Forming limit prediction model generation method, electronic equipment and storage medium

Abstract

The invention relates to a forming limit prediction model generation method, electronic equipment and a storage medium, and belongs to the technical field of plate forming performance evaluation.The method comprises the steps that based on preset size requirements, corresponding polycrystal models are established for a plurality of pretreated material samples under different strain paths, and the polycrystal models are obtained; generating an inp file containing the polycrystal model; performing crystal plasticity finite element simulation based on the inp file and a preset boundary condition to obtain a simulation result; based on a preset yield criterion and the constitutive model, an MK forming limit prediction model is established, and based on the MK forming limit prediction model, a prediction result about the primary strain ratio and the secondary strain ratio of the material sample is obtained; obtaining an experiment result of a forming limit bulging experiment on the material sample; and constructing a forming limit prediction model based on the simulation result, the prediction result and the experiment result. According to the invention, the problems of low prediction efficiency and low accuracy in the prior art are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of sheet metal forming performance evaluation, and in particular to a forming limit prediction model generation method, electronic equipment and storage medium. Background Art

[0002] With the rapid development of the aerospace and automotive industries, lightweighting has become one of the development trends in the transportation field. The proportion of lightweight materials such as high-strength steel, aluminum alloys and titanium alloys is increasing. The stamping process of metal sheets is an important component processing process. During the stamping process, the sheet is prone to defects such as cracking and wrinkling, resulting in an increase in the scrap rate. Avoiding defects through trial and error will reduce production efficiency and increase production costs.

[0003] The forming limit diagram (FLD) is an effective method to evaluate the formability of sheet metal. The forming limit curve (FLC) obtained by the forming limit experiment can be used to predict the cracking of sheet metal during forming. There are two main types of forming limit experiments: bulging experiment and double-pulling experiment. According to the standard, specimens with different strain paths are designed. Bulging experiments or double-pulling experiments are carried out under different strain paths. The primary and secondary strains before the failure of the sheet metal are obtained by DIC and other technologies, and the FLC is drawn. Although accurate FLC can be obtained by using the forming limit experiment, the experimental steps are complicated, the experiment is highly accidental, the accuracy requirements of non-contact strain analysis technology are high, and one forming limit experiment can only obtain FLC under one process parameter. Therefore, it is of great significance to use simulation to predict the forming limit.

[0004] The current forming limit prediction methods are mainly divided into bulging experimental simulation of different strain paths and MK model. The prediction of forming limit has the problems of low prediction efficiency and low accuracy. Summary of the invention

[0005] In view of this, it is necessary to provide a forming limit prediction model generation method to solve the problems of low prediction efficiency and low accuracy in the prior art.

[0006] In order to solve the above problems, the present invention provides a forming limit prediction model generation method, comprising: Based on the preset size requirements, corresponding polycrystalline models are established for the preprocessed material samples under several different strain paths, and an inp file containing the polycrystalline model is generated; Based on the inp file and the preset boundary conditions, the crystal plasticity finite element simulation is performed to obtain the simulation results; An MK forming limit prediction model is established based on the preset yield criterion and constitutive model, and prediction results of the major and minor strain ratios of the material sample are obtained based on the MK forming limit prediction model; Obtaining experimental results of forming limit bulging experiments on material samples; Based on the simulation results, prediction results and experimental results, a forming limit prediction model is constructed.

[0007] In a possible implementation, the preprocessing of the material sample includes: The material samples were subjected to mechanical polishing and electrolytic polishing respectively; The grain information of the material sample is obtained based on electron backscatter diffraction analysis technology.

[0008] In a possible implementation, the grain information includes: grain Euler angles; Based on the preset size requirements, a polycrystalline model under different strain paths is established for the pre-processed material sample, and an inp file containing model information is generated, including: According to the requirements of the forming limit test standard, several bulging test specimens with different strain states are designed, and a polycrystalline model is established in the Neper software according to the size of the specimen, and the polycrystalline model is exported in the format of an inp file; The grain Euler angles are converted into Miller indices and imported into the material properties of the inp file.

