Self-piercing riveting simulation prediction and parameter optimization method and related equipment

Abstract

The invention discloses a self-piercing riveting simulation prediction and parameter optimization method and related equipment, and relates to the technical field of mechanical connection numerical simulation, and the method comprises the steps: building a material failure model according to material failure information; setting boundary conditions of the self-piercing riveting joint forming simulation model according to test working conditions to obtain section data and a joint model of the self-piercing riveting joint; establishing a mechanical property simulation model of self-piercing riveting according to the mechanical property sample based on the joint model, and setting boundary conditions of the mechanical property simulation model; calculating according to the mechanical property simulation model to obtain a load displacement curve; optimizing parameters of the material failure model by taking the undercut amount and the bottom thickness size error of the cross section and the root mean square error of the load displacement curve as optimization targets; the optimized parameters are substituted into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, corresponding boundary conditions are set for simulation, and a simulation result is obtained. According to the method, the simulation precision and the process optimization efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical connection numerical simulation, and in particular to a self-piercing riveting simulation prediction and parameter optimization method and related equipment. BACKGROUND

[0002] Self-piercing riveting technology, as a current popular mechanical connection technology, is widely used in the fields of automobiles and aviation due to its high connection strength, environmentally friendly process, and suitability for dissimilar metal connection. For self-piercing riveting technology, there are many process parameters, including rivet parameters, rivet die parameters, and punch speed, etc. The process parameters of riveting have a significant impact on the success rate and mechanical properties of riveting. Therefore, obtaining appropriate process parameters is the key to ensuring excellent riveting performance and is also the focus of self-piercing riveting technology research. In most engineering applications, the selection of riveting process parameters requires a large number of connection tests, measurement of joint section characteristic dimensions, further measurement of joint mechanical properties, and ultimately obtaining the optimal riveting process parameters. Although this method can obtain good process parameter combinations, the process is tedious and the test cost is high.

[0003] In order to reduce the research and development cost, relevant researchers apply numerical simulation technology to the process parameter optimization of self-piercing riveting technology. However, there are still some problems in the application of numerical simulation technology, mainly including the following two aspects: 1. Limitations of traditional finite element method: In numerical simulation technology, the finite element method is most widely used in the field of self-piercing riveting. However, the finite element method uses element deletion to simulate material fracture. With element deletion, element mass and energy are also deleted, resulting in a large amount of missing mass and energy in the simulation process, poor simulation accuracy, and serious dependence on grid quality. In order to reduce the influence of element deletion, relevant researchers establish a 2D model to simulate joint forming, and additionally use a 3D model to simulate joint mechanical property testing. Although this method avoids a large amount of element deletion, the residual stress and strain of joint forming are difficult to inherit to the joint mechanical property testing model, thereby affecting the final test accuracy. 2. Adaptability of material failure model: Self-piercing riveting joint forming belongs to a typical large deformation condition and involves material fracture. In order to accurately simulate material fracture, establishing an accurate material failure model is the key to ensuring the accuracy of the entire numerical simulation. The conventional material failure model is established by conducting tensile tests on a variety of shaped tensile samples, establishing a finite element model for benchmarking, and further fitting the required failure strain curve and other information to establish a feasible failure model. This failure model is based on very limited failure samples and has a relatively single stress state. However, the stress state of the material in the actual self-piercing riveting process is very complex. The conventional establishment method has poor adaptability, thereby reducing the simulation accuracy. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a self-piercing riveting simulation prediction and parameter optimization method and related equipment to solve the problems of self-piercing riveting simulation and material adaptability, and improve the simulation accuracy and research and development efficiency.

[0005] To achieve the above-mentioned purpose, one aspect of the embodiments of the present application provides a self-piercing riveting simulation prediction and parameter optimization method, which comprises the following steps:

[0006] constructing a material failure model according to material failure information;

[0007] setting boundary conditions of a self-piercing riveting joint forming simulation model according to test working conditions to obtain cross-sectional data and a joint model of the self-piercing riveting joint;

[0008] establishing a mechanical property simulation model of self-piercing riveting based on the joint model according to a mechanical property sample, and setting boundary conditions of the mechanical property simulation model;

[0009] calculating a first load displacement curve according to the mechanical property simulation model;

[0010] optimizing parameters of the material failure model by taking the undercut amount and bottom thickness size error of the cross section in the cross-sectional data and the root mean square error of the first load displacement curve as optimization targets;

[0011] substituting the parameters obtained by optimizing the material failure model into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, setting corresponding boundary conditions for simulation, and obtaining simulation results.

