Amorphous material shear band prediction method, device, equipment and medium

Abstract

The invention discloses an amorphous material shear band prediction method and device, equipment and a medium, and relates to the technical field of material science. Comprising the following steps: acquiring microstructure data of a target amorphous material, and constructing an initial microscopic model of the target amorphous material according to the microstructure data; the microstructure data of the target amorphous material comprises atomic arrangement, local bonding information and density distribution data; calculating a rigidity gradient field in the target amorphous material based on the initial microscopic model, and determining a shear-expansion transition region in the target amorphous material based on the rigidity gradient field; and generating a shear-expansion transition region prediction model corresponding to the target amorphous material according to the shear-expansion transition region, and generating a shear band prediction result based on the shear-expansion transition region prediction model. Therefore, the randomness of a deep learning technology in amorphous material shear band prediction can be overcome, and the position of the amorphous material shear band can be efficiently and accurately predicted.

Description

Technical Field

[0001] The present invention relates to the field of material science and technology, and in particular to a method, device, equipment and medium for predicting shear bands of amorphous materials. Background Art

[0002] Amorphous materials such as metallic glass have high strength and high elastic limit. They are high-end intelligent functional materials with broad application prospects. They have attracted much attention due to their unique long-range disordered atomic structure. However, due to the formation of local shear bands, metallic glass has poor toughness. Shear bands are narrow bands formed by local strain concentration during plastic deformation, which are usually the main cause of failure of glass materials. Therefore, accurately predicting the formation location of shear bands is crucial to improving the toughness of metallic glass.

[0003] In recent years, with the advancement of computer technology and artificial intelligence, researchers have begun to use artificial intelligence technologies such as deep learning to predict the location of shear band formation in metallic glass. A deep learning model based on convolutional neural network (CNN) was proposed. The model accurately identifies areas where shear dilatancy transition may be formed by analyzing the initial static structure of metallic glass. Through convolutional neural network training, the model can identify "fertile sites" with high plastic deformation potential in metallic glass. The density distribution of these fertile sites (Density of Fertile Sites, DFS) can effectively predict the starting position of the shear band. In addition, the initial distribution of DFS can not only be used to predict shear bands, but also to determine whether the material exhibits brittleness or ductility when subjected to stress.

[0004] Although the CNN model shows high accuracy in predicting the shear band position, its prediction is somewhat random and non-physical, especially in slowly cooled amorphous materials, where the formation of shear bands is not completely certain. Therefore, how to overcome the randomness of deep learning technology in predicting shear bands in amorphous materials and efficiently and accurately predict the shear band position of amorphous materials is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for predicting shear bands of amorphous materials, which can overcome the randomness of deep learning technology in predicting shear bands of amorphous materials and efficiently and accurately predict the shear band positions of amorphous materials. The specific scheme is as follows:

[0006] In a first aspect, the present application provides a method for predicting shear bands in an amorphous material, comprising:

[0007] Acquiring microstructure data of a target amorphous material, and constructing an initial microscopic model of the target amorphous material according to the microstructure data; the microstructure data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution;

[0008] Calculating a stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determining a dilatancy transition zone inside the target amorphous material based on the stiffness gradient field;

[0009] A dilatancy transition zone prediction model corresponding to the target amorphous material is generated according to the dilatancy transition zone, and a shear band prediction result is generated based on the dilatancy transition zone prediction model.

[0010] Optionally, constructing an initial microscopic model of the target amorphous material according to the microstructure data includes:

[0011] Calculating the embedded atomic potential of the target amorphous material according to the microstructure data;

[0012] generating an atomic structure of the target amorphous material by simulating a rapid quenching step;

[0013] An initial microscopic model of the target amorphous material is constructed based on the embedded atomic potential and the atomic structure.

[0014] Optionally, the calculating the stiffness gradient field inside the target amorphous material based on the initial microscopic model includes:

[0015] Performing response analysis on the initial microscopic model through molecular dynamics simulation to obtain stiffness values ​​of each local area of ​​the initial microscopic model;

[0016] The local area stiffness change rate is calculated based on the stiffness values ​​of the respective local areas, and the stiffness gradient field inside the target amorphous material is generated based on the local area stiffness change.

