A method for positioning micro-cracks on a metal material surface

Abstract

The application provides a metal material surface micro-crack positioning method, comprising the following steps: applying a torsional load to a numerical model, calculating stress and strain distribution inside the material, adopting a crystal plasticity constitutive equation to describe crystal deformation behavior, and obtaining dislocation density distribution and slip system activation in each grain; judging a position inside the material prone to forming a micro-crack according to the dislocation density distribution and the slip system activation, and determining that the position is prone to forming the micro-crack when the dislocation density exceeds a preset critical value and multiple activated slip systems exist; judging whether the micro-crack extends according to fracture mechanics parameters and material fracture toughness, and determining that the micro-crack extends when a stress intensity factor exceeds the fracture toughness, and determining a crack propagation path by calculating a maximum release rate direction.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a metal material surface micro-crack positioning method. BACKGROUND

[0002] In the field of metal material science and engineering, accurate positioning of micro-cracks on the surface of metal materials has always been a key link in research and application, especially under complex conditions of bearing torsional load. When the material is subjected to torsional action, the complex changes in internal stress and strain state, such as the activity of dislocations, the activation of slip planes, and the plastic flow of grain boundaries, have a profound impact on the formation and propagation path of micro-cracks. However, the current understanding of the interaction between these micro-mechanisms and macro-crack behavior is still in its infancy, especially in multi-phase materials, where stress concentration at different phase interfaces and material anisotropy regulate the crack propagation path, making the problem even more complex. Furthermore, in high-temperature environments, dynamic recrystallization and microstructure evolution of the material further exacerbate the uncertainty of micro-crack positioning. In the face of these challenges, how to build a multi-scale mechanical model that bridges the micro-to-macro scale, closely linking the micro-mechanisms of torsional deformation with macro-crack behavior, to achieve high-precision positioning of micro-cracks, has become a major issue in materials science research. In summary, in the metal material surface micro-crack positioning method, it is a technical problem to be solved to deeply explore the internal relationship between micro-mechanisms and macro-crack behavior under torsional deformation and to build a multi-scale mechanical model that can accurately predict micro-crack behavior. SUMMARY

[0003] The present application provides a metal material surface micro-crack positioning method, mainly comprising:

[0004] According to the microstructure characteristics and chemical composition of the metal material, a three-dimensional digital model of the material is established, and the three-dimensional digital model is meshed using the finite element method to obtain a numerical model containing microstructure information of grains, grain boundaries, and first phase particles;

[0005] A torsional load is applied to the numerical model, the stress and strain distribution inside the material is calculated, the crystal plasticity constitutive equation is used to describe the crystal deformation behavior, and the dislocation density distribution and slip system activation in each grain are obtained;

[0006] According to the dislocation density distribution and slip system activation, the position inside the material prone to form micro-cracks is determined, when the dislocation density exceeds the preset critical value and there are multiple activated slip systems, the position is determined as the position prone to form micro-cracks;

[0007] A meso-mechanical model containing cracks is established for the determined position prone to micro-cracks, the meso-mechanical model combines grain orientation, grain boundary characteristics and first phase distribution, the extended finite element method is adopted to simulate the crack propagation process, and the stress intensity factor at the crack tip and the J integral fracture mechanics parameter are obtained;

[0008] According to the fracture mechanics parameter and the fracture toughness of the material, it is judged whether the micro-crack is expanded or not, when the stress intensity factor exceeds the fracture toughness, it is determined that the micro-crack is expanded, and the crack propagation path is determined by calculating the maximum release rate direction of the J integral;

[0009] The amplitude, energy and frequency characteristic parameters of the acoustic emission signal are obtained by using the acoustic emission detection method to monitor the material surface in real time, the signal is analyzed by wavelet transform, the acoustic characteristics of micro-crack initiation and propagation are obtained, and the position of the acoustic emission source is preliminarily estimated according to the time difference of the acoustic emission signal;

[0010] According to the time-frequency characteristics of the acoustic emission signal, the preliminarily estimated acoustic source position and the micro-crack propagation path obtained by numerical simulation, the specific position of the micro-crack is determined by using the triangular positioning algorithm, the acoustic source coordinates are determined by iterative calculation through the time difference of the acoustic emission signals received by multiple sensors, and the distribution of the micro-crack on the material surface is obtained by comparing multiple positioning results.

[0011] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0012] The present application discloses a kind of metal material surface micro-crack positioning method.The method first establishes the three-dimensional digital model of material, and the internal stress and strain distribution and dislocation density are calculated by finite element analysis and crystal plasticity constitutive equation, to determine the position prone to micro-crack.Then, a meso-mechanical model containing cracks is established, and the extended finite element method is used to simulate crack propagation to obtain fracture mechanics parameters.Meanwhile, the acoustic emission detection technology is used to monitor the material surface in real time, and the acoustic characteristics of micro-crack initiation and propagation are obtained.Finally, the specific position and distribution of the micro-crack are determined by combining the numerical simulation results and acoustic emission signals with the triangular positioning algorithm.The present application realizes the accurate prediction and positioning of metal material micro-crack at microscale, and provides a new method for material failure analysis and life assessment. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 It is a flow chart of the metal material surface micro-crack positioning method of the present application.

[0014] Figure 2 It is a schematic diagram of the metal material surface micro-crack positioning method of the present application.

[0015] Figure 3 It is still another schematic diagram of the metal material surface micro-crack positioning method of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and in detail below with reference to the drawings in the embodiments of the present application. The described embodiments are only some of the embodiments of the present application.

[0017] As Figure 1 -3, the metal material surface micro-crack positioning method can specifically include:

[0018] S101, according to the microstructure characteristics and chemical composition of the metal material, a three-dimensional digital model of the material is established, and a finite element method is used to divide the three-dimensional digital model into grids to obtain a numerical model containing microstructure information of grains, grain boundaries and first phase particles.