[0009] In a possible implementation, the crystal plasticity finite element simulation based on the inp file, the preset boundary conditions and the UMAT subroutine includes: Import the inp file into abaqus; According to the abaqus secondary development interface rules, write the UMAT subroutine based on FORTRAN language; Import UMAT subroutine to perform finite element simulation; Among them, the crystal plasticity model used for finite element simulation is:

[0010] In the formula, is the Jaumann derivative of the Cauchy stress tensor for the initial configuration, is the instantaneous 4th-order elastic modulus tensor, is the deformation tensor, N is the number of slip systems, is the elastic part of the deformation tensor, is the rotation rate tensor of the slip system, is the shear stress of the slip system, is the slip strain rate of the slip system, The reference shear strain rate is the reference slip rate, Slip system α The shear stress on Slip system α The critical shear stress (strength) on the sliding surface is the sliding resistance. n is the strain rate sensitivity index; is the hardening modulus, when When is the self-hardening modulus, when When , it is the latent hardening modulus; is the strain rate, is the reference strain rate, m is the strain rate sensitivity coefficient, R is the gas constant, T is the forming temperature, Q is the thermal activation energy, q is the ratio of self-hardening to latent hardening effect; is the Kronecker symbol, hour, ,at this time ; hour, ,at this time, , is the initial hardening modulus, is the total shear strain, i.e. the total slip of all slip systems, is the saturated slip resistance.

[0011] In a possible implementation, the simulation result includes: The forming limit curve FLC1 is drawn according to the major and minor strain ratios of material samples under several different strain paths before reaching the preset fracture condition.

[0012] In a possible implementation, obtaining the prediction result includes: The strain ratios of the material samples under the several different strain paths are input into the MK forming limit prediction model to obtain the primary and secondary strains that reach the preset fracture conditions, and the forming limit curve FLC2 is plotted.

[0013] In a possible implementation, obtaining the experimental result of a forming limit bulging experiment on a material sample includes: Carry out forming limit bulging test according to national standards; Based on the DIC technology, the major and minor strain ratios of the material sample before reaching the preset fracture condition are obtained, and the forming limit curve FLC is drawn.

[0014] In a possible implementation, constructing a forming limit prediction model based on simulation results, prediction results, and experimental results includes: Based on the neural network model, the data of FLC1 and FLC2 are fitted to the real FLC data curve obtained from the experiment, and a forming limit prediction model integrating mesoscopic simulation and MK theoretical model is obtained.

[0015] The present invention also provides an electronic device, comprising: Memory, used to store programs; A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the forming limit prediction model generation method described in any one of the above method items.

[0016] The present invention also provides a storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the forming limit prediction model generation method described in any one of the above-mentioned method items.

[0017] The beneficial effects of the present invention are as follows: the present invention provides a method for generating a forming limit prediction model. First, a crystal plasticity finite element method with higher accuracy is used for simulation to obtain simulation data; secondly, the present invention also uses a more efficient MK model to obtain prediction data; finally, the present invention fits the simulation data and the prediction data to the real data obtained from the experiment to obtain a forming limit prediction model that integrates the mesoscopic simulation and the MK theoretical model. Therefore, the forming limit prediction model provided by the present invention integrates the advantages of the crystal plasticity finite element method and the MK theoretical model, thereby effectively solving the problem of low prediction efficiency and low accuracy in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A method flow chart of an embodiment of a forming limit prediction model generation method provided by the present invention; Figure 2 for Figure 1 A method flow chart of step S101 in an embodiment; Figure 3 A schematic diagram of a polycrystalline model provided by the present invention; Figure 4 for Figure 1 A method flow chart of an embodiment of step S102; Figure 5 A schematic diagram of the structure of the forming limit specimen provided by the present invention; Figure 6 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] In order to solve the above problems, Figure 1 As shown, the present invention provides a forming limit prediction model generation method, comprising: S101. Based on preset size requirements, corresponding polycrystalline models are established for preprocessed material samples under a plurality of different strain paths, and an inp file containing model information is generated.

