence intensity changing with time;

[0069] Based on the curve of the fluorescence intensity changing with time, calculate the change amplitude, fluorescence change rate, and fluorescence fluctuation frequency of the fluorescence intensity in the target period, and perform weighted summation to obtain the interface interaction state index of the metal sample in the target period;

[0070] Based on the interface interaction state index of the metal sample in the target period and the preset interface interaction state index range, determine the interface interaction state of the metal sample in the target period.

[0071] In this embodiment, specifically, the change amplitude, change rate, and fluctuation frequency can be added according to the weight of 1:1:1 to obtain a value that can comprehensively reflect the interface interaction state.

[0072] In a possible implementation, before identifying the interface interaction state between carbides and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, it further includes:

[0073] Perform in-situ tensile experiments on multiple different metal samples, and record the corresponding interface interaction state index values under each interface interaction state;

[0074] Based on the interface interaction state index values corresponding to the states of interaction between each interface, determine the interface interaction state index range corresponding to each interface interaction state.

[0075] In this embodiment, through calibration of a large number of experiments under different material systems and tensile conditions, determine the range of this value under different interface interaction states (for example, 0 - 0.3 indicates good bonding, 0.3 - 0.7 indicates partial debonding, 0.7 - 1 indicates severe debonding), thereby quantifying the weakening or strengthening effect of carbides on the interface bonding strength.

[0076] In a possible implementation manner, after identifying the interface interaction state between carbides and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, it further includes:

[0077] Identify the stress distribution nephogram, mechanical property curve, and crack information on the surface of the metal sample in each image;

[0078] Input the stress distribution nephogram, interface interaction data, mechanical property curve, and crack information corresponding to each image into a strain deviation model to evaluate the degree of local strain concentration caused by carbides in the metal sample.

[0079] In this embodiment, during the tensile process of the material, the stress distribution nephogram, interface interaction data, mechanical property curve, and crack information are closely related: the stress distribution affects the interface interaction, and the interface bonding state in turn changes the stress transmission path. The two jointly determine the mechanical properties, and the generation and development of cracks are the result of the combined action of these factors, reflecting the failure trend of the material. The strain deviation model integrates this information, constructs a comprehensive evaluation system, analyzes the influence mechanism and weight of each factor, and comprehensively evaluates the evolution of carbides and their impact on material properties.

[0080] 1. Stress distribution nephogram: The stress concentration region is a weak point of the material, prone to crack initiation, and affects the strength and lifespan of the material. The greater the stress concentration coefficient, the higher the risk of material failure. By analyzing the characteristics of the stress distribution nephogram, the internal stress state and potential failure regions of the material can be judged.

[0081] 2. Interface interaction data: Good interface bonding enables uniform stress transmission and enhances the material properties; interface debonding or slip leads to stress concentration and reduces the material strength. The interface bonding strength and debonding degree affect the mechanical properties of the material and crack propagation. Measuring the interface bonding strength and observing the debonding situation can evaluate the influence of interface interaction on material properties.

[0082] 3. Stress-strain curve: The elastic modulus reflects the ability of a material to resist elastic deformation, the yield strength represents the stress at which the material begins plastic deformation, and the tensile strength reflects the maximum stress that the material can withstand. Key parameters are obtained from the mechanical property curve to intuitively understand the macroscopic mechanical properties and deformation behavior of the material.

[0083] 4. Crack information: Crack initiation and propagation are important processes for material failure. The crack length, propagation rate, and direction reflect the degree of material damage and the failure trend. The longer the crack length and the faster the propagation rate, the shorter the remaining life of the material. Monitoring crack information can evaluate the damage state of the material and predict the failure time.

[0084] The stress distribution contour map, interface interaction data, mechanical property curves, and crack information can be integrated to measure the change in the uniformity of the overall deformation of the metal matrix in regions with and without, aggregated or sparse, and different-sized carbides. These information are input into the final model to automatically evaluate the strain deviation in different regions, and then evaluate the degree of local strain concentration caused by carbides, and determine whether the carbides promote or hinder the cooperative deformation of the metal matrix.

[0085] The implementation methods of the final strain deviation model include:

[0086] 1. Data processing: Convert the stress distribution contour map into a numerical matrix, and extract features such as the coordinates of the stress concentration region and the stress amplitude; perform normalization processing on the interface interaction data to unify the dimension and numerical range; digitize the mechanical property curves and extract characteristic parameters such as the elastic modulus and yield strength; quantify the crack information, such as converting the crack length and propagation angle into numerical values.

[0087] 2. Build a neural network model: The nodes in the input layer correspond to the data features after preprocessing. The number of layers and nodes in the hidden layer are determined according to the data complexity, and the output layer is the comprehensive evaluation result.

