A method, device, equipment and medium for predicting plastic deformation of a solid material

Abstract

The application discloses a solid material plastic deformation prediction method and device, equipment and medium, and relates to the technical field of solid mechanics, and comprises the following steps: acquiring first configuration data corresponding to a solid material to be analyzed, and performing energy minimization processing on the first configuration data to obtain corresponding second configuration data; constructing a preset dynamic matrix corresponding to each atom in the second configuration data, and determining a target equivalent stiffness parameter of each atom based on the preset dynamic matrix; performing coarse-grained processing on the target equivalent stiffness parameter of each atom to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used for characterizing the relative stiffness distribution of each spatial position inside the solid material; and based on the target equivalent stiffness field, a target defect area in the solid material is identified, and plastic deformation prediction is performed based on the target defect area. As can be seen from the above, the application can realize general, efficient and accurate solid material plastic deformation prediction of multi-structure materials.

Description

Technical Field

[0001] This invention relates to the field of solid mechanics, and in particular to a method, apparatus, equipment, and medium for predicting the plastic deformation of solid materials. Background Technology

[0002] The plastic deformation and failure mechanisms of solid materials are core research topics in solid mechanics, materials science, and condensed matter physics. For crystalline materials, plastic deformation is mainly caused by defects such as dislocations, dislocations, and twins, and traditional dislocation theory can describe their deformation behavior well. However, for polycrystalline materials, quasicrystalline materials, and amorphous solids such as metallic glasses, due to the lack of long-range periodic structures, traditional dislocation theory is difficult to apply directly, and their plastic behavior often exhibits highly spatially localized characteristics.

[0003] In recent decades, researchers have proposed numerous structural, thermodynamic, and mechanical indices to identify “defects” in amorphous solids that foreshadow plastic events, including free volume, local excess entropy, hexagonal order parameter, soft modulus, vibrational properties, local fivefold symmetry, Voronoi anisotropy, compliance volume, residual plastic strength, softness, energy field, and atomic nonaffine properties. However, most indices are designed for specific structural features and have limited applicability. While Burgers vectors and soft modulus frameworks are applicable to both crystalline and amorphous solids, the former requires pre-loaded complete atomic displacement data, and the latter requires calculating the eigenvalue decomposition of the complete dynamic matrix, resulting in a computational complexity of O((dN)²), where d is the dimension and N is the number of atoms, which is extremely costly for large-scale systems.

[0004] In summary, how to achieve universal, efficient, and accurate prediction of plastic deformation in solid materials with multiple structures is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for predicting the plastic deformation of solid materials, which can achieve universal, efficient, and accurate prediction of the plastic deformation of solid materials with multiple structures. The specific solution is as follows: In a first aspect, this application provides a method for predicting the plastic deformation of solid materials, including: Obtain the first configuration data corresponding to the solid material to be analyzed, and perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data; Construct a preset dynamic matrix corresponding to each atom in the second configuration data, and determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atom being an atom whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region being a region determined based on the atom and its neighboring atoms; The target equivalent stiffness parameters of each atom are coarsened to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material. The target defect region in the solid material is identified based on the target equivalent stiffness field, and plastic deformation is predicted based on the target defect region.

[0006] Optionally, performing energy minimization processing on the first configuration data to obtain the corresponding second configuration data includes: Based on the conjugate gradient method or the steepest descent method, the energy minimization process is applied to the first configuration data to obtain the corresponding second configuration data.

[0007] Optionally, the step of constructing a preset dynamic matrix corresponding to each atom in the second configuration data, and determining the target equivalent stiffness parameter of each atom based on each preset dynamic matrix, includes: For any atom in the second configuration data, a preset distance condition is determined based on a preset distance threshold, and neighboring atoms that satisfy the preset distance condition are identified; the preset distance condition is used to control the distance between the atom and the neighboring atom to be less than or equal to the preset distance threshold. Determine the second-order partial derivative matrix corresponding to the interaction potential energy between the atom and its neighboring atoms, and use the second-order partial derivative matrix as the preset dynamic matrix of the atom; Calculate the determinant of the preset dynamic matrix, and calculate the target equivalent stiffness parameter of the atom based on the determinant.

[0008] Optionally, the coarsening of the target equivalent stiffness parameters of each atom to construct the corresponding target equivalent stiffness field includes: For any one of the atoms, the atom is taken as the center of a sphere, and a target spherical region corresponding to the center of the sphere is constructed based on a preset radius. The target equivalent stiffness parameters of each atom in the target spherical region are weighted and averaged using a preset Gaussian weighting function. The target equivalent stiffness field is constructed based on the weighted average result corresponding to each atom.