[0012] In some embodiments, before the step of constructing a material failure model according to material failure information, the method further comprises the step of obtaining the material failure information, comprising the following steps:

[0013] performing tensile tests on uniaxial tensile samples and notched tensile samples to obtain tensile test data;

[0014] establishing corresponding finite element models according to the tensile test data;

[0015] obtaining a second load displacement curve of the uniaxial tensile sample and a third load displacement curve of the notched tensile sample by using the finite element models respectively;

[0016] obtaining mechanical property parameters, a true stress-strain curve, and failure strain and stress triaxiality under different stress states of the material by comparing the second load displacement curve and the third load displacement curve.

[0017] In some embodiments, the step of obtaining the true stress-strain curve comprises the following steps:

[0018] fitting the true stress-strain curve by Swift hardening criterion;

[0019] The expression of the true stress-strain curve is:

[0020] σ=A(ε p +ε0) n ;

[0021] Wherein, σ is the true stress-strain curve, A is a material constant, ε0 is the yield strain, and n is the stress hardening index.

[0022] In some embodiments, the material failure model is constructed according to the material failure information, including the following steps:

[0023] The relationship between the failure strain and the stress triaxiality under different stress states is fitted to obtain a failure strain curve;

[0024] The damage evolution parameters are obtained by adjusting and selecting the uniaxial tensile specimen and the notched tensile specimen;

[0025] The material failure model includes the failure strain curve and the damage evolution parameters.

[0026] In some embodiments, the relationship between the failure strain and the stress triaxiality under different stress states is fitted to obtain a failure strain curve, including the following steps:

[0027] The relationship between the failure strain and the stress triaxiality under different stress states is fitted by a fracture failure criterion to obtain an initial failure strain curve;

[0028] The initial failure strain curve is substituted into the self-piercing rivet joint forming simulation model for correction, and when the load and failure displacement errors are both less than a preset threshold, a target failure strain curve is obtained.

[0029] In some embodiments, the step of establishing the joint model includes the following steps:

[0030] The joint model is established by a FEM-SPG hybrid modeling method;

[0031] Wherein, the central region of the joint model is established by SPG particles, and the remaining part of the joint model except the central region is established by finite element units.

[0032] In some embodiments, the root-cause data in the cross-section data and the root-cause data in the cross-section data and the root-cause data in the cross-section data are used as optimization targets to optimize the parameters of the material failure model, including the following steps:

[0033] The undercut amount and bottom thickness dimension error of the section in the section data and the root mean square error of the first load displacement curve are taken as optimization targets, and a stress attenuation index, a damage accumulation index, and a fracture criterion fitting parameter in the material failure information are taken as parameters of the material failure model, and the parameters of the material failure model are optimized by a genetic particle swarm hybrid algorithm.

[0034] To achieve the above object, another aspect of the embodiment of the present application provides a self-piercing riveting simulation prediction and parameter optimization device, which comprises:

[0035] A failure model establishing unit is configured to construct a material failure model according to material failure information;

[0036] A simulation model setting unit is configured to set boundary conditions of a self-piercing riveting joint forming simulation model according to test conditions, to obtain section data and a joint model of the self-piercing riveting joint;

[0037] A mechanical model establishing unit is configured to establish a mechanical performance simulation model of the self-piercing riveting joint based on the joint model and according to a mechanical performance sample, and to set boundary conditions of the mechanical performance simulation model;

[0038] A curve calculating unit is configured to calculate a first load displacement curve according to the mechanical performance simulation model;

[0039] A parameter optimization unit is configured to take the undercut amount and bottom thickness dimension error of the section in the section data and the root mean square error of the first load displacement curve as optimization targets, and to optimize parameters of the material failure model;

[0040] A simulation test unit is configured to substitute the parameters of the material failure model obtained by optimization into the self-piercing riveting joint forming simulation model and the mechanical performance simulation model, and to perform simulation by setting corresponding boundary conditions, to obtain simulation results.

