Prediction method and device for mechanical property of material interface phase, electronic equipment and storage medium

Abstract

The invention provides a material interface phase mechanical property prediction method and device, electronic equipment and a storage medium, and the prediction method comprises the steps: obtaining microscopic parameters corresponding to a test sample, the microscopic parameters comprising a filler mechanical parameter, a matrix mechanical parameter and an interface estimation parameter; based on the microcosmic parameters, macroscopic parameters corresponding to the microcosmic parameters are obtained through high-throughput calculation; by taking the microscopic parameters as input parameters and the macroscopic parameters as output parameters, generating a prediction model corresponding to the macroscopic parameters; and taking values of to-be-tested microscopic parameters of a to-be-tested sample, substituting the values into the prediction model, and carrying out reverse screening on the to-be-tested microscopic parameters of the to-be-tested sample to obtain the interface mechanical properties corresponding to the to-be-tested sample. According to the prediction method, a prediction model relationship between the microscopic parameters and the macroscopic parameters is established through means such as high-throughput calculation, so that the microscopic parameters are deduced reversely through macroscopic calculation and testing. The prediction method is low in operation cost, simple to operate and high in accuracy of obtained numerical values.

Description

Technical Field

[0001] The invention belongs to the field of material analysis and relates to a method, a device, an electronic device and a storage medium for predicting the mechanical properties of a material interface phase. Background Art

[0002] Among composite materials, composite materials composed of fillers and matrix materials are common. For such composite materials, the determination of macroscopic parameters, such as mechanical properties, thermodynamic properties and electrical properties, can be tested using conventional testing methods and devices. However, in the prior art, there is no accurate and easy-to-operate testing method for the determination of microscopic parameters of such composite materials, especially the microscopic parameters of the filler-matrix interface.

[0003] Epoxy molding compound (EMC) used in advanced electronic packaging structures is a common composite material composed of filler and matrix material. The most widely used epoxy molding compound is a composite material composed of epoxy resin and SiO2, of which SiO2 accounts for about 60% to 90% in epoxy molding compound. On the one hand, SiO2 filler can increase the Young's modulus and thermal conductivity of epoxy resin; on the other hand, the increase in filler content will reduce the thermal expansion coefficient of epoxy molding compound. The thermodynamic properties of epoxy molding compound, including Young's modulus, Poisson's ratio, thermal conductivity, thermal expansion coefficient, etc., further affect the reliability of packaged devices. Although various experimental schemes for formulation design have been discussed, various data have not formed a database, that is, all current experiments are discrete trial-and-error attempts, and cannot quantitatively select material components and optimize performance. In order to explore the influence of material micro-formulation structure on macro-mechanics more quickly and efficiently, many theoretical models and numerical models have been proposed: the commonly used Mori-Tanaka, Hashin–Shtrikman bounds, self-consistent model and other methods, however, these methods cannot fully consider the complex random structure in the homogenization analysis of parameters such as Young's modulus. Numerical models can overcome these shortcomings well. For example, the finite element method or the method based on fast Fourier transform can perform global field numerical simulation on complex composite material systems and obtain the local stress and strain distribution of materials.

[0004] At present, whether it is based on theoretical models or numerical models, the accurate prediction of mechanical parameters needs to consider the influence of the mechanical size thickness and mechanical parameters of the interface phase. However, although the thickness of the interface phase can be distinguished by atomic force microscopy, the measurement of mechanical parameters such as Young's modulus and Poisson's ratio of the filler-matrix interface is still a difficult problem. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art, the present invention provides a prediction method, device, electronic device and storage medium for the mechanical properties of material interface phases. The prediction method establishes a prediction model relationship between microscopic parameters and macroscopic parameters by means of high-throughput calculation, thereby realizing the reverse deduction of microscopic parameters through macroscopic calculation and testing. The prediction method has low operating cost, simple operation and high numerical accuracy.