[0009] Optionally, identifying the target defect region in the solid material based on the target equivalent stiffness field includes: If the structural characteristics of the solid material are long-range ordered, then the target defect region is identified based on the first-order gradient of the target equivalent stiffness field; If the structural characteristics of the solid material are short-range ordered or disordered, the target defect region is identified based on the second-order gradient of the target equivalent stiffness field.

[0010] Optionally, before performing plastic deformation prediction based on the target defect region, the method further includes: The first deformed atom in each of the atoms is determined, and the atoms are arranged in descending order according to the gradient or divergence magnitude corresponding to each atom, and several initial coverage rates are set. For any of the initial coverage rates, an initial quantity is determined based on the product of the initial coverage rate and a preset quantity. Then, the initial quantity of initial atoms is selected from the arranged atoms in ascending order. Second deformed atoms are determined from the initial atoms. An initial ratio is determined based on the quotient of the number of second deformed atoms and the number of first deformed atoms. Finally, an initial efficiency corresponding to the initial coverage rate is determined based on the difference between the initial ratio and the initial coverage rate. Wherein, deformed atoms are atoms that undergo plastic deformation. The initial coverage rate corresponding to the highest initial efficiency is determined as the target coverage rate, and the initial ratio corresponding to the target coverage rate is determined as the target ratio; Calculate the ratio between the target proportion and the target coverage. If the ratio is greater than 1, then predict plastic deformation based on the target defect area.

[0011] Optionally, the plastic deformation prediction based on the target defect region includes: The target quantity is determined based on the product of the target coverage and the preset quantity, and the target quantity of target atoms is selected from the arranged atoms in ascending order. Determine the target eigenvector corresponding to the preset dynamic matrix of the target atom, and determine the target vector corresponding to the non-affine displacement of the target atom; Determine the correlation degree between the tensor of the target feature vector and the target vector, and predict the direction of plastic deformation based on the correlation degree.

[0012] Secondly, this application provides a device for predicting the plastic deformation of a solid material, comprising: The configuration data acquisition module is used to acquire the first configuration data corresponding to the solid material to be analyzed, and to perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data. The parameter determination module is used to construct a preset dynamic matrix corresponding to each atom in the second configuration data, and to determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atoms being atoms whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region being a region determined based on the atom and its neighboring atoms; An equivalent stiffness field construction module is used to coarse-grain the target equivalent stiffness parameters of each atom to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material; The plastic deformation prediction module is used to identify target defect regions in the solid material based on the target equivalent stiffness field, and to predict plastic deformation based on the target defect regions.

[0013] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for predicting the plastic deformation of solid materials.

[0014] Fourthly, this application provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for predicting the plastic deformation of solid materials.