[0041] To achieve the above object, another aspect of the embodiment of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0042] To achieve the above object, another aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0043] The embodiment of the present application at least has the following beneficial effects:

[0044] The application provides a self-piercing riveting simulation prediction and parameter optimization method and related equipment, and the method comprises the following steps: constructing a material failure model according to material failure information; setting boundary conditions of a self-piercing riveting joint forming simulation model according to test working conditions, obtaining cross-section data and a joint model of the self-piercing riveting joint; establishing a mechanical property simulation model of the self-piercing riveting based on the joint model and a mechanical property sample, and setting boundary conditions of the mechanical property simulation model; obtaining a first load displacement curve through calculation based on the mechanical property simulation model; taking the undercut amount of the cross-section, the bottom thickness size error and the root mean square error of the first load displacement curve as optimization targets, and optimizing parameters of the material failure model; substituting the parameters of the optimized material failure model into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, setting corresponding boundary conditions for simulation, and obtaining simulation results. The application optimizes the material failure parameters based on the simulation results of the self-piercing riveting, improves the adaptability of the material failure parameters to the self-piercing riveting and similar large deformation working conditions, improves the simulation accuracy, avoids the result uncertainty caused by the conventional parameter fine-tuning, and improves the process optimization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0046] Figure 1 A flowchart of a self-piercing riveting simulation prediction and parameter optimization method provided by the embodiment of the application is shown in the figure.

[0047] Figure 2 An example flowchart of a self-piercing riveting simulation prediction and parameter optimization method provided by the embodiment of the application is shown in the figure.

[0048] Figure 3 An example flowchart of optimizing material failure parameters provided by the embodiment of the application is shown in the figure.

[0049] Figure 4 A structural schematic diagram of a self-piercing riveting simulation prediction and parameter optimization device provided by the embodiment of the application is shown in the figure.

[0050] Figure 5 A hardware structural schematic diagram of an electronic device provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0051] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and not intended to limit the present application. When the following description refers to the accompanying drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with embodiments of the present application. They are merely examples of apparatuses and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0052] It can be understood that the terms “first”, “second” and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information, without departing from the scope of the embodiments of the present application. Depending on the context, the word “if” as used herein can be interpreted as “when” or “upon” or “in response to determining” as used herein.

[0053] The terms “at least one”, “multiple”, “each”, “any” and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0055] The embodiment of the present application provides a self-piercing riveting simulation prediction and parameter optimization method and related equipment, and relates to the technical field of mechanical connection numerical simulation. The self-piercing riveting simulation prediction and parameter optimization method and related equipment provided by the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can further be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal and the like, but is not limited thereto; the server end can be configured as a stand-alone physical server, can also be configured as a server cluster or a distributed system formed by multiple physical servers, can further be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN and big data and artificial intelligence platform, and the server can also be a node server in a blockchain network; the software can be an application for implementing a self-piercing riveting simulation prediction and parameter optimization method, and the like, but is not limited to the above forms.

[0056] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as a program module. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0057] Reference Figure 1 The embodiment of the present application provides a self-piercing riveting simulation prediction and parameter optimization method, which can include but is not limited to S100 to S150, and specifically as follows:

[0058] S100: constructing a material failure model according to material failure information;

[0059] S110: setting boundary conditions of a self-piercing riveting joint forming simulation model according to test conditions, to obtain cross-sectional data and a joint model of the self-piercing riveting joint;

[0060] S120: establishing a mechanical property simulation model of the self-piercing riveting joint according to the joint model and the mechanical property sample, and setting boundary conditions of the mechanical property simulation model;

[0061] S130: calculating a first load displacement curve according to the mechanical property simulation model;

[0062] S140: taking the undercut amount and the bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve as optimization targets, and optimizing parameters of the material failure model;

[0063] S150: substituting the parameters obtained by optimizing the material failure model into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, setting corresponding boundary conditions for simulation, and obtaining simulation results.

