Method, device, electronic equipment and medium for predicting mechanical properties of die-casting materials

Abstract

The present application relates to the field of material property prediction, and specifically to a method, device, electronic device and medium for predicting the mechanical properties of die-casting materials. The prediction method comprises: inputting the real strain of the die-casting sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the real value, and obtaining the MAT124 prediction model; inputting the equivalent failure strain of the die-casting sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the real value, and obtaining the GISSMO prediction model; inputting the strain of the die-casting material to be tested into the MAT124 prediction model, inputting the equivalent failure strain into the GISSMO prediction model, and obtaining the prediction results of the mechanical properties of the die-casting material to be tested. The present application can accurately predict the mechanical properties of automotive die-castings.

Description

Technical Field

[0001] The present application relates to the field of material property prediction, and in particular, to a method, device, electronic equipment and medium for predicting mechanical properties of die-casting materials. Background Art

[0002] High-pressure die casting is widely used in the automotive, aerospace and other industrial fields due to its advantages such as high integration and high manufacturing efficiency. The yield and hardening behavior of die-cast materials in tension and compression makes the analysis of their mechanical properties challenging. At present, the conventional finite element analysis method uses the von Mises yield criterion, which believes that the yield and hardening behavior of tension and compression are consistent, and cannot accurately reflect the tension and compression anisotropy of the material, which will lead to deviations between the predicted results and the actual material properties.

[0003] At the same time, since defects such as pores, inclusions, and shrinkage cavities are easily formed during the casting manufacturing process, these defects will significantly affect the fracture behavior under different stress states. The current fracture failure model in the automotive industry is mostly based on the shell unit structure model, and only focuses on the fracture risk of the structure under the positive triaxiality condition. Die-casting materials are usually modeled using three-dimensional solid units. The traditional method assumes that the structural force condition is a plane stress or plane strain state, and it is difficult to accurately predict the fracture failure of the material under a three-dimensional stress state.

[0004] In view of this, this application is hereby filed. Summary of the invention

[0005] The purpose of the present application is to provide a method, device, electronic equipment and medium for predicting the mechanical properties of die-casting materials, so as to solve the problem that the prior art cannot accurately predict the mechanical properties of automotive die-castings.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for predicting mechanical properties of die-casting materials, comprising:

[0008] Inputting the true strain of the die-casting sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the MAT124 prediction model;

[0009] Inputting the equivalent failure strain of the die-cast sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the GISSMO prediction model;

[0010] The strain of the die-casting material to be tested is input into the MAT124 prediction model, and the equivalent failure strain is input into the GISSMO prediction model to obtain the prediction result of the mechanical properties of the die-casting material to be tested.

[0011] In some technical solutions, before inputting the real strain of the die-casting sample into the MAT124 sample model to be trained, it also includes:

[0012] Obtaining an engineering stress-strain curve during a mechanical property test of a die-casting material sample; the mechanical property test includes a uniaxial tensile test and a uniaxial compression test;

[0013] According to the engineering stress-strain curve, a true stress-strain curve is determined; the true stress-strain curve includes a uniaxial tensile true stress-strain curve and a uniaxial compressive true stress-strain curve.

[0014] In some technical solutions, the model parameters of the MAT124 sample model to be trained include: Young's modulus, Poisson's ratio, compression modulus, critical pressure in compression state, critical pressure in tension state, hardening curve in tension state and hardening curve in compression state.

[0015] In some technical solutions, the tensile state hardening curve and the compressive state hardening curve are constructed in the following manner:

[0016] According to the true stress-strain curve of uniaxial tension and the hardening model, the hardening curve of the tensile state is determined;

[0017] According to the true stress-strain curve of uniaxial compression and the hardening model, the hardening curve in compression state is determined.

[0018] In some technical solutions, during the operation of the MAT124 sample model to be trained, the hardening curve used is determined according to the average stress, the critical pressure in the tensile state and the critical pressure in the compressive state;

[0019] The hardening curve used is modified according to the stress loading rate.

[0020] In some technical solutions, the hardening curve used is determined based on the average stress, the critical pressure in the tensile state, and the critical pressure in the compressive state, including:

[0021] When the average stress is equal to the critical pressure in the compression state, the hardening curve used is determined to be the compression state hardening curve;

[0022] When the average stress is equal to the negative number of the critical pressure in the tensile state, the hardening curve used is determined to be the hardening curve in the tensile state;

[0023] When the average stress is between the inverse of the critical pressure in the compression state and the critical pressure in the tension state, the hardening curve to be used is determined to be a hardening curve under other stress states.