[0019] The microstructure scanning electron microscope image and the chemical composition data of the metal material are obtained, edge detection and region growing are performed according to the microstructure scanning electron microscope image to obtain the position, shape and size information of the grains, grain boundaries and first phase particles. According to the chemical composition data, the mechanical property parameters corresponding to different element contents are queried from the material database to determine the material attribute parameters of each phase. A three-dimensional reconstruction method based on cross-section interpolation is used to construct a three-dimensional digital model of the metal material according to the position, shape and size information. The three-dimensional digital model is discretized into a digital representation composed of tetrahedral elements by a tetrahedral element division method, and the corresponding material attribute parameters are assigned to the tetrahedral elements. The digital representation is meshed, and an adaptive mesh refinement algorithm based on stress gradient is used to increase the grid density in the area with large stress gradient to obtain a finite element grid model containing microstructure information. According to the finite element grid model, boundary conditions and load conditions are set, and a Johnson-Cook model is used to establish a constitutive equation describing the mechanical behavior of the metal material. Based on the nonlinear finite element solving process of the Newton-Raphson iteration method, the micro stress and strain distribution of the metal material is calculated, and the deformation and fracture behavior of the metal material is judged.

[0020] Illustratively, the microstructure scanning electron microscope image and chemical composition data of the metal material are obtained, the microstructure image is segmented and feature extracted through edge detection and region growing algorithm, the positions, shapes and sizes of the grains, grain boundaries and first phase particles are identified, the mechanical property parameters corresponding to different element contents are queried from the material database according to the chemical composition data, and the material attribute parameters of each phase are determined. According to the extracted microstructure feature information, a three-dimensional digital model of the metal material is constructed by using a three-dimensional reconstruction method based on cross-section interpolation, the continuous geometric model is discretized into a digital representation composed of a large number of tetrahedral elements by using a tetrahedral element division method, and each tetrahedral element is assigned with corresponding material attributes. The three-dimensional digital model is meshed, and an adaptive mesh refinement algorithm based on stress gradient is used to increase the grid density in the area with large stress gradient, and the mesh is refined in the stress concentration areas such as grain boundaries and second phase particles, thereby generating a finite element grid model containing microstructure information. Based on the generated finite element grid model, the boundary conditions and load conditions are set, the Johnson-Cook model is used to describe the plastic deformation behavior of the material, the constitutive equation is established to describe the mechanical behavior of the material, the nonlinear finite element solving process based on Newton-Raphson iteration method is used to calculate the micro stress and strain distribution, and the deformation and fracture behavior of the material is predicted. The microstructure scanning electron microscope image of the metal material is obtained, the image resolution is 2048x2048 pixels, and the gray level is 256 levels. The Canny edge detection algorithm is used to extract the grain boundary contour, and the threshold is set to low threshold 50 and high threshold 150. The region growing algorithm is used to identify the grains, the seed point is selected as the local minimum gray value point, and the growth threshold is set to 10. The first phase particles are extracted through morphological operation, and the opening operation kernel size is 3x3 pixels. The chemical composition data is obtained, including the element contents of C0.4%, Mn0.7%, Si0.3%, Cr1.0%, etc. The mechanical property parameters corresponding to different element contents are queried from the material database, such as elastic modulus 210GPa and yield strength 500MPa. Based on the extracted microstructure features, the three-dimensional reconstruction is performed by using the cross-section interpolation method. The cross-section interval is set to 0.1μm, and a total of 100 layers are reconstructed, thereby generating a three-dimensional digital model of 10μm×10μm×10μm. The tetrahedral element division method is used, and the element size is set to 0.05μm, thereby generating about 8 million tetrahedral elements. According to the material attribute parameters, each tetrahedral element is assigned with corresponding attribute value. The three-dimensional digital model is meshed, and the adaptive mesh refinement algorithm based on stress gradient is used. The initial grid size is set to 0.1μm, and the stress gradient threshold is set to 100MPa / μm. When the local stress gradient exceeds the threshold, the grid size is reduced by half. Within a range of 5 units around the grain boundaries and first phase particles, the mesh refinement is forced, and the minimum grid size is limited to 0.01μm. Finally, a finite element grid model containing about 12 million elements is generated.Based on the generated finite element mesh model, the boundary conditions are set as fixed at the bottom surface and 100 MPa uniform pressure applied at the top surface. Johnson-Cook model is used to describe the plastic deformation behavior of the material, with the model parameters set as the initial yield stress A = 500 MPa, B = 600 MPa, strain hardening exponent n = 0.4, temperature softening coefficient C = 0.015, and temperature softening exponent m = 1.0. B is a parameter related to strain hardening, reflecting the degree of material hardening with the increase of plastic strain. Newton-Raphson iteration method is used to solve the nonlinear equations, and the convergence criterion is set as the residual less than 1e-6. The micro stress-strain distribution is calculated by 100 loading steps, and the iteration number is limited to 20 times per step. According to the von Mises equivalent stress distribution, it is judged whether the element has plastic deformation. When the equivalent stress exceeds 700 MPa, it is considered that the region has fracture. Finally, the deformation and fracture behavior prediction results of the material under load are obtained.

[0021] S102, a torsional load is applied to the numerical model, the stress-strain distribution inside the material is calculated, the crystal plasticity constitutive equation is used to describe the crystal deformation behavior, and the dislocation density distribution and slip system activation in each grain are obtained.

[0022] The continuum is discretized into element grids by using the finite element discretization method, and the global stiffness matrix and load vector are established according to the element grids. The nonlinear equations are solved by Newton-Raphson iteration, and if the norm of the residual vector is less than the preset threshold, the node displacement field inside the material is obtained. According to the node displacement field, the strain tensor of each element is calculated, and the stress tensor is also calculated. For each grain, the decomposition shear stress of the applied stress on the slip plane is calculated, and if the decomposition shear stress exceeds the critical decomposition shear stress, the corresponding slip system is activated. The dislocation density evolution on each slip system is calculated by using the Orowan equation, and the dislocation density change rate is determined by the dislocation multiplication rate and the annihilation rate. According to the dislocation density evolution, the microstructure evolution information in the material deformation process is obtained, including the activation of slip system and the distribution of dislocation density.