[0088] 3. Model training and optimization: Collect a large amount of multi-source data under different materials and tensile conditions, label the corresponding evaluation results, and form a training data set. Use optimization algorithms such as stochastic gradient descent and Adam to adjust the model parameters through backpropagation, minimize the error between the prediction result and the actual evaluation result, and improve the accuracy and generalization ability of the model. Use cross-validation technology to divide the training data into multiple subsets, alternately train and validate the model, evaluate the model performance, and select the optimal model parameters.

[0089] 4. Model Evaluation and Application: Evaluate the trained model using the test dataset, calculate metrics such as accuracy, recall, mean squared error, etc., and judge the model performance. If the model performance does not meet the standard, adjust the model structure, parameters, or data preprocessing method, and retrain and evaluate. Apply the validated model to the evaluation of actual material tensile data, and output evaluation results such as the degree of influence of carbides on the mechanical properties of the material and the material failure risk level, providing a decision-making basis for material optimization design and performance improvement.

[0090] Specifically, the steps for drawing the stress distribution contour map can include:

[0091] Import the collected image into ImageJ, convert the image to grayscale mode, and the grayscale value can reflect the fluorescence intensity. Then, select the "Measure" option under the "Analyze" menu, and the software will pop up a result window showing various parameters of the selected area, including the average value (Mean) and standard deviation (StdDev) of the grayscale value, etc.

[0092] Divide the processed image into fine (1mm×1mm) grid areas, and calculate the fluorescence intensity grid by grid. By comparing the fluorescence intensity differences between adjacent grids, calculate the intensity gradient (in the stress concentration area, the fluorescence intensity gradient is often larger).

[0093] Adjacent grids refer to the grids that are directly adjacent in the horizontal or vertical direction in the divided image grid. For a grid located in the i-th row and j-th column, its adjacent grids are the grids in the (i - 1)-th row and j-th column, (i + 1)-th row and j-th column, i-th row and (j - 1)-th column, and i-th row and (j + 1)-th column (provided that these grids are within the image range). When calculating the fluorescence intensity gradient, mainly consider the fluorescence intensity differences between these adjacent grids.

[0094] Table 1

[0095]

[0096] Table 1 is an example of the fluorescence intensity of the image after grid division. In this example, each square represents a 1mm×1mm grid area, and the value inside the square represents the calculated fluorescence intensity gradient value of this area (the values here are assumed for illustration). It can be seen from the figure that the intensity gradient values vary in different areas. For example, the intensity gradients of some grids in the middle part are relatively large, indicating the existence of stress concentration and the influence of carbides in these areas.

[0097] For each 1mm×1mm grid area, its fluorescence intensity value is obtained through the above operations. Assuming that Iij represents the fluorescence intensity of the grid in the i-th row and j-th column (where i and j are the row and column indices of the grid in the image), then Iij is obtained by selecting pixel points within the grid area and calculating their grayscale average value.

[0098] The physical environment where the fluorescent probe used to label the metal matrix will change significantly due to stress concentration. The stress concentration area is accompanied by local plastic deformation, which will cause the lattice around the fluorescent probe molecules to distort. The luminescence characteristics of fluorescent substances are closely related to the spatial conformation of molecules and intermolecular interactions. The lattice distortion causes the energy level structure of fluorescent molecules to be disturbed, and the energy difference between the originally stable excited state and the ground state will change, thereby directly affecting the intensity of fluorescence emission.

[0099] According to the theory of mechanics of materials, input the fluorescence intensity gradient data into a pre-constructed numerical conversion model. This model is trained with images and stress-strain curves at different stages, and combines the fluorescence-stress coupling law of the experimental metal materials. It can convert the fluorescence intensity gradient into the corresponding stress value, and then draw the stress distribution nephogram of the metal matrix at each stage of tension, and the stress anomaly at the location of the carbide is thus highlighted.

[0100] The neural network model can effectively handle the complex non-linear relationship between fluorescence intensity and stress value. During the tension process, the factors affecting the fluorescence intensity include the morphology, distribution, and crystal structure change of carbides, as well as the deformation and stress concentration of the matrix, which makes the relationship between fluorescence intensity and stress present a complex relationship. The neural network constructs a complex network structure through a large number of neurons and can automatically learn the complex patterns and rules in the data.

[0101] In addition, because in different stages of material tension, such as elastic deformation, yield, strengthening, necking, etc., there are differences in the interaction mechanism between carbides and the matrix and the stress-strain distribution law, the relationship between fluorescence intensity and stress is not exactly the same. In the elastic stage, the material deformation is mainly elastic, and the fluorescence intensity is more sensitive to stress changes and shows a linear correlation trend; while in the necking stage, the local deformation of the material is concentrated and the stress distribution is complex, and the relationship between fluorescence intensity and stress will become complex. Constructing models separately can better analyze the characteristics of different stages and improve the accuracy.