[0015] In this application, firstly, the first configuration data corresponding to the solid material to be analyzed is obtained, and the first configuration data is subjected to energy minimization processing to obtain the corresponding second configuration data; then, a preset dynamic matrix corresponding to each atom in the second configuration data is constructed, and the target equivalent stiffness parameter of each atom is determined based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, where the neighboring atom is an atom whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, where the local region is the region determined based on the atom and its neighboring atoms; subsequently, the target equivalent stiffness parameter of each atom is coarsened to construct the corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material; finally, the target defect region in the solid material is identified based on the target equivalent stiffness field, and plastic deformation is predicted based on the target defect region. As can be seen from the above, this application first obtains the first configuration data of the solid material to be analyzed and then performs energy minimization processing to obtain the second configuration data. Next, based on the interaction potential energy of each atom and its nearest neighbor in the second configuration data, a preset dynamic matrix corresponding to each atom is constructed. Based on the preset dynamic matrix, the target equivalent stiffness parameter characterizing the local relative stiffness of the atom is further calculated. Subsequently, the target equivalent stiffness parameter is coarsened to construct a target equivalent stiffness field that reflects the internal spatial stiffness distribution of the material. Finally, based on the target equivalent stiffness field, the target defect region in the solid material is accurately identified, and plastic deformation is predicted. In this way, this application can clearly characterize the internal stiffness differences of solid materials, accurately locate defect positions, and provide reliable predictions for the position and direction of plastic deformation. It can achieve universal, efficient, and accurate prediction of plastic deformation in solid materials with multiple structures, possessing strong versatility and engineering practical value. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 A flowchart of a method for predicting the plastic deformation of solid materials provided in this application; Figure 2 This application provides a specific radial distribution function and atomic configuration diagram; wherein, Figure 2 In the diagram, (a) represents single-crystal Cu. Figure 2 (b) in the figure represents single-crystal Nb3Sn. Figure 2 (c) in the figure represents polycrystalline Cu. Figure 2 (d) in the figure represents polycrystalline Nb3Sn. Figure 2 (e) in the text refers to a quasicrystal. Figure 2 (f) in the figure represents the KA amorphous system; Figure 3 A specific visualization diagram illustrating the correlation between the equivalent stiffness field and plasticity is provided for this application; wherein, Figure 3 (a1), (a2), and (a3) ​​are single-crystal Cu. Figure 3 (b1), (b2), and (b3) in the figure represent single-crystal Nb3Sn. Figure 3 (c1), (c2), and (c3) are polycrystalline Cu. Figure 3 In the figure, (d1), (d2), and (d3) represent polycrystalline Nb3Sn. Figure 3 (e1), (e2), and (e3) are quasicrystals. Figure 3 (f1), (f2), and (f3) in the figure represent the KA amorphous system; Figure 4 This application provides a specific schematic diagram of a defect area coverage optimization curve; wherein, Figure 4 In the figure, (a) is the curve of efficiency as a function of coverage. Figure 4 (b) in the figure shows the curve of efficiency ratio as a function of coverage rate; Figure 5 This application provides a schematic diagram illustrating the variation curve of the probability enhancement factor with the normalized strain threshold; wherein, Figure 5 (a) in the figure represents polycrystalline Cu. Figure 5 (b) in the figure represents polycrystalline Nb3Sn. Figure 5 (c) in the text is a quasicrystal. Figure 5 (d) in the figure represents the KA amorphous system; Figure 6 This application provides a schematic diagram illustrating the correlation curve between the tensor of a specific eigenvector and a displacement vector as a function of a normalized strain threshold; wherein, Figure 6 (a) in the figure represents polycrystalline Cu. Figure 6 (b) in the figure represents polycrystalline Nb3Sn. Figure 6 (c) in the text is a quasicrystal. Figure 6 (d) in the figure represents the KA amorphous system; Figure 7 A schematic diagram of a device for predicting the plastic deformation of solid materials provided in this application; Figure 8 This application provides a structural diagram of an electronic device. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The plastic deformation and failure mechanisms of solid materials are core research topics in solid mechanics, materials science, and condensed matter physics. For crystalline materials, plastic deformation is mainly caused by defects such as dislocations, orientations, and twins, and traditional dislocation theory can describe their deformation behavior well. However, for polycrystalline materials, quasicrystalline materials, and amorphous solids such as metallic glasses, due to the lack of long-range periodic structures, traditional dislocation theory is difficult to apply directly, and their plastic behavior often exhibits highly spatially localized characteristics. In recent decades, researchers have proposed numerous structural, thermodynamic, and mechanical indices to identify "defects" that predict plastic events in amorphous solids, including free volume, local excess entropy, hexagonal order parameter, soft modulus, vibrational properties, local fivefold symmetry, Voronoi anisotropy, compliance volume, residual plastic strength, softness, energy field, and atomic non-affine properties. However, most indices are designed for specific structural features, limiting their applicability. While Burgers vector and soft-mode frameworks are applicable to both crystalline and amorphous solids, the former requires pre-loaded complete atomic displacement data, and the latter requires calculating the eigenvalue decomposition of the complete dynamic matrix, resulting in a computational complexity of O((dN)²), where d is the dimension and N is the number of atoms. This is extremely costly for large-scale systems. Therefore, this application provides a plastic deformation prediction scheme for solid materials, enabling universal, efficient, and accurate prediction of plastic deformation in multi-structured materials.

[0020] See Figure 1 As shown, this embodiment of the invention discloses a method for predicting the plastic deformation of solid materials, which may include: Step S11: Obtain the first configuration data corresponding to the solid material to be analyzed, and perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data.

[0021] In this embodiment, the first configuration data corresponding to the solid material, namely atomic configuration data, can be obtained by using the snapshot configuration output by molecular dynamics simulation, the relaxed configuration obtained by first-principles calculation, and experimental observations, such as synchrotron X-ray diffraction and electron microscopy combined with structural reconstruction algorithms. This includes atomic coordinates, atom type, system size, and boundary condition information. Figure 2 Schematic diagrams of radial distribution functions and atomic configurations for six model systems; among them, Figure 2 In the diagram, (a) represents single-crystal Cu. Figure 2(b) in the figure represents single-crystal Nb3Sn. Figure 2 (c) in the figure represents polycrystalline Cu. Figure 2 (d) in the figure represents polycrystalline Nb3Sn. Figure 2 (e) in the text refers to a quasicrystal. Figure 2 (f) in the figure represents the KA amorphous system.

[0022] Next, the first configuration data needs to be processed by energy minimization to eliminate the influence of thermal noise and obtain the "intrinsic structure" corresponding to the local minimum point on the potential energy surface, so as to obtain the second configuration data in the intrinsic structure state, i.e., the relaxed configuration. Specifically, in this embodiment, the first configuration data can be processed by energy minimization based on the conjugate gradient method or the steepest descent method to obtain the second configuration data.