[0023] Example, in the numerical model, the torsion load is set by applying opposite torques on both ends of the cylindrical specimen. The continuum is discretized into a mesh of elements using the finite element method, and a global stiffness matrix and load vector are established. The nonlinear equations are solved using the Newton-Raphson iteration method, and the residual vector is calculated at each iteration. Convergence is considered to be achieved when the residual norm is less than a predetermined threshold, and the node displacement field within the material is obtained. The strain tensor of each element is calculated based on the node displacement field, and the stress tensor is calculated using the rate-dependent crystal plasticity constitutive equation, which takes into account the hardening effect, including self-hardening and cross-hardening. For each grain, the resolved shear stress on the slip plane is calculated. When the resolved shear stress exceeds the critical resolved shear stress, the corresponding slip system is activated. The critical resolved shear stress is related to the dislocation density through the hardening relationship. The plastic shear strain increment on the activated slip system is calculated, and the crystal orientation is updated. The dislocation density evolution on each slip system is calculated using the Orowan equation, and the dislocation density change rate is determined by the dislocation multiplication rate and annihilation rate. The Frank-Read source multiplication and dislocation loop expansion mechanisms are considered, as well as dislocation intersection and annihilation processes. The dislocation density distribution within each grain is updated, and the microstructure evolution information during material deformation is obtained, including the activation of slip systems and the dislocation density distribution. A cylindrical specimen with a diameter of 10 mm and a height of 30 mm is subjected to a torsion torque of 1000 N·m, and the mesh is divided into 20-node hexahedral elements, resulting in a total of 50,000 elements. The Newton-Raphson iteration method is used to solve the nonlinear equations, with a maximum iteration number of 50 and a convergence threshold of 1e-6. In each iteration, the residual vector is calculated and the stiffness matrix is updated until convergence is achieved or the maximum number of iterations is reached. After obtaining the node displacement field, the strain tensor of each element is calculated. The rate-dependent crystal plasticity constitutive equation is used, taking into account the hardening effect, with a hardening modulus of 1000 MPa. The crystal orientation matrix is constructed using Euler angles (φ1, Φ, φ2), where φ1 ∈ [0, 2π], Φ ∈ [0, π], and φ2 ∈ [0, 2π], uniformly randomly distributed. For face-centered cubic metals, 12 {111} <110> slip systems are considered, i.e., the {111} family of planes, with slip directions in the <110> direction. The resolved shear stress on each slip system is calculated, and the initial critical resolved shear stress is set to 100 MPa. When the resolved shear stress exceeds the critical value, the slip system is activated, and the plastic shear strain increment is calculated. The Taylor hardening relationship is used to update the critical resolved shear stress, with a Taylor factor of 1.1. The Orowan equation is used to calculate the dislocation density evolution, with an initial dislocation density of 1e12 m-2. The Frank-Read source multiplication and dislocation loop expansion are considered, with a multiplication coefficient of 0.1 and an annihilation coefficient of 10.

[0024] S103, determining the position prone to form micro-cracks in the material according to the dislocation density distribution and the slip system activation. When the dislocation density exceeds the preset threshold value and there are multiple activated slip systems, it is determined that the position is prone to form micro-cracks.

[0025] The dislocation density distribution data of each region of the material is obtained, the dislocation density distribution data stores the dislocation density value of each grid cell in a three-dimensional array; a dislocation density threshold value is calculated according to the dislocation density distribution data; it is judged whether the dislocation density value of each grid cell exceeds the dislocation density threshold value, if it exceeds, the grid cell is marked as a potential micro-crack formation region; the slip system activation of each grain is read, the slip system activation records the activated slip system number in each grain in a two-dimensional array; the number of activated slip systems in each grain is counted, if the number of activated slip systems is greater than or equal to a preset threshold value, the grain is marked as a multi-slip system activation region; according to the marking results of the potential micro-crack formation region and the multi-slip system activation region, a Boolean array representing the position prone to form micro-cracks is generated; the local stress and strain state of the position prone to form micro-cracks is calculated, the local stress and strain state includes dislocation density gradient, grain boundary misfit degree and local stress triaxiality; a micro-crack formation probability model is established according to the local stress and strain state, the micro-crack formation probability model is used to calculate a micro-crack formation index; combined with the grain boundary characteristics and the grain orientation data, a three-dimensional distribution map of the micro-crack prone region is generated.