[0006] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0007] One of the purposes of the present invention is to provide a method for predicting the mechanical properties of a material interface phase, the prediction method comprising:

[0008] Obtaining microscopic parameters corresponding to the test sample, the microscopic parameters include filler mechanical parameters, matrix mechanical parameters and interface estimation parameters;

[0009] Based on the microscopic parameters, high-throughput calculation is used to obtain the macroscopic parameters corresponding to the microscopic parameters;

[0010] Taking microscopic parameters as input parameters and macroscopic parameters as output parameters, a prediction model corresponding to the macroscopic parameters is generated;

[0011] The measured microscopic parameters of the sample to be tested are taken, and the values ​​are substituted into the prediction model to perform reverse screening on the measured microscopic parameters of the sample to be tested, so as to obtain the interface mechanical properties corresponding to the sample to be tested.

[0012] As a preferred technical solution of the present invention, high-throughput calculation includes: taking values ​​of filler mechanical parameters, matrix mechanical parameters and interface estimation parameters within a reasonable range, arranging and combining different values ​​to obtain a microscopic parameter database, and performing high-throughput calculation with the microscopic parameter database to obtain a macroscopic parameter database.

[0013] As a preferred technical solution of the present invention, the method for generating a prediction model corresponding to a macro parameter includes machine learning.

[0014] As a preferred technical solution of the present invention, the machine learning method includes:

[0015] Taking filler mechanical parameters, matrix mechanical parameters and interface estimation parameters as initial features, generating the first prototype function corresponding to each initial feature;

[0016] Perform operations and combinations on the initial features to obtain several feature combinations;

[0017] For each feature combination and the first prototype function, generate a second prototype function;

[0018] Screening the second prototype function to obtain a second preferred prototype function;

[0019] The prediction model is obtained by performing curve fitting on the second preferred prototype function.

[0020] As a preferred technical solution of the present invention, for each of the feature combinations and the first prototype function, generating a second prototype function includes: screening the first prototype function corresponding to each initial feature to obtain a first preferred prototype function; and generating a second prototype function for each feature combination and the first preferred prototype function.

[0021] As a preferred technical solution of the present invention, the method for screening the first prototype function and the second prototype function includes determination coefficient screening.

[0022] As a preferred technical solution of the present invention, the reverse screening method includes: taking values ​​of microscopic parameters to be measured and substituting them into the prediction model to obtain predicted macroscopic parameters of the sample to be measured;

[0023] Compare the predicted macro parameters with the actual macro parameters of the sample to be tested, and screen out qualified macro parameters;

[0024] The measured microscopic parameters corresponding to the qualified macroscopic parameters are used as the prediction results of the interface mechanical properties;

[0025] The mechanical property value of the interface in the measured micro-parameter corresponding to the predicted macro-parameter closest to the actual macro-parameter is taken as the final result.

[0026] A second object of the present invention is to provide a device for predicting the mechanical properties of a material interface phase, the device comprising:

[0027] A parameter acquisition module is used to obtain the microscopic parameters corresponding to the test sample;

[0028] A high-throughput calculation module, used for obtaining macroscopic parameters corresponding to the microscopic parameters by high-throughput calculation based on the microscopic parameters;

[0029] A prediction model generation module is used to generate a prediction model corresponding to the macro parameters by taking the micro parameters as input parameters and the macro parameters as output parameters;

[0030] The reverse screening module takes values ​​of the microscopic parameters of the sample to be tested, brings them into the prediction model to reversely screen the microscopic parameters of the sample to be tested, and obtains the interface mechanical properties corresponding to the sample to be tested.

[0031] A third object of the present invention is to provide an electronic device, the electronic device comprising:

[0032] at least one processor; and a memory communicatively coupled to the at least one processor;

[0033] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the mechanical properties of the material interface phase provided by one of the purposes.

[0034] Compared with the prior art, the present invention has at least the following beneficial effects:

[0035] (1) The present invention provides a method for predicting the mechanical properties of a material interface phase. The method establishes a predictive model relationship between microscopic parameters and macroscopic parameters by means of high-throughput calculations, thereby realizing the reverse deduction of microscopic parameters through macroscopic calculations and tests.