[0017] Optionally, performing response analysis on the initial microscopic model through molecular dynamics simulation to obtain stiffness values ​​of each local area of ​​the initial microscopic model includes:

[0018] Performing response analysis on the initial microscopic model through molecular dynamics simulation, and constructing a dynamics matrix according to the response analysis results;

[0019] The stiffness value of each local area of ​​the initial microscopic model is calculated based on the dynamic matrix.

[0020] Optionally, determining the dilatancy transition zone inside the target amorphous material based on the stiffness gradient field includes:

[0021] Calculating the probability value of generating a shear band in each region based on the stiffness gradient field;

[0022] The dilatancy transition zone inside the target amorphous material is determined according to the probability value of generating shear bands in each region.

[0023] Optionally, after generating a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone and generating a shear band prediction result based on the dilatancy transition zone prediction model, the method further includes:

[0024] applying different types of simulated stresses to the dilatancy transition zone prediction model and monitoring the formation and expansion of each dilatancy transition zone;

[0025] A visualization report of the shear band formation location and path is generated based on the monitoring results and the shear band prediction results.

[0026] Optionally, applying different types of simulated stresses to the dilatancy transition zone prediction model includes:

[0027] Different types of stress fields are applied to the dilatancy transition zone prediction model through molecular dynamics to simulate the mechanical response of the target amorphous material under external stress.

[0028] Optionally, the target amorphous material is a metallic glass material.

[0029] In a second aspect, the present application provides a device for predicting shear bands of amorphous materials, comprising:

[0030] An initial microscopic model generation module is used to obtain microscopic structure data of a target amorphous material and construct an initial microscopic model of the target amorphous material according to the microscopic structure data; the microscopic structure data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution;

[0031] a dilatancy transition zone determination module, configured to calculate a stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determine a dilatancy transition zone inside the target amorphous material based on the stiffness gradient field;

[0032] A shear band prediction result generating module is used to generate a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone, and to generate a shear band prediction result based on the dilatancy transition zone prediction model.

[0033] In a third aspect, the present application provides an electronic device, including:

[0034] Memory, used to store computer programs;

[0035] The processor is used to execute the computer program to implement the above-mentioned amorphous material shear band prediction method.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned amorphous material shear band prediction method.

[0037] The present application provides a method for predicting shear bands of amorphous materials. First, microstructure data of a target amorphous material is acquired, and an initial microstructure model of the target amorphous material is constructed according to the microstructure data; the microstructure data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution; then, a stiffness gradient field inside the target amorphous material is calculated based on the initial microstructure model, and a dilatancy transition zone inside the target amorphous material is determined based on the stiffness gradient field; finally, a dilatancy transition zone prediction model corresponding to the target amorphous material is generated according to the dilatancy transition zone, and a shear band prediction result is generated based on the dilatancy transition zone prediction model.

[0038] As can be seen from the above, the present application obtains the local stiffness gradient field of the target amorphous material by performing response analysis on the microstructure of the target amorphous material, and predicts the shear band based on the local stiffness gradient field. That is, the present application proposes a shear band prediction algorithm that combines the local stiffness gradient and stress distribution, and predicts the formation area of ​​the shear band and its expansion path by analyzing the change of the local stiffness of the material and the spatial distribution of the shear transition zone. This can overcome the randomness of deep learning technology in the prediction of shear bands of amorphous materials, and efficiently and accurately predict the position of shear bands of amorphous materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 A flow chart of a method for predicting shear bands in amorphous materials disclosed in this application;

[0041] Figure 2 A schematic diagram of the microstructure model of Cu64Zr36 metallic glass disclosed in this application;

[0042] Figure 3 Schematic diagram of local stiffness distribution of Cu64Zr36 metallic glass disclosed in this application;

[0043] Figure 4A schematic diagram of the stress ball distribution of the Cu64Zr36 metallic glass after stress loading simulation disclosed in this application;

[0044] Figure 5 This is a schematic diagram of the dilatancy transition zone distribution of the Cu64Zr36 metallic glass disclosed in this application;

[0045] Figure 6 This is a schematic diagram of the shear band path prediction of the Cu64Zr36 metallic glass disclosed in this application;