[0024] In some technical solutions, the equivalent failure strain of the die-casting sample is obtained in the following manner:

[0025] The equivalent failure strain of the die-casting specimen is determined based on the fracture model, stress triaxiality, Lode angle and pore volume fraction of the die-casting specimen.

[0026] The fracture model is used to characterize the relationship between equivalent failure strain, stress triaxiality, Lode angle and pore volume fraction of die-casting specimens.

[0027] In some technical solutions, adjusting the model parameters of the GISSMO sample model to be trained includes:

[0028] The particle swarm optimization algorithm or the LS-OPT genetic algorithm is used to optimize the model parameters of the GISSMO sample model to be trained.

[0029] In a second aspect, the present application provides an electronic device, including:

[0030] at least one processor, and a memory communicatively coupled to at least one of the processors;

[0031] The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can perform the above method.

[0032] In a third aspect, the present application provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method.

[0033] Compared with the prior art, the beneficial effects of this application are:

[0034] The method for predicting the mechanical properties of die-cast materials provided in the present application includes inputting the actual strain of the die-cast sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the MAT124 prediction model; inputting the equivalent failure strain of the die-cast sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the GISSMO prediction model; inputting the strain of the die-cast material to be tested into the MAT124 prediction model, inputting the equivalent failure strain into the GISSMO prediction model, and obtaining the mechanical property prediction results of the die-cast material to be tested. This method couples the MAT124 model with the GISSMO model. The MAT124 model can accurately reflect the tensile and compressive anisotropic hardening behavior of die-casting materials, and provide more accurate plastic deformation prediction results than the traditional tensile and compressive isotropic yield model. By combining the tensile and compressive anisotropic hardening behavior and the three-dimensional fracture failure criterion, it can more accurately characterize the mechanical response of the material under collision conditions, more accurately predict plastic deformation, and improve the assessment accuracy of the safety performance of die-casting parts. This method is suitable for simulating the mechanical properties of die-casting materials mainly used in the automotive industry, including die-casting materials such as magnesium alloys, aluminum alloys, and cast steel, and die-casting materials with various complex geometric structures, such as automobile chassis, engine components, etc., especially in the field of lightweight design with high requirements for structural strength.

[0035] In the process of running the MAT124 sample model to be trained, the application determines the hardening curve to be used according to the average stress, the critical pressure in the tensile state and the critical pressure in the compressive state; and corrects the hardening curve to be used according to the stress loading speed, thereby further improving the prediction accuracy of the mechanical properties of the die-casting material.

[0036] In this application, the equivalent failure strain of the die-cast sample is obtained by using a specific fracture model and the pore volume fraction of the die-cast sample. Compared with the method that does not consider the internal pores of the material, the prediction result is more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 It is a schematic diagram of the process of the method for predicting mechanical properties of die-casting materials provided in this application;

[0039] Figure 2 It is a structural schematic diagram of a die-casting material mechanical property prediction device provided in the present application;

[0040] Figure 3 It is a structural schematic diagram of the electronic device provided by this application. DETAILED DESCRIPTION

[0041] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0042] As mentioned in the background technology, the existing technology has the problem of difficulty in predicting the mechanical properties of die castings under three-dimensional stress state. In this regard, the present application optimizes the MAT124 sample model and the GISSMO sample model through the real mechanical properties of the die casting sample, and the two models are used together to predict the mechanical properties of the die casting material to be tested, so as to improve the prediction accuracy. The present application is further described in detail below in conjunction with the embodiments.

[0043] Example 1

[0044] Figure 1 It is a flow chart of a method for predicting the mechanical properties of die-casting materials provided in this embodiment. The method can be executed by a device for predicting the mechanical properties of die-casting materials. The device can be composed of software and / or hardware and is generally integrated in an electronic device. The electronic device can be an electronic computer or other mobile terminal (such as a tablet computer, etc.). For ease of understanding, each step in the prediction method of this embodiment is executed by an electronic computer.

[0045] like Figure 1 As shown, this embodiment provides a method for predicting mechanical properties of die-casting materials, comprising the following steps:

[0046] S110, inputting the true strain of the die-casting sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the MAT124 prediction model.