[0026] Exemplarily, the dislocation density distribution data of each region of the material is obtained, and the dislocation density value of each grid unit is stored in a three-dimensional array. Based on the yield strength and shear modulus of the material, the Taylor relationship is used to calculate the critical value of the dislocation density. If the dislocation density exceeds the calculated critical value, the unit is marked as a potential microcrack formation area. The slip system activation status of each grain is read, and the number of activated slip systems in each grain is recorded using a two-dimensional array. The number of activated slip systems is counted, and the threshold value of the number of activated slip systems is set to 3. If the number of activated slip systems is greater than or equal to 3, the grain is marked as a multi-slip system activation area. Based on the marking results of the dislocation density distribution and the slip system activation status, the corresponding positions of the elements in the dislocation density supercritical array and the multi-slip system activation array are compared using a logical AND operation. A new Boolean array is generated to represent the locations where microcracks are easily formed, and these areas are determined as locations where microcracks are easily formed. For the identified locations where microcracks are easily formed, the local stress and strain state is calculated. A microcrack formation probability model was established, taking into account factors such as dislocation density gradient, grain boundary mismatch, and local stress triaxiality. Weight coefficients were set to comprehensively consider these factors and calculate the microcrack formation index. A three-dimensional distribution map of microcrack-prone areas was generated by combining grain boundary characteristics and grain orientation data. Dislocation density values ​​for each region of the material were stored in a 100×100×100 three-dimensional array, ranging from 1e10 to 1e15 m². Assuming a yield strength σy of 300 MPa and a shear modulus G of 80 GPa, the critical dislocation density value was calculated using the Taylor relation ρc = (σy / (αGb))², where the constant α = 0.5 and the modulus b of the Burgers vector = 2.5e-10 m, resulting in a critical value of 1.4e14 m². A 3D convolution kernel (3×3×3) was used to scan the dislocation density array, marking regions exceeding the critical value. Simultaneously, the activation status of 12 possible slip systems was recorded in a 50×50×50 two-dimensional array, represented by 0s and 1s. A sliding window method was used to count the number of activated slip systems in each grain, with a window size of 5×5×5. When the number of activated slip systems within the window was ≥3, the central element was marked as a multi-slip system active region. An element-by-element logical AND operation was performed on the two labeled arrays to generate a 100×100×100 Boolean array representing locations prone to microcrack formation. The local stress-strain state at these locations was calculated, taking into account the dislocation density gradient with a threshold of 1e13m^-2 / μm, the grain boundary misfit with a critical angle of 5°, and the local stress triaxiality with a critical value of 0.6. Weights of 0.4, 0.3, and 0.3 were assigned to these three factors, respectively, to calculate a comprehensive microcrack formation index. Finally, based on the calculated results, the microcrack formation probability was marked with different colors in three-dimensional space to generate a three-dimensional heat map of the microcrack-prone regions.

[0027] S104, for the determined position prone to micro-crack formation, a meso-mechanical model containing a crack is established. The meso-mechanical model combines grain orientation, grain boundary characteristics and first phase distribution, adopts an extended finite element method to simulate a crack propagation process, and obtains a crack tip stress intensity factor and a J integral fracture mechanics parameter.

[0028] A Voronoi partition method is adopted to construct a grain structure, and a meso-mechanical model containing a crack is obtained. Grid division is performed according to the meso-mechanical model, wherein an h self-adaptive refinement strategy is adopted in a crack tip region. For the grid division result, a constitutive equation combining crystal anisotropy is established, the constitutive equation adopts a generalized Hooke's law to describe crystal elastic behavior. A displacement field is calculated based on the constitutive equation, and a crack tip stress intensity factor is obtained from the displacement field. If the stress intensity factor is greater than a preset threshold, a domain integral method is adopted to calculate a J integral value. A crack propagation direction is determined according to the J integral value, and the crack propagation direction is determined based on a maximum circumferential stress criterion. A crack propagation rate is calculated through a Paris formula, and a crack propagation path is obtained.

[0029] For example, according to the determined position prone to micro-crack formation, a meso-mechanical model containing cracks is constructed, a Voronoi partition method is used to construct a grain structure, a boundary element method is combined to describe the grain boundary characteristics, a three-dimensional reconstruction algorithm is used to generate a geometric model containing grains, grain boundaries and first phases, and grain orientation, grain boundary characteristics and first phase distribution information are assigned to the corresponding geometric entities. Tetrahedral elements are used for meshing, an h-adaptive refinement strategy is used in the crack tip area to mesh the meso-mechanical model, a finite element mesh model containing cracks is generated, and an extension degree of freedom is introduced in the crack-containing element. A constitutive equation considering crystal anisotropy is established, the generalized Hooke's law is used to describe the elastic behavior of the crystal, the Hill yield criterion is used to represent the plastic deformation, the grain orientation information is converted into the material stiffness matrix, the boundary conditions and loads are set, and the extended finite element method is used to solve the displacement field to calculate the stress and strain distribution. Based on the displacement field, the stress intensity factor at the crack tip is calculated, the domain integral method is used to calculate the J integral value, and the path independence is considered. The maximum circumferential stress criterion is used to determine the crack propagation direction, the Paris formula is used to calculate the crack propagation rate, the crack morphology is updated and the calculation process is repeated to obtain the crack propagation path. In a 100x100x100μm3 cubic region, 500 grains are generated using the Voronoi partition method, and the average grain size is 20μm. The boundary element method is used to describe the grain boundary characteristics, the grain boundary thickness is set to 0.5μm, and the grain boundary strength is 80% of the intragranular strength. 50 spherical first phase particles are randomly distributed in the model, with a diameter range of 2-5μm. The initial crack is set to an elliptical shape with a length of 10μm and a width of 0.1μm. Tetrahedral elements are used for meshing, the initial element size is 2μm, the h-type adaptive refinement strategy is used within 5μm of the crack tip, the minimum element size is 0.1μm, and a total of about 1 million elements are generated. The generalized Hooke's law is used to describe the elastic behavior of the crystal, and for example, a face-centered cubic structure is set with elastic constants C11=204GPa, C12=137GPa, and C44=126GPa. The Hill yield criterion is used, and the initial yield strength is set to 200MPa. A tensile load of 100MPa is applied to the top of the model, and the bottom is fixed. The extended finite element method is used to solve the displacement field, and the iterative convergence accuracy is set to 1e-6. The calculated stress intensity factor at the crack tip is KI=5MPa·m0.5. The 8-point Gaussian integral is used to calculate the J integral value, and 5 integration paths are taken to verify the path independence, with a J integral value of 0.1kJ / m3. The maximum circumferential stress criterion is used to determine the crack propagation direction, and the Paris formula da / dN=C(ΔK)m is used to calculate the crack propagation rate, where the material constants C=1e-11 and m=3. a is the crack length, N is the stress cycle number, and ΔK is the stress intensity factor range, which is a measure of the stress field near the crack tip. The crack grows by 0.1μm per cycle, the crack morphology is updated, and the calculation is repeated 50 times to obtain the crack propagation path.