[0046] Figure 7 A schematic diagram of real-time monitoring of shear bands of Cu64Zr36 metallic glass disclosed in this application;

[0047] Figure 8 A schematic diagram of a shear band prediction device for an amorphous material disclosed in the present application;

[0048] Fig. 9 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In recent years, with the advancement of computer technology and artificial intelligence, researchers have begun to use artificial intelligence technologies such as deep learning to predict the shear band formation position of metallic glass, and proposed a deep learning model based on convolutional neural network. Due to the high nonlinearity and randomness of the deformation process of metallic glass, the formation position of the shear band cannot be completely determined by the initial structure. Even under the same loading conditions, the position of the shear band in different simulations may be different, which shows that the prediction of the shear band still has a certain uncertainty. In addition to the DFS distribution, the formation of the shear band may also be affected by other factors, such as loading conditions, temperature, etc. Therefore, the formation mechanism of the shear band cannot be fully captured by the analysis of the initial static structure alone. Although the CNN model can be successful in glasses with different processing histories or different compositions within the same alloy system, its applicability in other types of amorphous materials still needs to be further verified. To this end, the present application provides a shear band prediction scheme for amorphous materials, which can overcome the randomness of deep learning technology in the prediction of shear bands in amorphous materials, and efficiently and accurately predict the shear band position of amorphous materials.

[0051] See also Figure 1As shown, the embodiment of the present application discloses a method for predicting shear bands of amorphous materials, comprising:

[0052] Step S11, acquiring microstructure data of a target amorphous material, and constructing an initial microstructure model of the target amorphous material according to the microstructure data.

[0053] In this embodiment, the microstructure data of the target amorphous material is obtained, and an initial microscopic model of the target amorphous material is constructed according to the microstructure data through molecular dynamics simulation software. The microstructure data of the target amorphous material includes but is not limited to the atomic arrangement, local bonding information and density distribution data of the target amorphous material. It should be noted that the target amorphous material is a metallic glass material. Specifically, the construction of the initial microscopic model of the target amorphous material according to the microstructure data may include: inputting the microstructure data into the molecular dynamics simulation software, and calculating the embedded atomic potential of the metallic glass material according to the microstructure data; simulating the rapid quenching step by changing the temperature and pressure parameters to generate the atomic structure of the metallic glass material; calculating the system potential energy based on the embedded atomic potential and the atomic structure, and constructing the initial microscopic model of the metallic glass material according to the system potential energy. The calculation formula is as follows:

[0054] ;

[0055] The system potential energy The calculation formula is as follows:

[0056] ;

[0057] in, It's an atom The embedding energy depends on the local electron density ; It's an atom and atoms The bonding energy between For atoms and atoms By traversing all the atomic pairs in the system and calculating the bonding energy and embedding energy of each atomic pair, the total potential energy of the system can be obtained.

[0058] Step S12: calculating the stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determining the dilatancy transition zone inside the target amorphous material based on the stiffness gradient field.

[0059] In this embodiment, the stiffness gradient field inside the target amorphous material is calculated based on the initial microscopic model through molecular dynamics simulation, wherein the local stiffness gradient field is used to characterize the stiffness distribution difference in different regions. Specifically, the stiffness gradient field inside the target amorphous material based on the initial microscopic model may include: performing response analysis on the initial microscopic model through molecular dynamics simulation to obtain the stiffness values ​​of each local region of the initial microscopic model; calculating the stiffness change rate of the local region based on the stiffness values ​​of each local region, and generating the stiffness gradient field inside the target amorphous material based on the stiffness change of the local region.