[0024] In a possible implementation, the pretreatment of the material sample includes: The material samples were subjected to mechanical polishing and electrolytic polishing respectively; Obtaining grain information of material samples based on electron backscatter diffraction analysis technology; Preferably, during the polishing treatment, the sample is polished with 800, 1200, 1500, 2000, 2500, and 3000 grit sandpaper respectively until there are no obvious scratches on the surface, and then the sample is polished with a polishing cloth until it is bright, and finally an electrolytic polishing treatment is performed.

[0025] Furthermore, the grain information includes: grain Euler angles.

[0026] like Figure 2 In a possible implementation, step S101 includes: S201. According to the requirements of the forming limit test standard, design several bulging test specimens with different strain states, and establish the following in the Neper software according to the size of the specimen: Figure 3The polycrystal model shown is exported in the format of an inp file.

[0027] S202. Convert the grain Euler angles into Miller indices and import them into the material properties of the inp file.

[0028] It should be noted that when using the crystal plasticity finite element method for bulging simulation, since the crystal plasticity finite element method has high accuracy but low efficiency, fewer strain paths can be selected for simulation.

[0029] Preferably, in this embodiment, 4-5 bulging test specimens with different strain states are designed according to the requirements of the forming limit test standard.

[0030] S102, performing crystal plasticity finite element simulation based on the inp file and preset boundary conditions to obtain simulation results.

[0031] It should be noted that when setting the boundary conditions, the die, punch and blank holder can be designed according to the bulging experiment and the corresponding boundary conditions can be assigned.

[0032] In a possible implementation, the simulation results include: The forming limit curve FLC1 is drawn according to the major and minor strain ratios of material samples under several different strain paths before reaching the preset fracture condition.

[0033] like Figure 4 In a possible implementation, step S102 includes: S401. Import the inp file into abaqus; S402. Write the UMAT subroutine based on FORTRAN language according to the abaqus secondary development interface rules; S403, importing the UMAT subroutine to perform finite element simulation; Among them, the crystal plasticity model used for finite element simulation is: (1) In formula (1), is the Jaumann derivative of the Cauchy stress tensor for the initial configuration, is the instantaneous 4th-order elastic modulus tensor, is the deformation tensor, N is the number of slip systems, is the elastic part of the deformation tensor, is the rotation rate tensor of the slip system, is the shear stress of the slip system, is the slip strain rate of the slip system, The reference shear strain rate is the reference slip rate, Slip system α The shear stress on Slip system α The critical shear stress (strength) on the sliding surface is the sliding resistance. n is the strain rate sensitivity index; is the hardening modulus, when When is the self-hardening modulus, when When , it is the latent hardening modulus; is the strain rate, is the reference strain rate, m is the strain rate sensitivity coefficient, R is the gas constant, T is the forming temperature, Q is the thermal activation energy, q is the ratio of self-hardening to latent hardening effect; is the Kronecker symbol, hour, ,at this time ; hour, ,at this time, , is the initial hardening modulus, is the total shear strain, i.e. the total slip of all slip systems, is the saturated slip resistance.

[0034] S103, establishing an MK forming limit prediction model based on a preset yield criterion and a constitutive model, and obtaining prediction results.

[0035] Preferably, the selected yield criterion and parameters in the constitutive model can be determined according to relevant standard experiments.

[0036] Furthermore, the strain ratios of the material samples under several different strain paths are input into the MK forming limit prediction model to obtain the primary and secondary strains that reach the preset fracture conditions, and the forming limit curve FLC2 is plotted.

[0037] It should be noted that since the MK model has high prediction efficiency but low accuracy, as many strain ratios of different paths as possible can be selected as input to the model.

[0038] S104. Obtaining experimental results of a forming limit bulging experiment on a material sample.