[0023] Step S12: Construct a preset dynamic matrix corresponding to each atom in the second configuration data, and determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atom is an atom whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region is a region determined based on the atom and its neighboring atoms.

[0024] In this embodiment, a preset dynamic matrix corresponding to each atom in the second configuration data can be constructed, and the target equivalent stiffness parameter of each atom can be determined based on each preset dynamic matrix. The specific process may include: firstly, for any atom in the second configuration data, a preset distance condition is determined based on a preset distance threshold, and the neighboring atoms that satisfy the preset distance condition are determined; the preset distance condition is used to control the distance between the atom and the neighboring atoms to be less than or equal to the preset distance threshold; then, the second-order partial derivative matrix corresponding to the interaction potential energy between the atom and the neighboring atoms of the atom is determined, and the second-order partial derivative matrix is ​​determined as the preset dynamic matrix of the atom; finally, the determinant of the preset dynamic matrix is ​​calculated, and the target equivalent stiffness parameter of the atom is calculated based on the determinant; wherein, the target equivalent stiffness parameter is a dimensionless parameter derived from the determinant of the preset dynamic matrix, used to characterize the relative stiffness of the local region corresponding to the atom.

[0025] Specifically, for any atom i in the second configuration data, neighboring atoms whose distance from atom i is less than or equal to a preset distance threshold r_cut are identified. Then, a preset dynamic matrix D_i is obtained based on the second-order partial derivative matrix corresponding to the interaction potential energy between atom i and its neighboring atoms. The preset dynamic matrix has a dimension of d×d, where d is the spatial dimension, typically d=3. Next, the determinant |D_i| of the preset dynamic matrix D_i is calculated, and the formula for calculating the equivalent stiffness parameter is used to determine the target equivalent stiffness parameter, as shown below: D_i =ln(h|D_i|^(1 / 6) / (k_B T)); Where D_i Let be the target equivalent stiffness parameter of atom i, h be the reduced Planck constant, k_B be the Boltzmann constant, T be the temperature, and |D_i| be the determinant of the preset dynamic matrix D_i of atom i.

[0026] Step S13: Coarse-grained processing is performed on the target equivalent stiffness parameters of each atom to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material.

[0027] In this embodiment, the target equivalent stiffness parameters of each atom are coarsened to construct the corresponding target equivalent stiffness field. The coarsening process adopts a spherical weighted average method. The specific process may include: for any atom, taking the atom as the center of a sphere, and constructing a target spherical region corresponding to the center of the sphere based on a preset radius; using a preset Gaussian weighting function to perform a weighted average of the target equivalent stiffness parameters of each atom in the target spherical region; constructing the target equivalent stiffness field based on the weighted average result corresponding to each atom; wherein, the target CGES (Continuous Gradient Equivalent Stiffness) field is a continuous field quantity obtained after spatial coarsening and averaging of the target equivalent stiffness parameters, which is used to construct a differentiable continuous field for gradient analysis.

[0028] It should be noted that the selection of the preset radius when performing coarsening is based on the following considerations: the physical scale is comparable to the characteristic scale of the plastic deformation unit in the solid material, the coordination layer covers at least 2-3 neighboring coordination layers, and cross-system consistency is applicable to solid materials with different structural types.

[0029] See Figure 3 As shown, Figure 3 This is a visualization of the correlation between the CGES field and plasticity, where... Figure 3 (a1), (a2), and (a3) ​​are single-crystal Cu. Figure 3(b1), (b2), and (b3) in the figure represent single-crystal Nb3Sn. Figure 3 (c1), (c2), and (c3) are polycrystalline Cu. Figure 3 In the figure, (d1), (d2), and (d3) represent polycrystalline Nb3Sn. Figure 3 (e1), (e2), and (e3) are quasicrystals. Figure 3 (f1), (f2), and (f3) in the figure represent the KA amorphous system. Each sub-figure shows the CGES field, the CGES gradient / divergence field, and the non-affine displacement field, respectively.

[0030] Step S14: Identify the target defect region in the solid material based on the target equivalent stiffness field, and predict plastic deformation based on the target defect region.

[0031] In this embodiment, the identification of target defect regions in solid materials based on the target equivalent stiffness field can be specifically carried out as follows: if the solid material has a long-range ordered structure, the target defect region is identified based on the first-order gradient of the target equivalent stiffness field; if the solid material has a short-range ordered or disordered structure, the target defect region is identified based on the second-order gradient of the target equivalent stiffness field. That is, for long-range ordered single-crystal materials, the first-order gradient of the target equivalent stiffness field is used to identify defect regions such as dislocation cores; for disordered materials, such as polycrystalline materials, quasicrystalline materials, and amorphous materials, the second-order gradient (divergence) of the target equivalent stiffness field is used to identify defect regions such as grain boundaries and STZs.