[0064] Optionally, before the material failure information is used to construct the material failure model, the method further includes a step of obtaining the material failure information, including the following steps:

[0065] Performing a tensile test on a uniaxial tensile sample and a notched tensile sample to obtain tensile test data;

[0066] Establishing a corresponding finite element model according to the tensile test data;

[0067] Obtaining a second load displacement curve of the uniaxial tensile sample and a third load displacement curve of the notched tensile sample by using the finite element model;

[0068] Obtaining mechanical property parameters, a true stress strain curve, and failure strain and stress triaxiality under different stress states of the material by comparing the second load displacement curve and the third load displacement curve.

[0069] Optionally, the step of obtaining the true stress strain curve includes the following steps:

[0070] Fitting the true stress strain curve by using Swift hardening criterion;

[0071] The expression of the true stress strain curve is:

[0072] σ=A(ε p +ε0) n ;

[0073] Wherein, σ is the true stress strain curve, A is a material constant, ε0 is a yield strain, and n is a stress hardening index.

[0074] Optionally, the material failure model is constructed according to the material failure information, including the following steps:

[0075] fitting the relationship between the failure strain and stress triaxiality under the different stress states to obtain a failure strain curve;

[0076] damage evolution parameters are obtained by adjusting and selecting the uniaxial tensile sample and the notched tensile sample;

[0077] The material failure model comprises the failure strain curve and the damage evolution parameters.

[0078] Optionally, the step of fitting the relationship between the failure strain and stress triaxiality under the different stress states to obtain a failure strain curve comprises the following steps:

[0079] The relationship between the failure strain and stress triaxiality under the different stress states is fitted by a fracture failure criterion to obtain an initial failure strain curve;

[0080] The initial failure strain curve is substituted into the self-piercing riveting joint forming simulation model for correction, and when the load and failure displacement errors are both less than a preset threshold, a target failure strain curve is obtained.

[0081] Optionally, the step of establishing the joint model comprises the following steps:

[0082] The joint model is established by a FEM-SPG hybrid modeling method;

[0083] The central region of the joint model is established by SPG particles, and the rest of the joint model except the central region is established by finite element units.

[0084] Optionally, the step of taking the undercut amount and bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve as optimization targets and optimizing the parameters of the material failure model comprises the following steps:

[0085] The undercut amount and bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve are taken as optimization targets, stress attenuation index, damage accumulation index, and fracture criterion fitting parameters in the material failure information are taken as the parameters of the material failure model, and the genetic particle swarm hybrid algorithm is used to optimize the parameters of the material failure model.

[0086] Next, the scheme of the embodiments of the present application will be described and explained in detail in combination with specific application examples.

[0087] To solve the above technical problems, the present embodiment provides a self-piercing riveting simulation prediction and parameter optimization method, comprising steps S1-S6:

[0088] S1, material failure information acquisition: uniaxial tensile test is carried out on a uniaxial tensile sample and a notched tensile sample, a corresponding finite element model is established, and the mechanical property parameters of the material, the true stress-strain curve, the failure strain and the stress triaxiality under different stress states are obtained through the load displacement curve of the standard tensile sample;

[0089] S2, material failure model construction: the relationship between failure strain and stress triaxiality is fitted to obtain the failure strain curve, and the initial failure parameters of the material are obtained by further adjusting and selecting the damage evolution parameters through the standard tensile sample;

[0090] S3, self-piercing riveting joint forming simulation: a self-piercing riveting joint forming simulation model is established, boundary conditions are set according to the test conditions, and joint section data and joint model are obtained, the joint model is used as the input content of the mechanical property test simulation;

[0091] S4, self-piercing riveting mechanical property test simulation: based on the joint model obtained by the forming simulation, a self-piercing riveting mechanical property simulation model is established according to the mechanical property sample, boundary conditions are set, and the load-displacement curve is calculated and obtained;

[0092] S5, failure model parameter optimization: according to the section data and load-displacement curve obtained in steps S3-S4, the undercut amount of the section, the size error of the bottom thickness, and the root mean square error of the load-displacement curve are used as the optimization target, and the parameters of the material failure model are optimized;

[0093] S6, self-piercing riveting forming and mechanical property simulation verification: the optimized material failure model parameters are substituted into the joint forming simulation model and the mechanical property simulation model, and the boundary conditions of steps S3 and S4 are set to obtain the simulation results.