[0030] S105, judging whether the micro crack expands according to the fracture mechanics parameter and the fracture toughness of the material. When the stress intensity factor exceeds the fracture toughness, it is determined that the micro crack expands, and the crack propagation path is determined by calculating the maximum release rate direction of J integral.

[0031] The fracture mechanics parameter and the fracture toughness value of the material are obtained, the stress intensity factor is calculated by finite element post-processing according to the fracture mechanics parameter and the fracture toughness value, the J integral value is calculated by using the virtual displacement method, the energy release rate is obtained by selecting multiple integral paths around the crack tip for numerical integration, if the crack propagation direction is determined, the crack propagation increment is set, the crack is extended according to the maximum energy release rate direction, the crack morphology is updated by using the crack propagation increment method, the grid is regenerated and the stress field is calculated according to the updated crack morphology, the experimental observation scheme is set, the strain field of the sample surface is measured, and the accuracy of the numerical simulation result is verified according to the strain field data.

[0032] For example, the fracture mechanics parameters and the fracture toughness value of the material are obtained, the stress intensity factor is calculated by finite element post-processing, and the Irwin fracture criterion is used to determine the microcrack extension when the stress intensity factor KI at the crack tip is greater than the fracture toughness KIC of the material. The J integral value is calculated by the virtual displacement method, and a unit virtual displacement is applied to the crack tip, and the virtual power is calculated to obtain the J integral value. Multiple integral paths are selected around the crack tip, and the energy release rate is solved by numerical integration. The J integral value is calculated every 5° within the 360° range of the crack tip, and the direction corresponding to the maximum value is found. The crack extension direction is determined using the maximum energy release rate criterion. The J integral is used to describe the energy release rate at the crack tip in fracture mechanics. Based on the determined crack extension direction, the crack extension increment is set to 0.1mm, and the crack is extended in the direction of the maximum energy release rate. The crack morphology is updated using the crack extension increment method, the mesh is regenerated and the stress field is calculated, and the first two steps are repeated to obtain the complete crack extension path. An experimental observation plan is set up, and the surface strain field of the specimen is measured using in-situ tensile testing and digital image correlation technology. A high-resolution CCD camera was used to capture deformation images, and the strain field was calculated using digital image correlation software. A piezoelectric sensor array was arranged to collect acoustic emission signals and locate the crack source. The crack propagation process was recorded using acoustic emission monitoring. The experimental results were compared with the numerical simulation results to verify the accuracy of the simulation. For a surface crack with a length of 10 mm, the stress intensity factor KI = 35 MPa·m^0.5 was calculated through finite element post-processing. The fracture toughness KIC of the material was 40 MPa·m^0.5, and the Irwin fracture criterion was used to judge crack propagation. A virtual displacement of 1 μm was applied to the crack tip, and the J-integral value was calculated to be 0.15 kJ / m^2. Five integral paths were selected, with path radii of 0.1 mm, 0.2 mm, 0.3 mm, 0.4 mm, and 0.5 mm, respectively. The energy release rate was solved using the Simpson numerical integration method. The J-integral was calculated every 5° within a 360° radius of the crack tip, for a total of 72 directions. The direction 70° corresponding to the maximum J-integral of 0.18 kJ / m² was found. The crack was extended along the 70° direction with a crack propagation increment of 0.1 mm. After updating the crack morphology, a mesh consisting of approximately 500,000 tetrahedral elements was regenerated. The calculation process was repeated 10 times, resulting in a crack propagation path with a total length of 1 mm. In the experiment, a CCD camera with a resolution of 4096 × 3072 pixels and an acquisition frequency of 1 Hz was used to capture 500 deformation images. Using digital image correlation software, a subregion size of 21 × 21 pixels and a step size of 5 pixels were set, resulting in a strain field resolution of 0.01%. Eight piezoelectric sensors were deployed with a sampling frequency of 5 MHz and a preset threshold of 40 dB, recording 153 acoustic emission signals. The crack source location was determined using a triangulation algorithm, with an average deviation of 0.08 mm from the crack path predicted by numerical simulation.

[0033] S106, real-time monitoring is performed on the material surface by using acoustic emission detection method, amplitude, energy and frequency characteristic parameters of the acoustic emission signal are acquired, time-frequency analysis is performed on the signal by using wavelet transform, and acoustic characteristics of micro-crack initiation and expansion are obtained. A position of the acoustic emission source is preliminarily estimated according to a time difference of the acoustic emission signal.

[0034] The original acoustic emission signal collected by an acoustic emission sensor array arranged on the material surface is acquired; a band-pass filter is used to filter the original acoustic emission signal to obtain a first signal; wavelet threshold denoising is performed on the first signal to obtain a second signal; signal parameters including peak amplitude, rise time, duration, count, energy, average frequency and peak frequency are calculated according to the second signal; wavelet transform is performed on the second signal to obtain wavelet coefficients; a time-frequency diagram is drawn according to the wavelet coefficients to identify characteristic frequency bands and energy distribution in the micro-crack initiation and expansion process; acoustic emission source coordinates are calculated according to a time difference of the acoustic emission signal arriving at multiple sensors; if the time difference meets a preset condition, an over-determined equation set is solved by using a least square method; and a crack evolution diagram is drawn according to the acoustic emission source coordinates, the crack evolution diagram being used to track the initiation position and expansion path of the micro-crack.