[0060] Furthermore, the response analysis of the initial microscopic model is performed through molecular dynamics simulation, and the stiffness value of each local area is calculated by constructing a dynamic matrix. Specifically, the response analysis of the initial microscopic model through molecular dynamics simulation to obtain the stiffness value of each local area of ​​the initial microscopic model may include: performing response analysis on the initial microscopic model through molecular dynamics simulation, and constructing a dynamic matrix according to the response analysis results; and calculating the stiffness value of each local area of ​​the initial microscopic model based on the dynamic matrix. Wherein, the dynamic matrix To describe atoms The second-order derivative of the interaction with the neighborhood is expressed as follows:

[0061] ;

[0062] in, For atoms The potential energy, and is the spatial coordinate system. The dynamics matrix is ​​constructed by calculating the second-order derivative of the potential energy of the interaction between atoms, and then the dynamics matrix is ​​solved by Eigenvalue Atomic stiffness can be obtained , the calculation formula is as follows:

[0063] ;

[0064] Furthermore, the local stiffness can be calculated by local coarse-graining , and its calculation formula is as follows:

[0065] ;

[0066] in, is the coarse-graining radius, For atoms The number of atoms within the nearby coarse-graining radius.

[0067] In this embodiment, the dilatation transition zone (SDTZ) inside the target amorphous material is determined based on the stiffness gradient field. The dilatation transition zone is the core area where local plastic deformation of the amorphous material occurs. The formation of SDTZ involves not only shear strain, but also local changes in volume expansion, and the two together lead to local instability. Specifically, the dilatation transition zone inside the target amorphous material based on the stiffness gradient field may include: calculating the probability value of generating shear bands in each region based on the stiffness gradient field; and determining the dilatation transition zone inside the target amorphous material according to the probability value of generating shear bands in each region. That is, after obtaining the local stiffness gradient field, the dilatation transition zone inside the material where dilatation transition is prone to occur can be identified. The dilatation transition zone is usually located at a location where the local stiffness gradient is large and there is a dilatation effect, so these areas have a higher risk of shear band formation. By analyzing the local stiffness gradient field, the dilatation transition behavior of these dilatation transition zones can be predicted.

[0068] Furthermore, this embodiment can introduce other multi-scale modeling methods, such as finite element analysis (FEA) or Monte Carlo simulation (MC) to supplement the limitations of molecular dynamics calculations, especially in the prediction of shear bands in large-scale systems or complex structural materials, which can provide better accuracy and efficiency. At the same time, corrections can be made by introducing other microscopic physical parameters to enhance the comprehensiveness and reliability of shear band predictions. Among them, the microscopic physical parameters include but are not limited to local strain fields, free volumes or atomic density fluctuations. It can be understood that the weights of different parameters can be flexibly adjusted according to the specific characteristics of the material.

[0069] Step S13: generating a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone, and generating a shear band prediction result based on the dilatancy transition zone prediction model.

[0070] In this embodiment, a dilatancy transition zone prediction model corresponding to the target amorphous material is generated by calculating the dilatancy transition value of the dilatancy transition zone, and a shear band prediction result is generated based on the dilatancy transition zone prediction model. The calculation formula is as follows:

[0071] ;

[0072] Among them, is the Laplace operator, is the local soft area in the system, for The derivative along the x direction, It is a local area that is prone to shear dilatancy transition. It is the body expansion stress area. Regions with higher values ​​are more likely to produce shear bands under shear loading.

[0073] Furthermore, by applying different types of stress, the stress distribution and shear deformation behavior inside the material can be tracked in real time. Combining the results of material modeling and stress loading simulation, the prediction results of the shear band formation position are generated by the shear transition zone prediction algorithm and output as a visual report. Wherein, the stress types include but are not limited to tension, compression, and shear. Specifically, after generating a shear transition zone prediction model corresponding to the target amorphous material according to the shear transition zone, and generating a shear band prediction result based on the shear transition zone prediction model, it can also include: applying different types of simulated stress to the shear transition zone prediction model, and monitoring the formation and expansion of each shear transition zone; generating a visual report of the shear band formation position and path based on the monitoring results and the shear band prediction results.

[0074] It is worth mentioning that the mechanical response of the material under loading conditions can be simulated by applying different stress fields in molecular dynamics simulation to monitor the formation and expansion of SDTZ. Specifically, applying different types of simulated stresses to the dilatancy transition zone prediction model can include: applying different types of stress fields to the dilatancy transition zone prediction model through molecular dynamics to simulate the mechanical response of the target amorphous material under external stress. The stress tensor calculation formula of the stress field is as follows:

[0075] ;

[0076] in, is the stress tensor, is the system volume, is the atomic mass, is the velocity component, and are the distance and force between atoms respectively. The stress sphere quantity of the stress field The calculation formula is as follows:

[0077] ;

[0078] in for Directional principal stress and stress sphere are used to measure the volume expansion tension in the area.