[0036] (2) The present invention provides a method for predicting the mechanical properties of a material interface phase. The method has low operating costs, simple operation, and accurate numerical values. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the structure of the structural model provided in Example 1 of the present invention.

[0038] Figure 2 A flowchart of the machine learning method provided in Example 1 of the present invention.

[0039] Figure 3 This is a schematic diagram of an electronic device provided in Example 3.

[0040] Figure 3 In it, 10-electronic device, 11-processor, 12-ROM, 13-RAM, 14-bus, 15-I / O interface, 16-input unit, 17-output unit, 18-storage unit, 19-communication unit.

[0041] The present invention is further described in detail below. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims. DETAILED DESCRIPTION

[0042] The technical solution of the present application is further described below through specific implementation methods.

[0043] A specific embodiment of the present invention provides a method for predicting the mechanical properties of a material interface phase, the prediction method comprising:

[0044] Obtaining microscopic parameters corresponding to the test sample, the microscopic parameters include filler mechanical parameters, matrix mechanical parameters and interface estimation parameters;

[0045] Based on the microscopic parameters, high-throughput calculation is used to obtain the macroscopic parameters corresponding to the microscopic parameters;

[0046] Taking microscopic parameters as input parameters and macroscopic parameters as output parameters, a prediction model corresponding to the macroscopic parameters is generated;

[0047] The measured microscopic parameters of the sample to be tested are taken, and the values ​​are substituted into the prediction model to perform reverse screening on the measured microscopic parameters of the sample to be tested, so as to obtain the interface mechanical properties corresponding to the sample to be tested.

[0048] In a specific embodiment of the present invention, the microscopic parameters of the test sample can be obtained by establishing a standard model. For example, multiple standard models of a matrix material with a filler are prepared, and the filler filling degree of each standard model is different from each other, and the size and mechanical properties of the filler of each standard model are measured; optionally, the filler-matrix interface phase thickness is further measured.

[0049] In a specific embodiment of the present invention, a plurality of entity models may be provided, and the filler filling degree of each entity model is different from each other. For example, the number of entity models may be 2, 3, 4, 5, 6, 7 or 8, and the filler filling degree (volume fraction) may be 5%, 10%, 15%, 20%, 25% or 30%, etc., but is not limited to the listed values, and other values ​​not listed within the above-mentioned value ranges are also applicable.

[0050] In a specific embodiment of the present invention, the matrix materials commonly used in composite materials are suitable for the present method, such as epoxy resin, acrylic resin, silicone resin, phenolic resin and isocyanate resin.

[0051] In a specific embodiment of the present invention, the filler is generally selected as filler particles, and the size of the filler refers to the particle size distribution of the filler particles.

[0052] In a specific embodiment of the present invention, commonly used fillers of composite materials are suitable for this method, such as silicon dioxide, calcium carbonate or silicon dioxide.

[0053] In one specific embodiment of the present invention, the particle size distribution of the filler can be measured using a Malvern tester.

[0054] In a specific embodiment of the present invention, the mechanical properties of the solid model can be measured using a mechanical universal testing machine.

[0055] In a specific embodiment of the present invention, the mechanical properties of the solid model include Young's modulus and / or Poisson's ratio.

[0056] In a specific embodiment of the present invention, the filler-matrix interface thickness of the solid model can be measured using an atomic force microscope.

[0057] In a specific embodiment of the present invention, high-throughput calculation includes: taking values ​​of filler mechanical parameters, matrix mechanical parameters and interface estimation parameters within a reasonable range, arranging and combining different values ​​to obtain a micro parameter database, and performing high-throughput calculation with the micro parameter database to obtain a macro parameter database.

[0058] In a specific embodiment of the present invention, before high-throughput calculation is performed, a structural model can be established according to the filling ratio of the solid model, and filler mechanical parameters, matrix mechanical parameters and interface estimation parameters can be determined according to the test results of the solid model.

[0059] In a specific embodiment of the present invention, in the structural model, the fillers are completely randomly distributed, and the size of the fillers and the thickness of the filler-matrix interface phase meet the measurement results of the solid model.