[0102] The training data for the neural network is sourced from data collection during in-situ tensile experiments on different material samples. It covers a variety of carbide-containing materials, such as alloy steels and non-ferrous metal alloys with different compositions. For each material sample, multiple sets of data are collected during the tensile process, including fluorescence microscope image data (fluorescence intensity information), spectral analyzer data (fluorescence spectral characteristics), and mechanical sensor data (stress and strain values) at different tensile stages (elastic, yield, strengthening, etc.) and different stress levels.

[0103] The force applied to the sample is measured in real-time by a high-precision force sensor. Combining with the original size information of the sample, the stress value of the sample at each moment is obtained. These stress values are matched with the fluorescence intensity data collected at the same moment to obtain sample labels. At each moment of the tensile experiment, there are corresponding fluorescence intensity data and stress data. By associating the stress value at the same moment with the fluorescence intensity data, this stress value becomes the sample label for the corresponding fluorescence intensity data. By collecting a large number of such paired data at different tensile stages and different stress levels, a dataset with sample labels for model training is constructed.

[0104] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0105] The following is an apparatus embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0106] Figure 2 The structural schematic diagram of the interface interaction state recognition apparatus provided by the embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0107] As Figure 2 shown, the interface interaction state recognition apparatus 2 includes:

[0108] An acquisition module 21, configured to acquire multiple images at different tensile stages during the in-situ tensile process of a metal sample; wherein, the metal sample has undergone surface modification and fluorescence labeling;

[0109] An identification module 22, configured to identify the interface region between the carbide and the metal matrix in each image;

[0110] An analysis module 23, configured to identify the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, so as to obtain the carbide evolution monitoring result of the metal sample based on the interface interaction state between the carbide and the metal matrix in each image.

[0111] In a possible implementation, the recognition module 22 is specifically configured to:

[0112] Identify the interface line between the carbide and the metal matrix in each image through an edge detection algorithm;

[0113] Expand the interface line between the carbide and the metal matrix in each image to both sides to obtain the interface region between the carbide and the metal matrix in each image.

[0114] In a possible implementation, the analysis module 23 is specifically configured to:

[0115] For each image, perform gray-scale processing on the image, and use the gray-scale value of each pixel in the processed image as the fluorescence intensity of the pixel, and use the average gray-scale value of the interface region in each image as the fluorescence intensity of the interface region in the image.

[0116] In a possible implementation, the analysis module 23 is specifically configured to:

[0117] Based on the fluorescence intensity of the interface region in each image and the shooting time, draw a curve of the fluorescence intensity changing with time;

[0118] Based on the curve of the fluorescence intensity changing with time, calculate the change amplitude, fluorescence change rate and fluorescence fluctuation frequency of the fluorescence intensity in the target period, and perform weighted summation to obtain the interface interaction state index of the metal sample in the target period;

[0119] Based on the interface interaction state index of the metal sample in the target period and the preset range of the interface interaction state index, determine the interface interaction state of the metal sample in the target period.

[0120] In a possible implementation, the analysis module 23 is further configured to:

[0121] Before identifying the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, perform in-situ tensile experiments on a variety of different metal samples, and record the corresponding interface interaction state index values under each interface interaction state;

[0122] Based on the corresponding interface interaction state index values under each interface interaction state, determine the range of the interface interaction state index corresponding to each interface interaction state.

[0123] In a possible implementation, the analysis module 23 is further configured to:

[0124] After identifying the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, identify the stress distribution nephogram, mechanical property curve and crack information on the surface of the metal sample in each image;

[0125] Input the stress distribution nephogram, interface interaction data, mechanical property curve, and crack information corresponding to each image into the strain deviation model to evaluate the degree of local strain concentration induced by carbides in the metal sample.

[0126] In an embodiment of the present invention, due to the relative displacement, microcrack initiation, etc. that occur in the interface region between carbides and the metal matrix under force, the fluorescence signal marked on the metal matrix side exhibits special changes. The distribution of interface fluorescence intensity in multiple images during the in-situ tensile process of the metal sample is extracted. The interface interaction state between carbides and the metal matrix is identified through the change characteristics of fluorescence intensity, realizing the monitoring of the dynamic evolution of the interface interaction affected by carbides during the tensile process. Finally, the evolution process of carbides and the real-time visualization of their influence on the matrix and mechanical properties can be achieved, breaking through the limitations of previous ex-post analysis.