[0079] Furthermore, the stress loading conditions can be expanded to include various forms such as thermal loading, dynamic impact, cyclic loading, etc. These variations can make the prediction model more adaptable to the actual use requirements in different application scenarios.

[0080] As can be seen from the above, the embodiment of the present application constructs an initial model of the material based on the microstructure data of the amorphous material. Through molecular dynamics simulation, the local stiffness gradient field inside the material is calculated, and the high shear sensitive area where the dilatancy transition may occur is identified. A stress field is applied to the material to simulate its deformation behavior under different loading conditions. According to the dilatancy transition zone prediction model, combined with the local stiffness gradient and stress distribution, the formation position of the shear band is predicted and output as a visual report. That is, by analyzing the changes in the local stiffness of the material and the spatial distribution of the dilatancy transition zone, the formation area of ​​the shear band and its extension path are predicted. This can overcome the randomness of deep learning technology in the prediction of shear bands of amorphous materials, and efficiently and accurately predict the position of the shear bands of amorphous materials.

[0081] Furthermore, the present application provides a shear band prediction method based on Cu64Zr36 metallic glass, comprising:

[0082] See also Figure 2 As shown, in this embodiment, a microstructure model of Cu64Zr36 metallic glass is constructed, and the initial structure of the alloy is generated using molecular dynamics simulation software. Specifically, according to the proportion of the alloy (Cu accounts for 64% and Zr accounts for 36%), a cubic unit cell containing thousands of alloy atoms is constructed. The embedded atom method (EAM) is selected as the interaction potential to simulate the interaction between atoms, which can accurately describe the bonding force between atoms in the alloy. The Cu64Zr36 alloy is melted at a preset temperature and quickly cooled to room temperature to generate the initial structure of the amorphous metallic glass.

[0083] See also Figure 3 As shown, in this embodiment, the dynamic matrix (Hessian matrix) of the Cu64Zr36 alloy is generated by molecular dynamics simulation to describe the second-order derivative of the interaction force between different atoms. The position of each atom in the Cu64Zr36 alloy is solved by the eigenvalue of the dynamic matrix to obtain the local stiffness of each atom. The calculated stiffness value is spatially coarsened to generate a local stiffness gradient field of the Cu64Zr36 alloy. The area with a large change in stiffness gradient is a high shear sensitive area, which has a high potential for shear band formation.

[0084] See also Figure 4 As shown in the figure, in this embodiment, a simple shear of 5% along the x direction is applied to the Cu64Zr36 alloy sample, and its mechanical response is monitored in real time. The stress loading condition sets the quasi-static tensile rate. Molecular dynamics simulation is used to monitor the stress distribution inside the Cu64Zr36 alloy sample, especially the local high stress sphere. Concentration area. Prediction of shear band formation by calculating , identify the shear dilatancy transformation zone (SDTZ) in Cu64Zr36 alloy. For details, see Figure 5 As shown in Figure 2, based on the calculation results of the stiffness gradient field and stress, the prediction index of shear band formation in the Cu64Zr36 alloy sample is calculated. Larger areas, which may experience local volume expansion and shear deformation, belong to the shear-dilatancy transition zone.

[0085] Furthermore, by analyzing the spatial distribution of the dilatancy transition zone and its transfer relationship with the surrounding area, the formation location and path of the shear band can be predicted. Figure 6 As shown in the figure, the shear band is formed by connecting multiple shear-dilation transition zones. The shear band may extend along the area formed by connecting multiple shear-dilation transition zones to eventually form a complete shear band structure. Therefore, the location of the shear band can be accurately predicted by tracking these areas.

[0086] See also Figure 7 As shown, in this embodiment, the position, shape and evolution of the shear band actually generated in the Cu64Zr36 metallic glass are monitored in real time by molecular dynamics simulation. The formation and expansion of the shear band are recorded by a visualization tool. By comparing the predicted results with the shear band position and shape actually observed in the molecular dynamics simulation, the validity and accuracy of the prediction of the shear band formation position in the Cu64Zr36 metallic glass can be verified.