[0032] It should be noted that the defect region is also called the soft region, which is the area in the target CGES field that exhibits low stiffness value or high gradient / high divergence characteristics. It corresponds to the "defect" location in the material with weak mechanical stability and is the preferred region for plastic deformation.

[0033] It should be noted that, in order to maximize the capture efficiency of the target defect region for plastic events, before predicting plastic deformation based on the target defect region, the process may further include: firstly, determining the first deformable atom among the atoms, arranging the atoms in descending order according to the gradient or divergence amplitude corresponding to each atom, and setting several initial coverage rates; for any initial coverage rate, determining an initial quantity based on the product of the initial coverage rate and a preset quantity, selecting the initial quantity of initial atoms from the arranged atoms in ascending order, determining the second deformable atom among the initial atoms, determining an initial ratio based on the quotient of the number of the second deformable atoms and the number of the first deformable atoms, and determining the initial efficiency corresponding to the initial coverage rate based on the difference between the initial ratio and the initial coverage rate; wherein, the deformable atom is the atom that undergoes plastic deformation; then, determining the initial coverage rate corresponding to the largest initial efficiency as the target coverage rate, and determining the initial ratio corresponding to the target coverage rate as the target ratio; calculating the ratio between the target ratio and the target coverage rate, and if the ratio is greater than 1, then performing plastic deformation prediction based on the target defect region.

[0034] Specifically, firstly, an adiabatic quasi-static shear simulation is used to calculate the von Mises equivalent strain of each atom to determine the first deformed atom in each atom group. All atoms are then arranged in descending order according to their gradient or divergence magnitude, and several initial coverage rates are preset. Next, to determine the optimal coverage rate, the initial quantity is determined based on the product of the initial coverage rate f_cov and the preset quantity N. The initial number of atoms is then selected from the arranged atoms in ascending order to determine the second deformed atom in the initial atom group. The initial proportion f_sat is determined based on the quotient of the number of second deformed atoms and the number of first deformed atoms. Finally, the initial efficiency η corresponding to the initial coverage rate is determined based on the difference between the initial proportion f_sat and the initial coverage rate f_cov, as shown in the following formula: η = f_sat - f_cov; Next, by scanning f_cov, the coverage corresponding to the peak position is taken as the optimal coverage. That is, the initial coverage corresponding to the maximum initial efficiency is determined as the target coverage f_Cov, and the initial ratio corresponding to the target coverage is determined as the target ratio f_ss. See also... Figure 4 As shown, Figure 4 This is a schematic diagram of the defect area coverage optimization curve; where, Figure 4 In the figure, (a) is the curve of efficiency as a function of coverage. Figure 4 (b) in the figure shows the curve of efficiency ratio as a function of coverage rate.

[0035] Then, the probability enhancement factor Λ is calculated using the following formula: Λ=f_ss / f_Cov; When Λ>1, it indicates that the target defect region has predictive ability for plastic events. See also Figure 5 As shown, Figure 5 This is a schematic diagram illustrating the change of the probability enhancement factor with the normalized strain threshold; where, Figure 5 (a) in the figure represents polycrystalline Cu. Figure 5 (b) in the figure represents polycrystalline Nb3Sn. Figure 5 (c) in the text is a quasicrystal. Figure 5 (d) in the figure represents the KA amorphous system.

[0036] In this embodiment, the plastic deformation prediction based on the target defect region can be performed as follows: First, the target quantity is determined based on the product of the target coverage and the preset quantity, and the target number of target atoms is selected from the arranged atoms in ascending order; then, the target feature vector corresponding to the preset dynamic matrix of the target atom is determined, and the target vector corresponding to the non-affine displacement of the target atom is determined; finally, the correlation degree between the tensor of the target feature vector and the target vector is determined, and the direction of plastic deformation is predicted based on the correlation degree.

[0037] Specifically, in this embodiment, the target atoms in the target defect region are first calculated, and the target eigenvectors corresponding to the preset dynamic matrix of the target atoms, i.e., the low-frequency eigenvectors, are determined. Then, the target vectors corresponding to the non-affine displacements of the target atoms are determined. The direction of plastic deformation is predicted based on the correlation between the tensors of the target eigenvectors and the target vectors. The formula for calculating the correlation is as follows: C=(3 / 2)tr(E^(i)V); Where C represents the correlation degree, tr(·) is the trace of the matrix, E^(i) is the tensor obtained by symmetrizing the target vector corresponding to target atom i, and V is the target eigenvector. The correlation degree C can reach 0.38-0.6, confirming the predictive ability of the eigenvector for the direction of plastic deformation. See also Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the correlation between the eigenvectors and displacement vectors as a function of the normalized strain threshold; where, Figure 6 (a) in the figure represents polycrystalline Cu. Figure 6 (b) in the figure represents polycrystalline Nb3Sn. Figure 6 (c) in the text is a quasicrystal. Figure 6 (d) in the figure represents the KA amorphous system.