[0035] Exemplarily, an acoustic emission sensor array is arranged on the surface of the material, the acoustic emission signals are continuously collected by a data acquisition card, the sampling frequency and threshold value are set, and the collected original signals are preprocessed. The original signals are processed by a band-pass filter of 50 kHz-1 MHz and a wavelet threshold denoising method to obtain clear acoustic emission waveforms. The preprocessed acoustic emission signals are feature-extracted, the amplitude, energy and frequency domain parameters of the signals are calculated, including peak amplitude, rise time, duration, count, energy, average frequency and peak frequency. The frequency spectrum of the signals is calculated by fast Fourier transform, the main frequency and frequency center are extracted, and the feature vector of the acoustic emission event is formed. The acoustic emission signals are analyzed in time and frequency by wavelet transform, Daubechies wavelet is selected based on the signal characteristics, and 5 layers of decomposition are determined according to the signal frequency range. The wavelet coefficients of the signals are calculated, the time-frequency diagram is drawn, and the characteristic frequency band and energy distribution in the micro-crack initiation and expansion process are identified. According to the time difference of the acoustic emission signals arriving at multiple sensors, the least square method is used to solve the overdetermined equation set, and the acoustic emission source coordinates are calculated. Combined with the time sequence of the acoustic emission events, the initiation position and expansion path of the micro-crack are tracked, and the crack evolution diagram is drawn. The positioning accuracy is evaluated by Monte Carlo simulation, and in the 95% confidence interval, the positioning error is less than 5 mm. Four piezoelectric acoustic emission sensors are arranged on the surface of a material of 100x100 mm3, a 16-bit A / D converter is used, the sampling frequency is set to 5 MHz, and the threshold value is set to 40 dB. The collected original signals are processed by a band-pass filter of 50 kHz-1 MHz, db4 wavelet is used for 4-layer decomposition, soft threshold denoising, and clear waveforms are obtained by reconstruction. The features of the processed signals are extracted, including peak amplitude 85 dB, rise time 25 μs, duration 150 μs, count 56, and energy 3.2x10-7 J. The frequency spectrum is calculated by 2048-point FFT, the main frequency is 320 kHz, and the frequency center is 280 kHz. Db5 wavelet is used for 5-layer decomposition, and time-frequency analysis shows that the energy of the micro-crack initiation is concentrated in the frequency band of 200-300 kHz, and the expansion process is mainly concentrated in the frequency band of 150-250 kHz. The time difference of the signals received by the four sensors is used to construct an overdetermined equation set, which is solved by the least square method to obtain the acoustic source coordinates (35.2 mm, 42.8 mm). 1000 times of Monte Carlo simulation are performed, and the positioning error in the 95% confidence interval is 3.8 mm. According to the position and time information of the continuous 100 acoustic emission events, the crack evolution diagram is drawn, which shows that the crack is initiated from the coordinates (30 mm, 40 mm) and expanded along the direction of 70° for 15 mm.

[0036] S107, according to the time-frequency characteristics of the acoustic emission signal, the preliminary estimated acoustic source position and the micro-crack propagation path obtained by numerical simulation, the specific position of the micro-crack is determined by using a triangular positioning algorithm; the acoustic source coordinates are determined by iterative calculation using the time difference of the acoustic emission signals received by multiple sensors; the distribution of the micro-crack on the material surface is obtained by comparing multiple positioning results.

[0037] The time-frequency characteristics of the acoustic emission signal are obtained, the main frequency and energy distribution of the acoustic emission signal are extracted by using short-time Fourier transform to obtain the characteristic data of the acoustic emission signal. According to the acoustic emission signals received by multiple sensors, the time difference of signal arrival is calculated by using the generalized cross-correlation function GCC-PHAT to determine the estimation accuracy of the time difference. A nonlinear equation set is constructed to describe the relationship between the acoustic source position and the time difference, and the acoustic source coordinates are obtained by solving the nonlinear equation set using Newton iteration method. Statistical analysis is performed on multiple positioning results, and if a data point deviates from the mean value by more than three standard deviations, the 3σ criterion is used to eliminate the data point. The confidence interval of the positioning result is calculated by Monte Carlo simulation to determine whether the positioning result is within the preset confidence interval.

[0038] For example, the time-frequency characteristics of the acoustic emission signal are obtained, and the main frequency and energy distribution of the signal are extracted using short-time Fourier transform. The preliminary estimated acoustic source position and the micro-crack propagation path obtained by numerical simulation are extracted from the signal database, and these data are used as input parameters of the triangular positioning algorithm.

[0039] According to the acoustic emission signals received by multiple sensors, the time difference of signal arrival is calculated, the generalized cross-correlation function GCC-PHAT is used to improve the estimation accuracy of the time difference, and a nonlinear equation set is constructed to describe the relationship between the acoustic source position and the time difference.

[0040] The nonlinear equation set is solved by using Newton iteration method, the initial value is set as the preliminary estimated position, the iteration step is 0.1 mm, and the convergence threshold is 1e-6. The acoustic source coordinates are obtained by multiple iteration calculations, and the intermediate results and final convergence coordinates of each iteration are recorded.

[0041] Statistical analysis is performed on multiple positioning results, and the 3σ criterion is used to eliminate data points deviating from the mean value by more than three standard deviations. The mean value and standard deviation are calculated, the distribution heat map of the micro-crack on the material surface is drawn, and the high probability area is marked. The 95% confidence interval of the positioning result is calculated by Monte Carlo simulation to evaluate the positioning accuracy. The positioning result is compared with the numerical simulation path for verification.

[0042] For a 100×100 mm3material surface, four acoustic emission sensors were arranged. The collected signals were processed by 1024-point short-time Fourier transform with a time window length of 0.2 ms and an overlap ratio of 50%. The main frequency range was 200-300 kHz, and the peak energy was 3.5×10-7J. The preliminary estimated acoustic source location (45 mm, 55 mm) and the numerically simulated crack path were obtained from the database. The GCC-PHAT algorithm was used to calculate the signal time difference with a sampling frequency of 5 MHz and a cross-correlation window length of 1024 points, and the time difference accuracy was 0.2 μs. Four nonlinear equations were constructed, and the Newton iteration method was used for solving with an initial value of (45 mm, 55 mm), an iteration step of 0.1 mm, a convergence threshold of 1e-6, and a maximum iteration number of 100. After 50 iterations, the acoustic source coordinates (46.3 mm, 54.8 mm) were obtained. The positioning process was repeated 100 times, 5 abnormal values were removed using the 3σ criterion, and the average value of the remaining 95 effective results was (46.2 mm, 54.9 mm) with a standard deviation of (0.3 mm, 0.2 mm). A heat map was drawn to show that 90% of the positioning results were concentrated in a 2 mm×2 mm area. Through 10,000 Monte Carlo simulations, the 95% confidence interval was calculated to be ±0.6 mm. The positioning results were compared with the numerically simulated path, and the average deviation was 0.4 mm with a maximum deviation of 0.9 mm.