[0060] In a specific embodiment of the present invention, the reasonable interval refers to the range of values ​​of mechanical properties that meet the physical properties and chemical properties of the filler, the matrix and the filler-matrix interface. The number of values ​​can be selected according to the specific needs of high-throughput calculations and is not further limited here. As an example, the number of values ​​of the mechanical properties of the filler is 3 to 10, the number of values ​​of the mechanical properties of the matrix is ​​3 to 10, and the number of values ​​of the mechanical properties of the filler-matrix interface is 20 to 200.

[0061] In a specific embodiment of the present invention, the Poisson's ratio of the filler and the matrix and the thickness of the filler-matrix interface also need to be assigned in the high-throughput calculation, and the assignment can be performed based on the measurement results of the solid model.

[0062] In one specific embodiment of the present invention, the method of generating a prediction model corresponding to a macro parameter includes machine learning.

[0063] In one specific embodiment of the present invention, the machine learning method includes:

[0064] Taking filler mechanical parameters, matrix mechanical parameters and interface estimation parameters as initial features, generating the first prototype function corresponding to each initial feature;

[0065] Perform operations and combinations on the initial features to obtain several feature combinations;

[0066] For each feature combination and the first prototype function, generate a second prototype function;

[0067] Screening the second prototype function to obtain a second preferred prototype function;

[0068] The prediction model is obtained by performing curve fitting on the second preferred prototype function.

[0069] In a specific implementation of the present invention, the prototype function may include f(x)=k1x, f(x)=k2x -1 , f(x)=k3x 1 / 2 , f(x)=k4x -1 / 2 , f(x)=k5x 2 , f(x)=k6x -2 , f(x)=k7x 3 , f(x)=k8x -3 , f(x)=k9ln(x), f(x)=k 10 / ln(x),f(x)=k 11 e x , f(x)=k 12 e -x Etc., but not limited to the above prototype functions, other suitable prototype functions can also be applied to this method.

[0070] In a specific implementation of the present invention, the first prototype function corresponding to each initial feature is screened to obtain a first preferred prototype function; and a second prototype function is generated for each feature combination and the first preferred prototype function.

[0071] In a specific embodiment of the present invention, the method for screening the first prototype function and the second prototype function includes determination coefficient screening.

[0072] In a specific implementation of the present invention, the least squares regression method is used to calculate the determination coefficient of each prototype function in the first prototype function group of each feature, and the first preferred prototype function is selected according to the determination coefficient.

[0073] In one embodiment of the present invention, the least squares regression method is used to calculate the coefficient of determination (R 2 ) method is to calculate the residual sum of squares between the actual value and the model predicted value under given input parameters.

[0074] In one embodiment of the present invention, the least squares regression method is used to calculate the coefficient of determination (R 2 ) can be expressed as follows:

[0075]

[0076] Where n is the number of samples, y i is the actual measured value, is the predicted value of the model, is the average value of the actual measured values.

[0077] In a specific implementation of the present invention, at least three prototype functions are selected from the first prototype function as the first preferred prototype function. For example, the number of prototype functions in the first optimal prototype function group can be 3, 4, 5, 6, and 7, but is not limited to the listed values, and other values ​​not listed within the numerical range are also applicable.

[0078] In a specific embodiment of the present invention, the method of performing calculation combination on the initial features includes addition, subtraction, multiplication, division, or taking the reciprocal, natural logarithm, square, cube and then performing addition, subtraction, multiplication, and division operations, preferably, each feature is arranged and combined and then multiplied. As an example, the mechanical properties of the filler, the matrix and the filler-matrix interface are set to be x1, x2 and x3 respectively, then the feature combinations obtained after the arrangement and combination are multiplied are x1, x2, x3, x1*x2, x1*x3, x2*x3 and x1*x2*x3.

[0079] In a specific implementation of the present invention, the first preferred prototype function of each feature is combined by operation and forms a second prototype function with the first preferred prototype function.