[0087] As can be seen from the above, the embodiment of the present application predicts the formation of shear bands in Cu64Zr36 metallic glass based on the local stiffness gradient, and can identify the high shear sensitive areas inside the material. Through the combination of molecular dynamics simulation and stress loading simulation, the formation of shear bands can be predicted in real time, and immediate feedback can be provided for material design and optimization. Therefore, the position of shear bands in Cu64Zr36 metallic glass can be predicted efficiently and accurately.

[0088] See also Figure 8 As shown, the present application provides a shear band prediction device for an amorphous material, comprising:

[0089] An initial microscopic model generating module 11 is used to obtain microscopic structural data of a target amorphous material and construct an initial microscopic model of the target amorphous material according to the microscopic structural data; the microscopic structural data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution;

[0090] A dilatancy transition zone determination module 12, configured to calculate a stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determine a dilatancy transition zone inside the target amorphous material based on the stiffness gradient field;

[0091] The shear band prediction result generating module 13 is used to generate a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone, and generate a shear band prediction result based on the dilatancy transition zone prediction model.

[0092] As can be seen from the above, the embodiment of the present application obtains the local stiffness gradient field of the target amorphous material by performing response analysis on the microstructure of the target amorphous material, and predicts the shear band based on the local stiffness gradient field. That is, the present application proposes a shear band prediction algorithm that combines the local stiffness gradient and stress distribution, and predicts the formation area of ​​the shear band and its extension path by analyzing the change of the local stiffness of the material and the spatial distribution of the shear transition zone. This can overcome the randomness of deep learning technology in the prediction of shear bands of amorphous materials, and efficiently and accurately predict the position of shear bands of amorphous materials.

[0093] In some specific implementations, the initial microscopic model generation module 11 may specifically include:

[0094] An embedded atomic potential calculation unit, used to calculate the embedded atomic potential of the target amorphous material according to the microstructure data;

[0095] A rapid quenching simulation unit, used to generate the atomic structure of the target amorphous material by simulating a rapid quenching step;

[0096] An initial microscopic model generating unit is used to construct an initial microscopic model of the target amorphous material based on the embedded atomic potential and the atomic structure.

[0097] In some specific implementations, the dilatancy transition zone determination module 12 may specifically include:

[0098] A local area stiffness value determination submodule is used to perform response analysis on the initial microscopic model through molecular dynamics simulation to obtain stiffness values ​​of each local area of ​​the initial microscopic model;

[0099] A stiffness gradient field generating unit, configured to calculate a stiffness change rate of a local region based on the stiffness values ​​of the respective local regions, and to generate a stiffness gradient field inside the target amorphous material based on the stiffness change of the local region;

[0100] A shear band generation probability calculation unit, used for calculating the probability value of shear band generation in each region based on the stiffness gradient field;

[0101] a dilatancy transition zone determination unit, configured to determine the dilatancy transition zone inside the target amorphous material according to the probability value of generating shear bands in each region;

[0102] Furthermore, in some specific implementations, the local area stiffness value determination submodule may specifically include:

[0103] A dynamics matrix construction unit, used for performing response analysis on the initial microscopic model through molecular dynamics simulation, and constructing a dynamics matrix according to the response analysis results;

[0104] The local area stiffness value calculation unit is used to calculate the stiffness value of each local area of ​​the initial microscopic model based on the dynamic matrix.

[0105] In some specific embodiments, the amorphous material shear band prediction device may further include:

[0106] A simulated stress loading module, used to apply different types of simulated stresses to the dilatancy transition zone prediction model and monitor the formation and expansion of each dilatancy transition zone;

[0107] A shear band prediction report generation module, used to generate a visualization report of the shear band formation position and path according to the monitoring results and the shear band prediction results;

[0108] Furthermore, in some specific implementations, the simulated stress loading module may specifically include:

[0109] The simulated stress loading unit is used to apply different types of stress fields to the shear expansion transition zone prediction model through molecular dynamics to simulate the mechanical response of the target amorphous material under external stress.

[0110] Furthermore, the present application also discloses an electronic device. Fig. 9 It is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input-output interface 25 and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the amorphous material shear band prediction method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment can specifically be an electronic computer.