[0038] Finally, the defect identification results and plasticity prediction results can be output, including the spatial distribution of the target defect area, the location of the target defect area, the plasticity event probability enhancement factor Λ, and the predicted plastic deformation direction.

[0039] As can be seen from the above, in this embodiment, the first configuration data corresponding to the solid material to be analyzed is first obtained, and the first configuration data is subjected to energy minimization processing to obtain the corresponding second configuration data; then, a preset dynamic matrix corresponding to each atom in the second configuration data is constructed, and the target equivalent stiffness parameter of each atom is determined based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, and the neighboring atom is an atom whose distance from the atom satisfies the preset distance condition; the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, and the local region is the region determined based on the atom and its neighboring atoms; then, the target equivalent stiffness parameter of each atom is coarsened to construct the corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material; finally, the target defect region in the solid material is identified based on the target equivalent stiffness field, and plastic deformation is predicted based on the target defect region. As can be seen from the above, this embodiment first obtains the first configuration data of the solid material to be analyzed and then obtains the second configuration data through energy minimization processing. Next, based on the interaction potential energy of each atom and its nearest neighbor in the second configuration data, a preset dynamic matrix corresponding to each atom is constructed. Based on the preset dynamic matrix, the target equivalent stiffness parameter characterizing the local relative stiffness of the atom is further calculated. Subsequently, the target equivalent stiffness parameter is coarsened to construct a target equivalent stiffness field that reflects the internal spatial stiffness distribution of the material. Finally, based on the target equivalent stiffness field, the target defect region in the solid material is accurately identified, and plastic deformation is predicted. In this way, this embodiment can clearly characterize the internal stiffness differences of the solid material, accurately locate the defect, and provide a reliable prediction of the position and direction of plastic deformation. It can achieve universal, efficient, and accurate prediction of plastic deformation in solid materials with multiple structures, possessing strong versatility and engineering practical value.

[0040] Next, taking the edge dislocation in an FCC (Face-Centered Cubic) copper single crystal as an example, the implementation process of the present invention will be explained.

[0041] like Figure 2 As shown in (a), an FCC Cu single-crystal model containing edge dislocations was constructed. The system size is approximately 103 × 125 × 13 ų, containing 14,580 atoms. The EAM potential was used to describe the interatomic interactions. The relaxation configuration was obtained after minimizing the conjugate gradient energy. The preset dynamic matrix and target equivalent stiffness parameters of each atom were calculated, and the target CGES field was constructed using a 6 Å coarse-grained radius. Figure 3 As shown in (a1) and (a2), the spatial distribution of the gradient magnitude clearly reveals the location of the dislocation core. AQS shear simulations were performed up to a large strain, as... Figure 3As shown in (a3), the non-affine displacement field highly coincides with the CGES gradient field. The results indicate that when the optimized coverage is approximately 15%, the probability enhancement factor Λ is approximately 7, meaning the probability of a plastic event occurring within the target defect region is 7 times that in the bulk region. Figure 6 As shown in (a), the tensor correlation C between the low-frequency eigenvector of the preset dynamics matrix of atoms in the target defect region and the non-affine displacement vector of plastic deformation is calculated. Even at the zero strain threshold, C≈0.42, which confirms the predictive ability of the eigenvector for the direction of plastic deformation. It can be seen that this embodiment has the following beneficial effects: (1) Plastic deformation can be predicted without preloading; (2) High computational efficiency, the computational complexity of the preset dynamics matrix is ​​O(d²N), which is significantly lower than O((dN)²) of the complete dynamics matrix; (3) Wide applicability, uniformly applicable to various structural types such as single crystal, polycrystalline, quasicrystalline and amorphous solids; (4) Clear physical meaning, able to accurately locate defects such as dislocation cores, grain boundaries, and shear transition regions; (5) High prediction accuracy, the probability of plastic events occurring in the defect region is 3-7 times that in the bulk region; (6) Supports material design, and can provide microscopic theoretical guidance for material composition optimization, structural design and performance control.

[0042] Accordingly, see Figure 7 As shown in the illustration, this application also provides a device for predicting the plastic deformation of solid materials, which may include: Configuration data acquisition module 11 is used to acquire the first configuration data corresponding to the solid material to be analyzed, and to perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data; The parameter determination module 12 is used to construct a preset dynamic matrix corresponding to each atom in the second configuration data, and to determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atoms being atoms whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region being a region determined based on the atom and its neighboring atoms; The equivalent stiffness field construction module 13 is used to coarse-grain the target equivalent stiffness parameters of each atom to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material; The plastic deformation prediction module 14 is used to identify target defect regions in the solid material based on the target equivalent stiffness field, and to predict plastic deformation based on the target defect regions.