[0080] In a specific implementation of the present invention, the least squares regression method is used to calculate the determination coefficient of each prototype function in the second prototype function of each feature combination, and the second preferred prototype function of each feature combination is screened out according to the determination coefficient.

[0081] In a specific embodiment of the present invention, at least 10 prototype functions are selected from the second prototype function as the second preferred prototype functions, such as 10, 12, 15, 18 and 20, etc., but are not limited to the listed values. Other unlisted values ​​within the above-mentioned numerical ranges are also applicable, and 15 is preferred.

[0082] In a specific implementation manner of the present invention, the curve fitting method is preferably a curve_fit method.

[0083] In a specific embodiment of the present invention, the second preferred prototype function is used to generate a prediction model between input parameters and output parameters using the curve_fit method, wherein the curve_fit method is from the SciPy documentation.

[0084] In one specific embodiment of the present invention, the reverse screening method includes: taking values ​​of microscopic parameters to be measured and substituting them into a prediction model to obtain predicted macroscopic parameters of the sample to be measured;

[0085] Compare the predicted macro parameters with the actual macro parameters of the sample to be tested, and screen out qualified macro parameters;

[0086] The measured microscopic parameters corresponding to the qualified macroscopic parameters are used as the prediction results of the interface mechanical properties;

[0087] The mechanical property value of the interface in the measured micro-parameter corresponding to the predicted macro-parameter closest to the actual macro-parameter is taken as the final result.

[0088] In a specific embodiment of the present invention, taking values ​​of the microscopic parameters to be measured refers to taking values ​​of the mechanical properties of the filler, the matrix and the filler-matrix interface.

[0089] In a specific embodiment of the present invention, according to the actual mechanical properties of the filler and the matrix, a reasonable range of mechanical properties of the filler-matrix interface can be obtained, and values ​​are taken within the reasonable range at smaller intervals (such as 0.1 GPa), and the actual mechanical properties of the filler and the matrix and the values ​​of the mechanical properties of the filler-matrix interface are used as input parameters, and brought into the prediction model to obtain the prediction results of the mechanical property parameters of the structural model. The value of the mechanical properties of the filler-matrix interface in the input parameters corresponding to the prediction result closest to the actual result is the prediction result of the mechanical properties of the final filler-matrix interface.

[0090] To better illustrate the present invention and facilitate understanding of the technical solution of the present invention, typical but non-limiting embodiments of the present invention are as follows:

[0091] Example 1

[0092] This embodiment provides a method for predicting mechanical properties of an interface phase, the prediction method comprising:

[0093] Preparation of solid models: Using silica particles as filler and epoxy resin as matrix, solid models with filler volume filling rates of 20%, 30% and 40% were prepared by high-speed stirring, and 2% by mass of silane coupling agent was added to ensure the dispersion of filler particles. The silane coupling agent was Z-6040. The particle size distribution of silica particles was determined by Malvern tester, the Young's modulus and Poisson's ratio of the solid model were tested by mechanical universal testing machine, and the filler-matrix interface phase thickness was measured by atomic force microscopy.

[0094] Establishment of structural model: Establish the structural model according to the filling ratio of the solid model. In the structural model, the fillers are completely randomly distributed, and the size of the fillers and the thickness of the filler-matrix interface phase meet the measurement results of the solid model. Figure 1 shown.

[0095] High-throughput calculation: The Young's modulus of silica particles was taken as 65 GPa, 70 GPa and 75 GPa, and the Young's modulus of epoxy resin matrix was taken as 1.5 GPa, 2 GPa and 2.5 GPa, respectively. The Poisson's ratio of silica particles was 0.17, and the Poisson's ratio of epoxy resin matrix was 0.37. The thickness of silica particle-epoxy interface phase was 71.3 nm, and the Young's modulus of silica particle-epoxy interface phase was taken in the range of 2.98 to 71 GPa. 100 values ​​were randomly selected to obtain an input parameter database with 900 sets of digital parameters. The data of the input parameter database was used as input parameters for high-throughput calculation, and the Young's modulus of the structural model was used as the output parameter.