[0043] The above is merely an example and a description of the structure of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as they do not deviate from the structure of the present application or exceed the scope defined by the present claims, which shall belong to the protection scope of the present application.

Claims

1. A method for positioning micro-cracks on a surface of a metal material, characterized by, The method comprises: According to the microstructure characteristics and chemical composition of the metal material, a three-dimensional digital model of the material is established, the three-dimensional digital model is meshed using the finite element method, and a numerical model containing the microstructure information of the grains, grain boundaries and first phase particles is obtained; A torsional load is applied to the numerical model, the stress and strain distribution inside the material is calculated, the crystal plasticity constitutive equation is used to describe the crystal deformation behavior, and the dislocation density distribution and slip system activation in each grain are obtained; According to the dislocation density distribution and slip system activation, the position inside the material where microcracks are prone to form is determined, and when the dislocation density exceeds a preset critical value and there are multiple activated slip systems, the position is determined as the position where microcracks are prone to form; For the determined position where microcracks are prone to form, a mesoscopic mechanics model containing a crack is established, the mesoscopic mechanics model combines the grain orientation, grain boundary characteristics and first phase distribution, and the extended finite element method is used to simulate the crack propagation process to obtain the stress intensity factor and J integral fracture mechanics parameters at the crack tip; According to the fracture mechanics parameters and the fracture toughness of the material, it is judged whether the microcrack propagates or not, and when the stress intensity factor exceeds the fracture toughness, it is determined that the microcrack propagates, and the crack propagation path is determined by calculating the maximum release rate direction of the J integral; The surface of the material is monitored in real time by using the acoustic emission detection method, the amplitude, energy and frequency characteristic parameters of the acoustic emission signal are obtained, the signal is analyzed by wavelet transform, the acoustic characteristics of microcrack initiation and propagation are obtained, and the position of the acoustic emission source is preliminarily estimated according to the time difference of the acoustic emission signal; According to the time-frequency characteristics of the acoustic emission signal, the preliminarily estimated position of the acoustic source and the microcrack propagation path obtained by numerical simulation, the specific position of the microcrack is determined by using the triangular positioning algorithm, the acoustic emission signal time difference received by multiple sensors is used to determine the acoustic source coordinates by iterative calculation, and the distribution of the microcrack on the surface of the material is obtained by comparing multiple positioning results.

2. The method of claim 1, wherein, According to the microstructure characteristics and chemical composition of the metal material, a three-dimensional digital model of the material is established, the three-dimensional digital model is meshed using the finite element method, and a numerical model containing the microstructure information of the grains, grain boundaries and first phase particles is obtained, which comprises: Obtaining the microstructure scanning electron microscope image and chemical composition data of the metal material, performing edge detection and region growing on the microstructure scanning electron microscope image to obtain the position, shape and size information of the grains, grain boundaries and first phase particles; According to the chemical composition data, the mechanical property parameters corresponding to different element contents are queried from the material database to determine the material attribute parameters of each phase; A three-dimensional reconstruction method based on cross-section interpolation is used to construct a three-dimensional digital model of the metal material according to the position, shape and size information; The three-dimensional digital model is discretized into a digital representation composed of tetrahedral elements by a tetrahedral element division method, and the corresponding material attribute parameters are assigned to the tetrahedral elements; The digital representation is meshed, and an adaptive mesh refinement algorithm based on stress gradient is used to increase the grid density in the area with large stress gradient to obtain a finite element grid model containing microstructure information; According to the finite element mesh model, boundary conditions and load cases are set, and a constitutive equation describing mechanical behavior of the metal material is established by using a Johnson-Cook model; Based on a nonlinear finite element solving process of a Newton-Raphson iteration method, micro stress and strain distribution of the metal material is calculated, and deformation and fracture behavior of the metal material is judged.

3. The method of claim 1, wherein, The numerical model is subjected to a torsional load, stress and strain distribution inside the material is calculated, a crystal plasticity constitutive equation is used to describe crystal deformation behavior, and dislocation density distribution and slip system activation in each grain are obtained, including: A finite element discretization method is used to discretize a continuum into element meshes, and a global stiffness matrix and a load vector are established according to the element meshes; By solving the nonlinear equation set through the Newton-Raphson iteration, if the norm of the residual vector is less than a preset threshold, the node displacement field inside the material is obtained; According to the node displacement field, the strain tensor of each element is calculated, and the stress tensor is calculated at the same time; For each grain, the decomposed shear stress of the applied stress on the slip plane is calculated, and if the decomposed shear stress exceeds the critical decomposed shear stress, the corresponding slip system is activated; The dislocation density evolution on each slip system is calculated by using an Orowan equation, and the dislocation density change rate is determined by the dislocation multiplication rate and the annihilation rate; According to the dislocation density evolution, microstructure evolution information in the material deformation process is obtained, and the microstructure evolution information includes the activation of the slip system and the dislocation density distribution.