[0047] Among them, the MAT124 sample model refers to the model of the die-casting sample constructed using the *MAT_PLASTICITY_COMPRESSION_TENSION (*MAT_124) material model in LS-DYNA. This model can characterize the difference in yield and hardening behavior of the material under uniaxial tension and uniaxial compression, that is, tension-compression anisotropy. Here, the model output value is stress, and the true value is true stress. Reducing the loss between the model output value and the true value is to reduce the difference between the two.

[0048] In an optional embodiment, before inputting the real strain of the die-casting sample into the MAT124 sample model to be trained, it also includes:

[0049] Obtaining an engineering stress-strain curve during a mechanical property test of a die-casting material sample; the mechanical property test includes a uniaxial tensile test and a uniaxial compression test;

[0050] According to the engineering stress-strain curve, a true stress-strain curve is determined; the true stress-strain curve includes a uniaxial tensile true stress-strain curve and a uniaxial compressive true stress-strain curve.

[0051] Among them, the mechanical properties test includes static and dynamic mechanical properties test. Each mechanical properties test is repeated no less than three times. The experimental process is recorded by digital image correlation technology, and the force-displacement curve and the equivalent failure strain of the material under different stress states are read during the experiment.

[0052] The engineering stress-strain curve is calculated as follows: , engineering stress σ eng Defined as the load F divided by the initial cross-sectional area A0 of the material; , engineering strain ɛ eng It is defined as the elongation of the specimen ΔL divided by the initial length of the specimen L0.

[0053] Draw a straight line parallel to the elastic region (linear region) on the engineering stress-engineering strain curve, and the straight line passes through the coordinate axis (0,0.002). The point where this straight line intersects with the engineering stress-strain curve is defined as the yield point; the engineering stress-strain curve after the yield point is taken as the plastic stage curve; the above curve is calculated to obtain the true stress-strain curve.

[0054] The calculation formula for tensile true stress and strain is:

[0055] ;

[0056] ;

[0057] The formula for calculating the true compressive stress-strain is:

[0058] ;

[0059] ;

[0060] where σ t is the true stress, ɛ t is the true strain, σ e is the engineering stress, ɛ e For engineering strain.

[0061] Optionally, before obtaining the engineering stress-strain curve during the mechanical property test of the die-casting material sample, the method further includes:

[0062] For the die-casting materials to be tested, X-ray imaging or ultrasonic testing technology is used, combined with numerical image processing algorithms, to extract the size, shape and distribution of defects in die-casting parts, and obtain the pore volume fraction of the material;

[0063] According to the defects extracted above, reasonable sampling is carried out to ensure the structural uniformity and reliability of the tested samples;

[0064] Machining ensures that the sample is consistent with the design size;

[0065] Dumbbell-shaped specimens are processed, including rod-shaped tension, rod-shaped notch tension, rod-shaped upsetting compression, rod-shaped notch compression, etc., to characterize specimens in different stress states.

[0066] In an optional embodiment, the model parameters of the MAT124 sample model to be trained include: Young's modulus, Poisson's ratio, compression modulus, critical pressure in compression state, critical pressure in tension state, hardening curve in tension state and hardening curve in compression state.

[0067] In an optional implementation, the tensile state hardening curve and the compressive state hardening curve are constructed in the following manner:

[0068] According to the true stress-strain curve of uniaxial tension and the hardening model, the hardening curve of the tensile state is determined;

[0069] According to the true stress-strain curve of uniaxial compression and the hardening model, the hardening curve in compression state is determined.

[0070] Optionally, the hardening model is: , where: K, ε0, n, a, b, c, p are the coefficients to be fitted; α is the weight coefficient.

[0071] In this embodiment, the hardening curve in the tensile state and the hardening curve in the compressive state are both obtained by extrapolating the corresponding true stress-strain curves to describe the hardening behavior of the material in the tensile and compressive states.

[0072] In an optional embodiment, during the operation of the MAT124 sample model to be trained, the hardening curve to be used is determined according to the average stress, the critical pressure in the tensile state and the critical pressure in the compressive state;

[0073] The hardening curve used is modified according to the stress loading rate.