[0039] It should be noted that in step S104, 5-8 size specimens need to be designed according to the forming limit test standard to carry out bulging test. The schematic diagram of the size specimens is as follows: Figure 5 shown.

[0040] Specifically, based on the DIC technology, the major and minor strain ratios of the material sample before reaching the preset fracture condition are obtained, and the forming limit curve FLC is plotted.

[0041] It should be noted that, in the present invention, FLC1 is the result obtained by performing crystal plasticity finite element simulation, FLC2 is the result obtained by MK model prediction, and FLC is the result obtained by performing bulging experiments according to national standards.

[0042] S105. Construct a forming limit prediction model based on the simulation results, prediction results and experimental results.

[0043] In a possible implementation, step S105 includes: Based on the neural network model, the data of FLC1 and FLC2 are fitted to the real FLC data curve obtained from the experiment, and a forming limit prediction model integrating mesoscopic simulation and MK theoretical model is obtained.

[0044] It should be noted that the present invention can be used to predict the forming limit of metal sheets such as high-strength steel, aluminum alloy and titanium alloy.

[0045] Compared with the prior art, the present invention provides a method for generating a forming limit prediction model. First, a more accurate crystal plasticity finite element method is used for simulation to obtain simulation data. Second, the present invention also uses a more efficient MK model to obtain prediction data. Finally, the present invention fits the simulation data and the prediction data to the real data obtained from the experiment to obtain a forming limit prediction model that integrates the mesoscopic simulation and the MK theoretical model. Therefore, the forming limit prediction model provided by the present invention integrates the advantages of the crystal plasticity finite element method and the MK theoretical model, thereby effectively solving the problem of low prediction efficiency and low accuracy of the prior art.

[0046] like Figure 6 As shown, the present invention further provides an electronic device 60, comprising: A memory 610, used for storing programs; The processor 620 is coupled to the memory 610 and is used to execute the program stored in the memory 610 to implement the steps in the forming limit prediction model generation method described in any one of the above embodiments.

[0047] Figure 6 Only some of the components of the electronic device 60 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0048] In some embodiments, the processor 620 may be a central processing unit (CPU), a microprocessor or other data processing chip, and is used to run program codes or process data stored in the memory 610, such as the forming limit prediction model generation method of the present invention.

[0049] In some embodiments, the processor 620 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 620 may be local or remote. In some embodiments, the processor 620 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0050] In some embodiments, the memory 610 may be an internal storage unit of the electronic device 60, such as a hard disk or memory of the electronic device 60. In other embodiments, the memory 610 may also be an external storage device of the electronic device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 60.

[0051] Furthermore, the memory 610 may include both an internal storage unit of the electronic device 60 and an external storage device. The memory 610 is used to store application software installed in the electronic device 60 and various data.

[0052] In one embodiment, when the processor 620 executes the forming limit prediction model generation program in the memory 610, the following steps may be implemented: Based on the preset size requirements, corresponding polycrystalline models are established for the pre-processed material samples under several different strain paths, and an inp file containing model information is generated; Based on the inp file and the preset boundary conditions, the crystal plasticity finite element simulation is performed to obtain the simulation results; Based on the preset yield criterion and constitutive model, the MK forming limit prediction model is established and the prediction results are obtained; Obtaining experimental results of forming limit bulging experiments on material samples; Based on the simulation results, prediction results and experimental results, a forming limit prediction model is constructed.

[0053] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 60 mentioned, and the electronic device 60 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 60 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0054] The present invention also provides a storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the forming limit prediction model generation method described in any one of the above embodiments.

[0055] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer storage medium, wherein the computer storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0056] The forming limit prediction model generation method, electronic device and storage medium provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A forming limit prediction model generation method, characterized in that: include: Based on the preset size requirements, corresponding polycrystalline models are established for the preprocessed material samples under several different strain paths, and an inp file containing the polycrystalline model is generated; Based on the inp file and the preset boundary conditions, the crystal plasticity finite element simulation is performed to obtain the simulation results; An MK forming limit prediction model is established based on the preset yield criterion and constitutive model, and prediction results of the major and minor strain ratios of the material sample are obtained based on the MK forming limit prediction model; Obtaining experimental results of forming limit bulging experiments on material samples; Based on the simulation results, prediction results and experimental results, a forming limit prediction model is constructed.