4. The method of claim 1, wherein, According to the dislocation density distribution and the slip system activation, the position inside the material prone to form microcracks is judged; when the dislocation density exceeds a preset critical value and there are multiple activated slip systems, the position is determined as the position prone to form microcracks, including: Dislocation density distribution data of each region of the material is obtained, and the dislocation density distribution data stores the dislocation density value of each grid element by using a three-dimensional array; The dislocation density critical value is calculated according to the dislocation density distribution data; It is judged whether the dislocation density value of each grid element exceeds the dislocation density critical value, and if it exceeds, the grid element is marked as a potential microcrack formation region; The slip system activation of each grain is read, and the slip system activation records the activated slip system number in each grain by using a two-dimensional array; The number of activated slip systems in each grain is counted, and if the number of activated slip systems is greater than or equal to a preset threshold, the grain is marked as a multi-slip system activation region; According to the marking results of the potential microcrack formation region and the multi-slip system activation region, a Boolean type array representing the position prone to form microcracks is generated; Local stress and strain state of the position prone to form microcracks is calculated, and the local stress and strain state includes dislocation density gradient, grain boundary misfit degree and local stress triaxiality; According to the local stress and strain state, a microcrack formation probability model is established, and the microcrack formation probability model is used to calculate a microcrack formation index; Combined with grain boundary characteristics and grain orientation data, a three-dimensional distribution map of a microcrack prone region is generated.

5. The method of claim 1, wherein, The micro-mechanical model containing cracks is established for the determined position prone to micro-crack formation; the micro-mechanical model combines grain orientation, grain boundary characteristics and first phase distribution, and adopts an extended finite element method to simulate a crack propagation process, to obtain crack tip stress intensity factor and J integral fracture mechanics parameters, including: A Voronoi partition method is adopted to construct a grain structure, to obtain a micro-mechanical model containing cracks; According to the micro-mechanical model, meshing is performed, wherein an h self-adaptive refinement strategy is adopted in a crack tip region; For the meshing result, a constitutive equation combining crystal anisotropy is established, and the constitutive equation adopts a generalized Hooke's law to describe crystal elastic behavior; Based on the constitutive equation, a displacement field is calculated, and a crack tip stress intensity factor is obtained from the displacement field; If the stress intensity factor is greater than a preset threshold, a domain integral method is adopted to calculate a J integral value; According to the J integral value, a crack propagation direction is determined, and the crack propagation direction is determined based on a maximum circumferential stress criterion; A crack propagation rate is calculated through a Paris formula, to obtain a crack propagation path.

6. The method of claim 1, wherein, According to fracture mechanics parameters and material fracture toughness, it is determined whether a micro-crack propagates or not; When the stress intensity factor exceeds the fracture toughness, it is determined that the micro-crack propagates, a crack propagation path is determined by calculating a maximum release rate direction of the J integral, including: Fracture mechanics parameters and material fracture toughness values are obtained, and a stress intensity factor is calculated through finite element post-processing based on the fracture mechanics parameters and the fracture toughness values; A J integral value is calculated by using a virtual displacement method, and an energy release rate is obtained by selecting a plurality of integral paths around a crack tip to perform numerical integration; If the crack propagation direction is determined, a crack propagation increment is set, and the crack is extended according to a maximum energy release rate direction; A crack morphology is updated by using a crack propagation increment method, a grid is regenerated and a stress field is calculated according to the updated crack morphology; An experimental observation scheme is set, a strain field of a sample surface is measured, and the accuracy of a numerical simulation result is verified according to the strain field data.

7. The method of claim 1, wherein, The material surface is monitored in real time by using an acoustic emission detection method, amplitude, energy and frequency characteristic parameters of an acoustic emission signal are obtained, time-frequency analysis of the signal is performed by using a wavelet transform, and acoustic characteristics of micro-crack initiation and propagation are obtained; According to a time difference of arrival of the acoustic emission signal, a position of an acoustic emission source is preliminarily estimated, including: Original acoustic emission signals collected by an acoustic emission sensor array are obtained, and the acoustic emission sensor array is arranged on a material surface; The original acoustic emission signals are filtered by using a band-pass filter, to obtain a first signal; The first signal is subjected to wavelet threshold denoising processing, to obtain a second signal; Signal parameters are calculated according to the second signal, and the signal parameters include peak amplitude, rise time, duration, count, energy, average frequency and peak frequency; The second signal is subjected to wavelet transform, to obtain wavelet coefficients; A time-frequency diagram is drawn according to the wavelet coefficients, and characteristic frequency bands and energy distribution in a micro-crack initiation and propagation process are identified; Acoustic emission source coordinates are calculated according to a time difference of arrival of the acoustic emission signal at a plurality of sensors. If the time difference meets preset conditions, a least square method is used to solve the over-determined equation set; A crack evolution map is drawn according to the acoustic emission source coordinates, and the crack evolution map is used to track the initiation position and propagation path of the micro-crack.

8. The method of claim 1, wherein, The specific position of the micro-crack is determined by using a triangular positioning algorithm according to the time-frequency characteristics of the acoustic emission signal, the preliminarily estimated acoustic source position and the micro-crack propagation path obtained by numerical simulation; the acoustic source coordinates are determined by using the time difference of the acoustic emission signals received by the multiple sensors and through iterative calculation; The distribution of the micro-crack on the material surface is obtained by comparing multiple positioning results, including: The time-frequency characteristics of the acoustic emission signal are obtained, the main frequency and energy distribution of the acoustic emission signal are extracted by using a short-time Fourier transform, and the characteristic data of the acoustic emission signal are obtained; The time difference of the signal arrival is calculated by using a generalized cross-correlation function GCC-PHAT according to the acoustic emission signals received by the multiple sensors, and the estimation accuracy of the time difference is determined; A nonlinear equation set is constructed to describe the relationship between the acoustic source position and the time difference, and the nonlinear equation set is solved by using a Newton iteration method to obtain the acoustic source coordinates; Statistical analysis is performed on the multiple positioning results, and if a data point deviates from the mean value by more than three standard deviations, the data point is removed according to the 3σ criterion; The confidence interval of the positioning result is calculated by Monte Carlo simulation, and it is judged whether the positioning result is within the preset confidence interval.

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