[0111] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0112] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0113] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the amorphous material shear band prediction method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0114] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the amorphous material shear band prediction method disclosed above is implemented. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0115] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0116] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0118] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0119] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for predicting shear bands in amorphous materials, characterized in that: include: Acquiring microstructure data of a target amorphous material, and constructing an initial microscopic model of the target amorphous material according to the microstructure data; the microstructure data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution; Calculating a stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determining a dilatancy transition zone inside the target amorphous material based on the stiffness gradient field; A dilatancy transition zone prediction model corresponding to the target amorphous material is generated according to the dilatancy transition zone, and a shear band prediction result is generated based on the dilatancy transition zone prediction model.

2. The method for predicting shear bands of amorphous materials according to claim 1, characterized in that: The step of constructing an initial microscopic model of the target amorphous material according to the microstructure data comprises: Calculating the embedded atomic potential of the target amorphous material according to the microstructure data; generating an atomic structure of the target amorphous material by simulating a rapid quenching step; An initial microscopic model of the target amorphous material is constructed based on the embedded atomic potential and the atomic structure.

3. The method for predicting shear bands of amorphous materials according to claim 1, characterized in that: The step of calculating the stiffness gradient field inside the target amorphous material based on the initial microscopic model comprises: Performing response analysis on the initial microscopic model through molecular dynamics simulation to obtain stiffness values ​​of each local area of ​​the initial microscopic model; The local area stiffness change rate is calculated based on the stiffness values ​​of the respective local areas, and the stiffness gradient field inside the target amorphous material is generated based on the local area stiffness change.

4. The method for predicting shear bands of amorphous materials according to claim 3, characterized in that: The performing response analysis on the initial microscopic model by molecular dynamics simulation to obtain stiffness values ​​of each local area of ​​the initial microscopic model includes: Performing response analysis on the initial microscopic model through molecular dynamics simulation, and constructing a dynamics matrix according to the response analysis results; The stiffness value of each local area of ​​the initial microscopic model is calculated based on the dynamic matrix.

5. The method for predicting shear bands of amorphous materials according to claim 1, characterized in that: The determining of the dilatancy transition zone inside the target amorphous material based on the stiffness gradient field comprises: Calculating the probability value of generating a shear band in each region based on the stiffness gradient field; The dilatancy transition zone inside the target amorphous material is determined according to the probability value of generating shear bands in each region.

6. The method for predicting shear bands of amorphous materials according to claim 1, characterized in that: After generating a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone and generating a shear band prediction result based on the dilatancy transition zone prediction model, the method further includes: applying different types of simulated stresses to the dilatancy transition zone prediction model and monitoring the formation and expansion of each dilatancy transition zone; A visualization report of the shear band formation location and path is generated based on the monitoring results and the shear band prediction results.

7. The method for predicting shear bands of amorphous materials according to claim 6, characterized in that: The applying different types of simulated stresses to the dilatancy transition zone prediction model comprises: Different types of stress fields are applied to the dilatancy transition zone prediction model through molecular dynamics to simulate the mechanical response of the target amorphous material under external stress.

8. The method for predicting shear bands of amorphous materials according to any one of claims 1 to 7, characterized in that: The target amorphous material is a metallic glass material.

9. A device for predicting shear bands in amorphous materials, characterized in that: include: An initial microscopic model generation module is used to obtain microscopic structure data of a target amorphous material and construct an initial microscopic model of the target amorphous material according to the microscopic structure data; the microscopic structure data of the target amorphous material includes data of atomic arrangement, local bonding information and density distribution; a dilatancy transition zone determination module, configured to calculate a stiffness gradient field inside the target amorphous material based on the initial microscopic model, and determine a dilatancy transition zone inside the target amorphous material based on the stiffness gradient field; A shear band prediction result generating module is used to generate a dilatancy transition zone prediction model corresponding to the target amorphous material according to the dilatancy transition zone, and to generate a shear band prediction result based on the dilatancy transition zone prediction model.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the shear band prediction method of an amorphous material according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the method for predicting shear bands in amorphous materials according to any one of claims 1 to 8 is implemented.

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