[0074] In an optional embodiment, the hardening curve used is determined according to the average stress, the critical pressure in the tensile state, and the critical pressure in the compressive state, including:

[0075] When the average stress is equal to the critical pressure in the compression state, the hardening curve used is determined to be the compression state hardening curve;

[0076] When the average stress is equal to the negative number of the critical pressure in the tensile state, the hardening curve used is determined to be the hardening curve in the tensile state;

[0077] When the average stress is between the inverse of the critical pressure in the compression state and the critical pressure in the tension state, the hardening curve to be used is determined to be a hardening curve under other stress states.

[0078] The average stress being between the inverse of the critical pressure in the compression state and the critical pressure in the tension state means that the average stress is greater than the inverse of the critical pressure in the tension state and less than the critical pressure in the compression state. In this embodiment, the hardening curves under other stress states between uniaxial tension and uniaxial compression are calculated by smooth transition through the critical pressure in the tension state and the critical pressure in the compression state, wherein the critical pressure in the tension state and the critical pressure in the compression state are both input as positive values.

[0079] The hardening curves for other stress states are as follows: ,in, ; where scale is the scale factor, f t (p) is a function of the yield stress and equivalent plastic strain under tension, f c (p) is a function of the yield stress and equivalent plastic strain under compression, p is the average stress, and σ1, σ2, and σ3 are the principal stresses.

[0080] S120, inputting the equivalent failure strain of the die-cast sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the GISSMO prediction model.

[0081] Among them, the GISSMO sample model refers to the model of the die-casting sample constructed using the *MAT_ADD_DAMAGE_GISSMO (GISSMO) failure accumulation model, which can predict the failure of die-casting materials. The output value of the model here is the material fracture situation, and the true value is the true fracture situation of the material. Reducing the loss between the model output value and the true value is to reduce the difference between the two.

[0082] In an optional embodiment, the equivalent failure strain of the die-casting sample is obtained in the following manner:

[0083] The equivalent failure strain of the die-casting specimen is determined based on the fracture model, stress triaxiality, Lode angle and pore volume fraction of the die-casting specimen.

[0084] The fracture model is used to characterize the relationship between equivalent failure strain, stress triaxiality, Lode angle and pore volume fraction of die-casting specimens.

[0085] The classic Hosford-Coulomb model is as follows:

[0086] ;

[0087] η is stress triaxiality, h is work hardening exponent, a, b, c are material parameters, and θ is Lode angle parameter.

[0088] .

[0089] Considering that the casting is a typical material with initial defects, this embodiment introduces the pore volume fraction f on the basis of the classic Hosford-Coulomb, and the improved Hosford-Coulomb (MHC) fracture model is as follows:

[0090]

[0091] Among them, k is the pore defect influence coefficient, and f is the pore volume fraction of the material.

[0092] Stress triaxiality is hydrostatic stress and Mises equivalent stress Ratio of:

[0093] ; Where σ1, σ2, σ3 are the principal stresses;

[0094] Determination of the critical instability strain ε under different stress states using a hybrid numerical-experimental method crit and the equivalent failure strain ε f ;

[0095] Use LS-DYNA software to build a finite element model and adjust the critical instability strain ε of the simulation model crit and the equivalent failure strain ε f , until the force-displacement curve output by the simulation model is consistent with the experimental force-displacement curve;

[0096] The finite element model was built using LS-DYNA software to estimate the stress triaxiality and Lode angle of the fracture unit during the loading process of each specimen, so as to obtain the equivalent failure strain corresponding to different stress triaxiality and Lode angle.

[0097] In an optional implementation, adjusting the model parameters of the GISSMO sample model to be trained includes:

[0098] The particle swarm optimization algorithm or the LS-OPT genetic algorithm is used to optimize the model parameters of the GISSMO sample model to be trained.

[0099] The nonlinear damage accumulation index n and stress decay index m of the GISSMO failure model should be applicable to all stress states. This implementation method uses the particle swarm optimization algorithm to calibrate n and m, and takes the experimental curve of the notched tensile specimen as the target curve. The specific operation steps are as follows:

[0100] Determine the objective function to be optimized: the R2 value of the curve fit;

[0101] Define the variables to be optimized: material parameters n and m;

[0102] Choose the number of particles, their dimensions (same as the number of variables), initial values ​​for positions and velocities;

[0103] Set the number of particles, number of variables and maximum number of iterations of the particle swarm optimization algorithm;

[0104] Output the results n and m.