[0094] For step S1, the finite element model does not set failure parameters, the mechanical property parameters include elastic modulus, yield stress, and the true stress-strain curve is fitted by Swift hardening criterion, the formula is as follows:

[0095] σ=A(ε p +ε0) n ;

[0096] Wherein, A is a material constant, ε0 is yield strain, n is stress hardening index, by adjusting the above parameters, the simulation curve before failure is matched with the test curve, when the simulation load and the test load error is less than 10%, the true stress-strain curve of the material is obtained.

[0097] For step S2, the failure strain curve is fitted by the fracture failure criterion, and the failure strain curve obtained by fitting is substituted into the simulation model for correction. When the load and failure displacement errors are both less than 10%, it is considered that the initial failure model is established.

[0098] For step S2, the damage evolution parameters include the stress attenuation index and the damage accumulation index. Given the initial values and ranges of the stress attenuation index and the damage accumulation index, the load displacement curve comprehensive error of each sample is adjusted to be small.

[0099] For step S3, the self-piercing rivet joint forming simulation model only includes the joint model, which is established by the FEM-SPG hybrid modeling method. The severely deformed region (central region) is established by SPG particles, and the remaining part is established by finite element units.

[0100] For the above requirements, the joint model includes the rivet, the rivet die, the pressure plate and the lap segment plate.

[0101] For step S4, the mechanical property simulation model should at least include the tensile performance model and the shear performance model.

[0102] For step S5, the parameters of the material failure model include the stress attenuation index, the damage accumulation index, and the fracture criterion fitting parameters in step S1. Further, the genetic-particle swarm hybrid algorithm is used to optimize the parameters of the material failure model.

[0103] Next, a more specific embodiment will be described.

[0104] The embodiment provides a self-piercing rivet joint mechanical property simulation prediction method coupled with a forming process, which has the implementation steps as shown in Figure 2 The method comprises the following steps:

[0105] S1, material failure information acquisition: tensile test is performed on uniaxial tensile samples and notched tensile samples, corresponding finite element models are established, and the mechanical property parameters of the material, the true stress-strain curve, the failure strain and the stress triaxiality under different stress states are obtained by the load displacement curve of the standard tensile sample;

[0106] S2, material failure model construction: the relationship between the failure strain and the stress triaxiality is fitted to obtain the failure strain curve, and the initial failure parameters of the material are obtained by further adjusting and selecting the damage evolution parameters through the standard tensile sample;

[0107] S3, self-piercing rivet joint forming simulation: a self-piercing rivet joint forming simulation model is established, boundary conditions are set according to the test conditions, self-piercing rivet joint cross-section data and joint models are obtained, and the joint model is used as the input content of the mechanical property test simulation;

[0108] S4, self-piercing riveting mechanical property test simulation: based on the joint model obtained by the forming simulation, a mechanical property simulation model of self-piercing riveting is established according to the mechanical property sample, the boundary conditions are set, and the load-displacement curve is calculated and obtained;

[0109] S5, failure model parameter optimization: according to the cross-section data and the load-displacement curve obtained in steps S3-S4, the undercut amount of the cross-section, the size error of the bottom thickness, and the root mean square error of the load-displacement curve are taken as the optimization target, and the material failure parameters are optimized; exemplarily, Figure 3 An example flowchart for optimizing material failure parameters is provided.

[0110] S6, self-piercing riveting forming and mechanical property simulation verification: the optimized material failure model parameters are substituted into the joint forming simulation model and the mechanical property simulation model, and the boundary conditions of steps S3 and S4 are set to obtain the simulation results.

[0111] Preferably, for step S1, uniaxial tensile samples and notched samples are set for the upper and lower plate materials, the notched samples include 0-degree shear samples, 45-degree shear samples, hole opening samples, and R20 samples, a finite element model consistent with the actual sample is established, the elastic modulus, yield stress, and initial true stress-strain curve of the material are obtained by uniaxial tensile test, and further, all notched tensile samples are calibrated, and the material hardening coefficient is adjusted to minimize the error of each sample.