[0096] Machine Learning: Its process is as follows Figure 2 As shown, the Young's modulus of the silica particles, the epoxy resin matrix, and the interface of the silica particles-epoxy resin matrix are set as features, which are x1, x2, and x3, respectively. Each feature generates 12 prototype functions, and the first prototype function group of each feature is obtained. The prototype functions include f(x) = k1x, f(x) = k2x -1 , f(x)=k3x 1 / 2 , f(x)=k4x -1 / 2 , f(x)=k5x 2 , f(x)=k6x -2 , f(x)=k7x 3 , f(x)=k8x -3 , f(x)=k9ln(x), f(x)=k 10 / ln(x),f(x)=k 11 e x , f(x)=k 12 e -x , where f(x) represents the output parameter, x is the characteristic parameter, k1~k 12 The coefficient is calculated using the least squares regression (LSR) method for all the prototype functions in each feature, and the three prototype functions with the largest determination coefficients are selected to form the first optimal prototype function group for the corresponding feature;

[0097] Arrange and combine the above three features and perform multiplication operations to obtain feature combinations, which include x1, x2, x3, x1*x2, x1*x3, x2*x3 and x1*x2*x3. Substitute each prototype function in the first optimal prototype function group of each feature into each feature combination to obtain the second prototype function group of each feature combination. Use the least squares regression method to calculate the determination coefficient of the prototype function of the second prototype function group of each feature combination, and select the three prototype functions with the largest determination coefficients to form the second optimal prototype function group of the corresponding feature combination;

[0098] The determination coefficients of the prototype functions in the second optimal prototype function group of each feature combination and the first optimal prototype function group of each feature are compared, and the 15 prototype functions with the largest determination coefficients are selected to obtain the third optimal prototype function group; the curve_fit method is used to generate a prediction model between the input parameters and the output parameters of the third optimal prototype function group, and the model coefficients are obtained.

[0099] Reverse screening: According to the actual Young's modulus of the carbon dioxide particles and the epoxy resin matrix, the reasonable range of the Young's modulus of the carbon dioxide particle-epoxy resin interface can be obtained to be 2.98~71GPa, and the value is taken within 2.98~71GPa at an interval of 0.1GPa, and the actual Young's modulus of the carbon dioxide particles and the epoxy resin matrix and the value of the Young's modulus of the carbon dioxide particle-epoxy resin interface are used as input parameters, and brought into the prediction model to obtain the prediction result of the Young's modulus of the structural model. The value of the Young's modulus of the carbon dioxide particle-epoxy resin interface in the input parameter corresponding to the prediction result closest to the Young's modulus measurement result of the physical model corresponding to the structural model is the final prediction result of the Young's modulus of the carbon dioxide particle-epoxy resin interface.

[0100] Example 2

[0101] This embodiment provides a device for predicting mechanical properties of a material interface phase, the device comprising:

[0102] A parameter acquisition module is used to obtain the microscopic parameters corresponding to the test sample;

[0103] A high-throughput calculation module, used for obtaining macroscopic parameters corresponding to the microscopic parameters by high-throughput calculation based on the microscopic parameters;

[0104] A prediction model generation module is used to generate a prediction model corresponding to the macro parameters by taking the micro parameters as input parameters and the macro parameters as output parameters;

[0105] The reverse screening module takes values ​​of the microscopic parameters of the sample to be tested, brings them into the prediction model to reversely screen the microscopic parameters of the sample to be tested, and obtains the interface mechanical properties corresponding to the sample to be tested.

[0106] Example 3

[0107] This embodiment provides an electronic device, such as Figure 3, which is a schematic diagram of the structure of an electronic device 10 for implementing an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0108] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory ROM12, a random access memory RAM13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory ROM12 or the computer program loaded from the storage unit 18 to the random access memory RAM13. In RAM13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM12 and RAM13 are connected to each other through a bus 14; the I / O interface 15 is also connected to the bus 14.

[0109] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0110] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the prediction method for the mechanical properties of the interface phase provided in Example 1.