[0105] The optimized n and m values ​​were applied to the simulation model and compared with the experimental results;

[0106] If the R2 value is ≥ 0.85, stop the optimization, otherwise continue.

[0107] GISSMO is used to characterize the material fracture behavior. The model is based on the following incremental formula for damage accumulation: ; ΔD is the damage accumulation increment; D is the damage accumulation value; is the equivalent failure strain related to stress triaxiality and lode angle, that is, the equivalent failure strain output by the fracture model; Δε p is the equivalent plastic strain increment; n is the nonlinear damage accumulation index;

[0108] The criterion for material instability and the beginning of stress weakening is as follows: ; ΔF is the stability variable increment; F is the stability variable; ε crit (η) is the critical instability strain, which is related to the stress triaxiality η.

[0109] The stress attenuation formula after the stress-damage coupling relationship in the GISSMO model begins (F=1) is: ; where σ and σ* are the uncoupled stress value and the coupled stress value respectively; D crit is the damage accumulation value when F=1; m is the stress decay exponent. When the damage accumulation value D gradually increases to 1, the stress decays to 0 and the material fails completely.

[0110] S130, inputting the strain of the die-casting material to be tested into the MAT124 prediction model, inputting the equivalent failure strain into the GISSMO prediction model, and obtaining the prediction result of the mechanical properties of the die-casting material to be tested.

[0111] The above-mentioned method for predicting the mechanical properties of die-casting materials includes inputting the actual strain of the die-casting sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the MAT124 prediction model; inputting the equivalent failure strain of the die-casting sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the GISSMO prediction model; inputting the strain of the die-casting material to be tested into the MAT124 prediction model, inputting the equivalent failure strain into the GISSMO prediction model, and obtaining the mechanical property prediction result of the die-casting material to be tested. This method couples the MAT124 model with the GISSMO model. The MAT124 model can accurately reflect the tensile and compressive anisotropic hardening behavior of die-casting materials, and provide more accurate plastic deformation prediction results than the traditional tensile and compressive isotropic yield model. By combining the tensile and compressive anisotropic hardening behavior and the three-dimensional fracture failure criterion, it can more accurately characterize the mechanical response of the material under collision conditions, more accurately predict plastic deformation, and improve the assessment accuracy of the safety performance of die-casting parts. This method is suitable for simulating the mechanical properties of die-casting materials mainly used in the automotive industry, including die-casting materials such as magnesium alloys, aluminum alloys, and cast steel, and die-casting materials with various complex geometric structures, such as automobile chassis, engine components, etc., especially in the field of lightweight design with high requirements for structural strength.

[0112] Furthermore, during the operation of the MAT124 sample model to be trained, the hardening curve used is determined according to the average stress, the critical pressure in the tensile state and the critical pressure in the compressive state; and the hardening curve used is corrected according to the stress loading speed, thereby further improving the prediction accuracy of the mechanical properties of the die-casting material.

[0113] Furthermore, the equivalent failure strain of the die-casting specimen is obtained using a specific fracture model and the pore volume fraction of the die-casting specimen. Compared with the method that does not consider the internal pores of the material, the prediction result is more reliable.

[0114] Example 2

[0115] like Figure 2 As shown, this embodiment provides a die-casting material mechanical property prediction device, comprising:

[0116] A MAT124 prediction model building module 201 is used to input the real strain of the die-casting sample into the MAT124 sample model to be trained, adjust the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the real value, and obtain the MAT124 prediction model;

[0117] A GISSMO prediction model building module 202 is used to input the equivalent failure strain of the die-casting sample into the GISSMO sample model to be trained, adjust the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtain the GISSMO prediction model;

[0118] The prediction module 203 is used to input the strain of the die-casting material to be tested into the MAT124 prediction model, and input the equivalent failure strain into the GISSMO prediction model to obtain the prediction result of the mechanical properties of the die-casting material to be tested.

[0119] The device is used to execute the above method, and thus has at least functional modules and beneficial effects corresponding to the above method.

[0120] Example 3

[0121] like Figure 3 As shown, this embodiment provides an electronic device, including:

[0122] at least one processor; and

[0123] a memory communicatively connected to at least one of the processors; wherein,

[0124] The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform the above method. At least one processor in the electronic device can perform the above method, and thus has at least the same advantages as the above method.