[0112] Preferably, for step S1, after determining the material performance parameters, the stress triaxiality-failure strain curve at the fracture site center unit fracture time of the simulation model corresponding to the sample is extracted, and further, the stress triaxiality-failure strain curve is integrated to obtain the average stress triaxiality and the corresponding failure strain.

[0113] Preferably, for step S2, the Modified Mohr-Coulomb (MMC) fracture criterion is used to fit the stress triaxiality and failure relationship obtained above to obtain the initial failure strain curve, and the fracture criterion is as follows:

[0114]

[0115] x1, x2, x3 are the fracture criterion fitting parameters, the fitted failure strain curve is substituted into the simulation model for calibration, and when the load and failure displacement error are both less than 10%, the initial failure model is considered to be established;

[0116] Preferably, the damage evolution parameters include stress decay index m and damage accumulation index n, the initial value of the stress decay index is 3.5, the index value range is 2-5, the initial value of the damage accumulation index is 4, the index value range is 1-7, and the average error of the load displacement curve of each sample is minimized by continuously adjusting the combination of the damage evolution parameters.

[0117] Preferably, for step S3, a self-piercing rivet joint forming simulation model is established, and in order to reduce the amount of calculation and the applicability of the generated joint model, only the model within the lap section is established. Further, for the construction of the joint forming simulation model, in order to avoid the problem of non-conservation of mass and energy caused by the traditional finite element method, the FEM-SPG hybrid modeling method is used to realize it, and further, in order to save computing resources and reduce calculation time, the severely deformed area of the upper plate near the rivet shank is established by SPG particles, and the rest is established by finite element units.

[0118] Further, the boundary conditions of the forming simulation model are set, including punch radial constraint, punch axial velocity, pressure plate radial constraint, pressure plate pressure force and die constraint, wherein the punch axial velocity and the pressure plate pressure force are set according to the test conditions, which are 250mm / s and 2kN respectively in this embodiment.

[0119] Further, the joint model after forming is reserved, which includes the riveted upper and lower plates and the rivet.

[0120] Preferably, for step S4, based on the joint model generated in step S3, a riveted joint pull performance model and a shear performance model are established, the remaining part of the model is modeled by finite element units, and the boundary constraints and displacement of the model are set, wherein for the pull performance model, the displacement amount should be greater than 10mm, and for the shear performance model, the displacement amount should be greater than 5mm.

[0121] Preferably, for step S5, the fracture criterion fitting parameters x1, x2, x3, the stress decay index and the damage accumulation index are optimized, given the value range of each parameter, the value range of the fracture criterion fitting parameters is set to [0.5x1, 1.5x1], [0.5x2, 1.5x2], [0.5x3, 1.5x3] respectively, the given range of the stress decay index is [m-1, m+1], and the given range of the damage accumulation index is [1, n+2], different material failure parameter groups are obtained by Latin hypercube sampling, the parameter groups are input into the simulation model to obtain the calculation results under each parameter group, and the undercut error and the bottom thickness size error of the joint section, and the root mean square error of the load displacement curve are extracted and calculated, wherein the root mean square error of the load displacement curve is the weighted root mean square error of the tensile performance and the shear performance, and the average root mean square error is taken in this embodiment. Further, a Kriging model is constructed, an optimization model is established, the optimization model is solved by a genetic particle swarm hybrid algorithm, and finally a material failure parameter combination meeting the requirements is obtained. The final material failure parameter combination is brought into the simulation model to calculate the joint forming result and the mechanical property test result.

[0122] The embodiment provides a self-punching riveting simulation prediction and parameter optimization method, optimizes material failure parameters based on simulation results of self-punching riveting, improves adaptability of the material failure parameters to self-punching riveting and similar large deformation conditions, improves simulation accuracy, avoids result uncertainty caused by conventional parameter fine tuning, improves process optimization efficiency, and in addition, the particle method is used to model self-punching riveting, so that the problems of mass-energy non-conservation and calculation instability caused by the element deletion method can be effectively avoided.