2. The forming limit prediction model generation method according to claim 1, characterized in that: The pretreatment of the material sample includes: The material samples were subjected to mechanical polishing and electrolytic polishing respectively; The grain information of the material sample is obtained based on electron backscatter diffraction analysis technology.

3. The forming limit prediction model generation method according to claim 2, characterized in that: The grain information includes: grain Euler angles; Based on the preset size requirements, a polycrystalline model under different strain paths is established for the pre-processed material sample, and an inp file containing model information is generated, including: According to the requirements of the forming limit test standard, several bulging test specimens with different strain states are designed, and a polycrystalline model is established in the Neper software according to the size of the specimen, and the polycrystalline model is exported in the format of an inp file; The grain Euler angles are converted into Miller indices and imported into the material properties of the inp file.

4. The forming limit prediction model generation method according to claim 1, characterized in that: The crystal plasticity finite element simulation based on the inp file, the preset boundary conditions and the UMAT subroutine includes: Import the inp file into abaqus; According to the abaqus secondary development interface rules, write the UMAT subroutine based on FORTRAN language; Import UMAT subroutine to perform finite element simulation; Among them, the crystal plasticity model used for finite element simulation is: In the formula, is the Jaumann derivative of the Cauchy stress tensor for the initial configuration, is the instantaneous 4th-order elastic modulus tensor, is the deformation tensor, N is the number of slip systems, is the elastic part of the deformation tensor, is the rotation rate tensor of the slip system, is the shear stress of the slip system, is the slip strain rate of the slip system, The reference shear strain rate is the reference slip rate, Slip system α The shear stress on Slip system α The critical shear stress (strength) on the sliding surface is the sliding resistance. n is the strain rate sensitivity index; is the hardening modulus, when When is the self-hardening modulus, when When , it is the latent hardening modulus; is the strain rate, is the reference strain rate, m is the strain rate sensitivity coefficient, R is the gas constant, T is the forming temperature, Q is the thermal activation energy, q is the ratio of self-hardening to latent hardening effect; is the Kronecker symbol, hour, ,at this time ; hour, ,at this time, , is the initial hardening modulus, is the total shear strain, i.e. the total slip of all slip systems, is the saturated slip resistance.

5. The forming limit prediction model generation method according to claim 1, characterized in that: The simulation results include: The forming limit curve FLC1 is drawn according to the major and minor strain ratios of material samples under several different strain paths before reaching the preset fracture condition.

6. The forming limit prediction model generation method according to claim 5, characterized in that: The obtaining of prediction results includes: The strain ratios of the material samples under the several different strain paths are input into the MK forming limit prediction model to obtain the primary and secondary strains that reach the preset fracture conditions, and the forming limit curve FLC2 is plotted.

7. The forming limit prediction model generation method according to claim 6, characterized in that: The step of obtaining the experimental result of the forming limit bulging experiment on the material sample comprises: Carry out forming limit bulging test according to national standards; Based on the DIC technology, the major and minor strain ratios of the material sample before reaching the preset fracture condition are obtained, and the forming limit curve FLC is drawn.

8. The forming limit prediction model generation method according to claim 6, characterized in that: The forming limit prediction model is constructed based on the simulation results, the prediction results and the experimental results, including: Based on the neural network model, the data of FLC1 and FLC2 are fitted to the real FLC data curve obtained from the experiment, and a forming limit prediction model integrating mesoscopic simulation and MK theoretical model is obtained.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the forming limit prediction model generation method described in any one of claims 1 to 8 above.

10. A storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the forming limit prediction model generation method described in any one of claims 1 to 8.

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