[0111] In some embodiments, the method for predicting the mechanical properties of the interface phase may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting the mechanical properties of the interface phase may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for predicting the mechanical properties of the interface phase in any other appropriate manner (e.g., by means of firmware).

[0112] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0114] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0118] The applicant declares that the present invention illustrates the detailed structural features of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned detailed structural features, that is, it does not mean that the present invention must rely on the above-mentioned detailed structural features to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the components selected by the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

[0119] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0120] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0121] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for predicting mechanical properties of material interface phases, characterized in that: The prediction method comprises: Obtaining microscopic parameters corresponding to the test sample, wherein the microscopic parameters include filler mechanical parameters, matrix mechanical parameters and interface estimation parameters; Based on the microscopic parameters, high-throughput calculation is used to obtain macroscopic parameters corresponding to the microscopic parameters; Taking the microscopic parameters as input parameters and the macroscopic parameters as output parameters, generating a prediction model corresponding to the macroscopic parameters; The microscopic parameters to be measured of the sample to be measured are taken, and the values ​​are substituted into the prediction model to perform reverse screening on the microscopic parameters to be measured of the sample to be measured, so as to obtain the interface mechanical properties corresponding to the sample to be measured.

2. The prediction method according to claim 1, characterized in that: The high-throughput calculation includes: taking values ​​of filler mechanical parameters, matrix mechanical parameters and interface estimation parameters within a reasonable range, arranging and combining different values ​​to obtain a microscopic parameter database, and performing high-throughput calculation with the microscopic parameter database to obtain a macroscopic parameter database.

3. The prediction method according to claim 1, characterized in that: The method for generating the prediction model corresponding to the macro parameter includes machine learning.

4. The prediction method according to claim 3, characterized in that: The machine learning method includes: Taking filler mechanical parameters, matrix mechanical parameters and interface estimation parameters as initial features, generating a first prototype function corresponding to each of the initial features; Performing calculations and combinations on the initial features to obtain a plurality of feature combinations; For each of the feature combinations and the first prototype function, generate a second prototype function; Screening the second prototype function to obtain a second preferred prototype function; Curve fitting is performed on the second preferred prototype function to obtain the prediction model.

5. The prediction method according to claim 4, characterized in that: For each of the feature combinations and the first prototype function, generating a second prototype function comprises: Screening the first prototype function corresponding to each of the initial features to obtain a first preferred prototype function; A second prototype function is generated for each of the feature combinations and the first preferred prototype function.

6. The prediction method according to claim 5, characterized in that: The method for screening the first prototype function and the second prototype function includes determination coefficient screening.

7. The method according to claim 1, characterized in that The reverse screening method comprises: taking values ​​of the microscopic parameters to be tested, and substituting them into the prediction model to obtain predicted macroscopic parameters of the sample to be tested; Comparing the predicted macroscopic parameters with the actual macroscopic parameters of the sample to be tested, and screening out qualified macroscopic parameters; The measured microscopic parameters corresponding to the qualified macroscopic parameters are used as the prediction results of the interface mechanical properties; The mechanical property value of the interface in the measured micro-parameter corresponding to the predicted macro-parameter closest to the actual macro-parameter is taken as the final result.

8. A device for predicting mechanical properties of material interface phases, characterized in that: The prediction device comprises: A parameter acquisition module is used to obtain the microscopic parameters corresponding to the test sample; A high-throughput calculation module, used for obtaining macroscopic parameters corresponding to the microscopic parameters by high-throughput calculation based on the microscopic parameters; A prediction model generation module, used to generate a prediction model corresponding to the macro parameter by taking the micro parameter as an input parameter and the macro parameter as an output parameter; The reverse screening module takes values ​​of the microscopic parameters of the sample to be tested, brings them into the prediction model to reversely screen the microscopic parameters of the sample to be tested, and obtains the interface mechanical properties corresponding to the sample to be tested.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the mechanical properties of the material interface phase according to any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the method for predicting the mechanical properties of the material interface phase according to any one of claims 1 to 7 is implemented.

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