[0043] In some specific embodiments, the configuration data acquisition module 11 may include: An energy minimization unit is used to perform energy minimization processing on the first configuration data based on the conjugate gradient method or the steepest descent method to obtain the corresponding second configuration data.

[0044] In some specific embodiments, the parameter determination module 12 may include: The neighboring atom determination unit is used to determine the preset distance condition based on a preset distance threshold for any atom in the second configuration data, and to determine the neighboring atom that satisfies the preset distance condition; the preset distance condition is used to control the distance between the atom and the neighboring atom to be less than or equal to the preset distance threshold; The dynamic matrix determination unit is used to determine the second-order partial derivative matrix corresponding to the interaction potential energy between the atom and its neighboring atoms, and to determine the second-order partial derivative matrix as the preset dynamic matrix of the atom. The parameter determination unit is used to calculate the determinant of the preset dynamic matrix and calculate the target equivalent stiffness parameter of the atom based on the determinant.

[0045] In some specific embodiments, the equivalent stiffness field construction module 13 may include: A spherical region construction unit is used to construct a target spherical region corresponding to any one of the atoms, with the atom as the center of a sphere, based on a preset radius, and to perform a weighted average of the target equivalent stiffness parameters of each atom in the target spherical region using a preset Gaussian weighting function. An equivalent stiffness field construction unit is used to construct the target equivalent stiffness field based on the weighted average result corresponding to each atom.

[0046] In some specific embodiments, the plastic deformation prediction module 14 may include: The defect region identification unit is used to identify the target defect region based on the first-order gradient of the target equivalent stiffness field if the structural characteristics of the solid material are long-range ordered; and to identify the target defect region based on the second-order gradient of the target equivalent stiffness field if the structural characteristics of the solid material are short-range ordered or disordered.

[0047] In some specific embodiments, the device for predicting the plastic deformation of the solid material may further include: The initial coverage setting module is used to determine the first deformed atom in each atom, arrange each atom in descending order according to the gradient or divergence magnitude corresponding to each atom, and set several initial coverage rates; An initial efficiency determination module is used to determine an initial quantity based on the product of the initial coverage and a preset quantity for any given initial coverage, select the initial quantity of initial atoms from the arranged atoms in ascending order, determine the second deformed atoms in the initial atoms, determine an initial ratio based on the quotient of the number of the second deformed atoms and the number of the first deformed atoms, and determine the initial efficiency corresponding to the initial coverage based on the difference between the initial ratio and the initial coverage; wherein, the deformed atom is an atom that undergoes plastic deformation; The target ratio determination module is used to determine the initial coverage rate corresponding to the maximum initial efficiency as the target coverage rate, and to determine the initial ratio corresponding to the target coverage rate as the target ratio; The ratio calculation module is used to calculate the ratio between the target proportion and the target coverage. If the ratio is greater than 1, plastic deformation prediction is performed based on the target defect area.

[0048] In some specific embodiments, the plastic deformation prediction module 14 may include: The target atom selection unit is used to determine the target quantity based on the product of the target coverage and the preset quantity, and to select the target quantity of target atoms from the arranged atoms in ascending order; The target vector determination unit is used to determine the target feature vector corresponding to the preset dynamic matrix of the target atom, and to determine the target vector corresponding to the non-affine displacement of the target atom; The direction prediction unit is used to determine the correlation degree between the target feature vector and the target vector tensor, and predict the direction of plastic deformation based on the correlation degree.

[0049] Furthermore, embodiments of this application also disclose an electronic device, Figure 8 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device 20 may 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. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the solid material plastic deformation prediction method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0050] In this embodiment, the power supply 23 is used to provide operating 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 external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire 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.

[0051] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0052] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the method for predicting the plastic deformation of solid materials executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0053] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for predicting the plastic deformation of solid materials. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0055] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0057] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the plastic deformation of a solid material, characterized in that, include: Obtain the first configuration data corresponding to the solid material to be analyzed, and perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data; Construct a preset dynamic matrix corresponding to each atom in the second configuration data, and determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atom being an atom whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region being a region determined based on the atom and its neighboring atoms; The target equivalent stiffness parameters of each atom are coarsened to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material. The target defect region in the solid material is identified based on the target equivalent stiffness field, and plastic deformation is predicted based on the target defect region.

2. The method for predicting plastic deformation of solid materials according to claim 1, characterized in that, The step of performing energy minimization processing on the first configuration data to obtain the corresponding second configuration data includes: Based on the conjugate gradient method or the steepest descent method, the energy minimization process is applied to the first configuration data to obtain the corresponding second configuration data.