[0127] Figure 3 is a schematic diagram of the terminal provided by an embodiment of the present invention. As Figure 3 shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the embodiments of the above various interface interaction state recognition methods are implemented, such as Figure 1 the steps 101 to 103 shown. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above device embodiments are implemented, such as Figure 2 the functions of the modules / units 21 to 23 shown.

[0128] Exemplarily, the computer program 32 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 can be divided into Figure 2 the modules / units 21 to 23 shown.

[0129] The terminal 3 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand, Figure 3This is only an example of the terminal 3, which does not constitute a limitation on the terminal 3. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0130] The so-called processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0131] The memory 31 may be an internal storage unit of the terminal 3, such as the hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both the internal storage unit and the external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0132] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0133] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0134] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0135] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in an electrical, mechanical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0138] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-described embodiments of the method for identifying the interaction states of each interface. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0139] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for identifying an interface interaction state, characterized in that: include: Acquiring multiple images of a metal sample at different stretching stages during in-situ stretching; wherein the metal sample is surface-modified and fluorescently labeled; identifying the interface region between carbide and metal matrix in each image; Based on the fluorescence intensity of the interface area in each image, the interface interaction state between the carbide and the metal matrix in the metal sample is identified, so as to obtain the carbide evolution monitoring result of the metal sample based on the interface interaction state between the carbide and the metal matrix in each image; The step of identifying the interface interaction state between the carbide and the metal matrix in each image based on the fluorescence intensity of the interface region in the image comprises: A curve of fluorescence intensity versus time is drawn based on the fluorescence intensity of the interface area in each image and the shooting time; Based on the curve of the fluorescence intensity changing with time, the fluorescence intensity change amplitude, fluorescence change rate and fluorescence fluctuation frequency in the target time period are calculated, and weighted summation is performed to obtain the interface interaction state index of the metal sample in the target time period; Based on the interface interaction state index of the metal sample in the target time period and a preset interface interaction state index range, the interface interaction state of the metal sample in the target time period is determined.

2. The method for identifying the interface interaction state according to claim 1, characterized in that: The step of identifying the interface region between the carbide and the metal matrix in each image comprises: The interface line between carbide and metal matrix is ​​identified in each image by edge detection algorithm; The interface line between the carbide and the metal matrix in each image is extended to both sides to obtain the interface area between the carbide and the metal matrix in each image.

3. The method for identifying the interface interaction state according to claim 1, characterized in that: Identify the fluorescence intensity of the interface region in each image, including: For each image, grayscale processing is performed on the image, and the grayscale value of each pixel in the processed image is used as the fluorescence intensity of the pixel, and the grayscale mean value of the interface area in each image is used as the fluorescence intensity of the interface area in the image.

4. The method for identifying the interface interaction state according to claim 1, characterized in that: Before identifying the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, the method further includes: Conduct in-situ tensile tests on a variety of different metal samples and record the corresponding interface interaction state index values ​​under each interface interaction state; Based on the interface interaction state index value corresponding to each interface interaction state, the interface interaction state index range corresponding to each interface interaction state is determined.

5. The method for identifying the interface interaction state according to claim 1, characterized in that: After identifying the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface area in each image, the method further includes: Identify stress distribution cloud diagrams, mechanical property curves and crack information on the surface of the metal sample in each image; The stress distribution cloud map, interface interaction data, mechanical property curve and crack information corresponding to each image are input into the strain deviation model to evaluate the local strain concentration degree caused by carbides on the metal sample.

6. An interface interaction state identification device, characterized in that: include: An acquisition module, used to acquire multiple images of the metal sample at different stretching stages during in-situ stretching; wherein the metal sample is surface-modified and fluorescently labeled; An identification module for identifying the interface region between carbide and metal matrix in each image; An analysis module, for identifying the interface interaction state between the carbide and the metal matrix in the metal sample based on the fluorescence intensity of the interface region in each image, so as to obtain the carbide evolution monitoring result of the metal sample based on the interface interaction state between the carbide and the metal matrix in each image; The analysis module is specifically used for: A curve of fluorescence intensity versus time is drawn based on the fluorescence intensity of the interface area in each image and the shooting time; Based on the curve of the fluorescence intensity changing with time, the fluorescence intensity change amplitude, fluorescence change rate and fluorescence fluctuation frequency in the target time period are calculated, and weighted summation is performed to obtain the interface interaction state index of the metal sample in the target time period; Based on the interface interaction state index of the metal sample in the target time period and a preset interface interaction state index range, the interface interaction state of the metal sample in the target time period is determined.

7. The interface interaction state recognition device according to claim 6, characterized in that: The identification module is specifically used for: The interface line between carbide and metal matrix is ​​identified in each image by edge detection algorithm; The interface line between the carbide and the metal matrix in each image is extended to both sides to obtain the interface area between the carbide and the metal matrix in each image.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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