[0125] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on a memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to an interface). In other embodiments, if necessary, multiple processors can be used together with multiple memories, and / or multiple buses can be used together with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 3 A processor 301 is taken as an example.

[0126] The memory 302, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the die-casting material mechanical property prediction method in the embodiment of the present application (for example, the MAT124 prediction model construction module, the GISSMO prediction model construction module, and the prediction module in the die-casting material mechanical property prediction device). The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 302, that is, realizes the above-mentioned die-casting material mechanical property prediction method.

[0127] The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0128] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0129] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0130] Example 4

[0131] This embodiment provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method. The computer instructions on the computer-readable storage medium are used to enable a computer to execute the above method, and thus have at least the same advantages as the above method.

[0132] The medium in this application may adopt any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of the medium (a non-exhaustive list) include: an electrical connection with one or more wires, 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 thereof. In this document, the medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device, or device.

[0133] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

[0135] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0137] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for predicting mechanical properties of die-casting materials, characterized in that: include: Inputting the true strain of the die-casting sample into the MAT124 sample model to be trained, adjusting the model parameters of the MAT124 sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the MAT124 prediction model; Inputting the equivalent failure strain of the die-cast sample into the GISSMO sample model to be trained, adjusting the model parameters of the GISSMO sample model to be trained to reduce the loss between the model output value and the true value, and obtaining the GISSMO prediction model; Input the strain of the die-cast material to be tested into the MAT124 prediction model, input the equivalent failure strain into the GISSMO prediction model, and obtain the prediction result of the mechanical properties of the die-cast material to be tested; During the operation of the MAT124 sample model to be trained, determining the hardening curve to be used according to the average stress, the critical pressure in the tensile state and the critical pressure in the compressive state; The hardening curve used is modified according to the stress loading rate.

2. The method for predicting mechanical properties of die-casting materials according to claim 1, characterized in that: Before inputting the real strain of the die-casting sample into the MAT124 sample model to be trained, it also includes: Obtaining an engineering stress-strain curve during a mechanical property test of a die-casting material sample; the mechanical property test includes a uniaxial tensile test and a uniaxial compression test; According to the engineering stress-strain curve, a true stress-strain curve is determined; the true stress-strain curve includes a uniaxial tensile true stress-strain curve and a uniaxial compressive true stress-strain curve.

3. The method for predicting mechanical properties of die-casting materials according to claim 1, characterized in that: The model parameters of the MAT124 sample model to be trained include: Young's modulus, Poisson's ratio, compression modulus, critical pressure in compression state, critical pressure in tension state, hardening curve in tension state and hardening curve in compression state.

4. The method for predicting mechanical properties of die-casting materials according to claim 3, characterized in that: The tensile state hardening curve and the compressive state hardening curve are constructed in the following manner: According to the true stress-strain curve of uniaxial tension and the hardening model, the hardening curve of the tensile state is determined; According to the true stress-strain curve of uniaxial compression and the hardening model, the hardening curve in compression state is determined.

5. The method for predicting mechanical properties of die-casting materials according to claim 1, characterized in that: Based on the mean stress, the critical pressure in tension and the critical pressure in compression, the hardening curve to be used is determined, including: When the average stress is equal to the critical pressure in the compression state, the hardening curve used is determined to be the compression state hardening curve; When the average stress is equal to the negative number of the critical pressure in the tensile state, the hardening curve used is determined to be the hardening curve in the tensile state; When the average stress is between the inverse of the critical pressure in the compression state and the critical pressure in the tension state, the hardening curve to be used is determined to be a hardening curve under other stress states.

6. The method for predicting mechanical properties of die-casting materials according to claim 1, characterized in that: The equivalent failure strain of the die-casting specimen is obtained in the following manner: The equivalent failure strain of the die-casting specimen is determined based on the fracture model, stress triaxiality, Lode angle and pore volume fraction of the die-casting specimen. The fracture model is used to characterize the relationship between equivalent failure strain, stress triaxiality, Lode angle and pore volume fraction of die-casting specimens.

7. The method for predicting mechanical properties of die-casting materials according to claim 1, characterized in that: Adjusting the model parameters of the GISSMO sample model to be trained includes: The particle swarm optimization algorithm or the LS-OPT genetic algorithm is used to optimize the model parameters of the GISSMO sample model to be trained.

8. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that the at least one processor can execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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