3. The method for predicting plastic deformation of solid materials according to claim 1, characterized in that, The step of constructing a preset dynamic matrix corresponding to each atom in the second configuration data, and determining the target equivalent stiffness parameter of each atom based on each preset dynamic matrix, includes: For any atom in the second configuration data, a preset distance condition is determined based on a preset distance threshold, and neighboring atoms that satisfy the preset distance condition are identified; the preset distance condition is used to control the distance between the atom and the neighboring atom to be less than or equal to the preset distance threshold. Determine the second-order partial derivative matrix corresponding to the interaction potential energy between the atom and its neighboring atoms, and use the second-order partial derivative matrix as the preset dynamic matrix of the atom; Calculate the determinant of the preset dynamic matrix, and calculate the target equivalent stiffness parameter of the atom based on the determinant.

4. The method for predicting plastic deformation of solid materials according to claim 1, characterized in that, The coarsening of the target equivalent stiffness parameters of each atom to construct the corresponding target equivalent stiffness field includes: For any one of the atoms, the atom is taken as the center of a sphere, and a target spherical region corresponding to the center of the sphere is constructed based on a preset radius. The target equivalent stiffness parameters of each atom in the target spherical region are weighted and averaged using a preset Gaussian weighting function. The target equivalent stiffness field is constructed based on the weighted average result corresponding to each atom.

5. The method for predicting plastic deformation of solid materials according to claim 1, characterized in that, The step of identifying the target defect region in the solid material based on the target equivalent stiffness field includes: If the structural characteristics of the solid material are long-range ordered, then the target defect region is identified based on the first-order gradient of the target equivalent stiffness field; If the structural characteristics of the solid material are short-range ordered or disordered, the target defect region is identified based on the second-order gradient of the target equivalent stiffness field.

6. The method for predicting plastic deformation of solid materials according to any one of claims 1 to 5, characterized in that, Before performing plastic deformation prediction based on the target defect region, the method further includes: The first deformed atom in each of the atoms is determined, and the atoms are arranged in descending order according to the gradient or divergence magnitude corresponding to each atom, and several initial coverage rates are set. For any of the initial coverage rates, an initial quantity is determined based on the product of the initial coverage rate and a preset quantity. Then, the initial quantity of initial atoms is selected from the arranged atoms in ascending order. Second deformed atoms are determined from the initial atoms. An initial ratio is determined based on the quotient of the number of second deformed atoms and the number of first deformed atoms. Finally, an initial efficiency corresponding to the initial coverage rate is determined based on the difference between the initial ratio and the initial coverage rate. Wherein, deformed atoms are atoms that undergo plastic deformation. The initial coverage rate corresponding to the highest initial efficiency is determined as the target coverage rate, and the initial ratio corresponding to the target coverage rate is determined as the target ratio; Calculate the ratio between the target proportion and the target coverage. If the ratio is greater than 1, then predict plastic deformation based on the target defect area.

7. The method for predicting plastic deformation of solid materials according to claim 6, characterized in that, The plastic deformation prediction based on the target defect region includes: The target quantity is determined based on the product of the target coverage and the preset quantity, and the target quantity of target atoms is selected from the arranged atoms in ascending order. Determine the target eigenvector corresponding to the preset dynamic matrix of the target atom, and determine the target vector corresponding to the non-affine displacement of the target atom; Determine the correlation degree between the tensor of the target feature vector and the target vector, and predict the direction of plastic deformation based on the correlation degree.

8. A device for predicting the plastic deformation of a solid material, characterized in that, include: The configuration data acquisition module is used to acquire the first configuration data corresponding to the solid material to be analyzed, and to perform energy minimization processing on the first configuration data to obtain the corresponding second configuration data. The parameter determination module is used to construct a preset dynamic matrix corresponding to each atom in the second configuration data, and to determine the target equivalent stiffness parameter of each atom based on each preset dynamic matrix; the preset dynamic matrix is ​​a matrix determined based on the interaction potential energy between the atom and its neighboring atoms, the neighboring atoms being atoms whose distance from the atom satisfies a preset distance condition, and the target equivalent stiffness parameter is used to characterize the relative stiffness of the local region corresponding to the atom, the local region being a region determined based on the atom and its neighboring atoms; An equivalent stiffness field construction module is used to coarse-grain the target equivalent stiffness parameters of each atom to construct a corresponding target equivalent stiffness field; the target equivalent stiffness field is used to characterize the relative stiffness distribution of each spatial position inside the solid material; The plastic deformation prediction module is used to identify target defect regions in the solid material based on the target equivalent stiffness field, and to predict plastic deformation based on the target defect regions.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, which is loaded and executed by the processor to implement the method for predicting the plastic deformation of solid materials as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the method for predicting plastic deformation of solid materials as described in any one of claims 1 to 7.

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