[0123] Reference Figure 4 The embodiment of the application also provides a self-punching riveting simulation prediction and parameter optimization device, which can realize the self-punching riveting simulation prediction and parameter optimization method.

[0124] A failure model establishing unit is configured to construct a material failure model according to material failure information;

[0125] A simulation model setting unit is configured to set boundary conditions of a self-punching riveting joint forming simulation model according to a test working condition, to obtain cross-sectional data and a joint model of the self-punching riveting joint;

[0126] A mechanical model establishing unit is configured to establish a mechanical property simulation model of the self-punching riveting based on the joint model and according to a mechanical property sample, and to set boundary conditions of the mechanical property simulation model;

[0127] A curve calculating unit is configured to calculate a first load displacement curve according to the mechanical property simulation model;

[0128] a parameter optimization unit configured to optimize parameters of the material failure model by taking undercut amount and bottom thickness dimension error of the cross section in the cross section data and root mean square error of the first load displacement curve as optimization targets;

[0129] a simulation test unit configured to simulate the self-piercing riveting joint forming simulation model and the mechanical property simulation model by substituting the optimized parameters of the material failure model into the self-piercing riveting joint forming simulation model and the mechanical property simulation model and setting corresponding boundary conditions, and obtain simulation results.

[0130] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically implement the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0131] The embodiment of the present application further provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of the embodiment of the present application when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer and the like.

[0132] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically implement the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0133] Please refer to Figure 5 , Figure 5 the hardware structure of the electronic device of another embodiment is shown, and the electronic device comprises:

[0134] The processor 501 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0135] The memory 502 can be implemented in the form of ROM (Read Only Memory), a static storage device, a dynamic storage device, or RAM (Random Access Memory). The memory 502 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are saved in the memory 502 and called and executed by the processor 501 to implement the method of the embodiments of the present application.

[0136] The input / output interface 503 is configured to realize information input and output.

[0137] The communication interface 504 is configured to realize communication interaction between the device and other devices, and the communication can be realized through a wired manner (for example, a USB, a network cable, or the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, or the like).

[0138] The bus 505 is configured to transmit information between various components (for example, the processor 501, the memory 502, the input / output interface 503, and the communication interface 504) of the device.

[0139] The processor 501, the memory 502, the input / output interface 503, and the communication interface 504 are communicatively connected to each other in the device through the bus 505.

[0140] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method of the present application.

[0141] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiment, the present storage medium embodiment specifically realizes the functions of the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0142] The memory is a non-transitory computer readable storage medium, and can be used to store a non-transitory software program and a non-transitory computer executable program. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and the remote memory can be connected to the processor through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0143] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0144] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0145] The apparatus embodiments described above are merely exemplary, wherein the units described as separate components can or can not be physically separate, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0146] Those skilled in the art can understand that all or some of the steps in the method disclosed above, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0147] The terms "first", "second", "third", "fourth" and the like in the description of the application and in the claims of the foregoing drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so construed can be interchanged, such that the embodiments of the application described herein can be carried out in other than the order discussed herein without departing from the scope of the application. Further, the terms "comprise" and "comprising" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, product or apparatus that comprises a list of steps or units does not necessarily comprise only those steps or units but can include other not expressly listed steps or units. The terms "comprise", "comprising", "include", "including" and "includes" are used in their open, conventional sense and can be used in conjunction with other terms such as "consisting of", "consisting essentially of", and "consisting of", unless these terms are specifically excluded.

[0148] It should be understood that in the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are three cases: only A, only B, and A and B at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0149] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the above units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0150] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0151] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0152] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that makes a contribution or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0153] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, but this does not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A method for self-piercing riveting simulation prediction and parameter optimization, characterized in that, The method comprises the following steps: According to the material failure information, a material failure model is constructed; According to the test working condition, the boundary conditions of the self-piercing riveting joint forming simulation model are set, the cross-section data and the joint model of the self-piercing riveting joint are obtained; According to the joint model, a mechanical property simulation model of the self-piercing riveting joint is established according to the mechanical property sample, and the boundary conditions of the mechanical property simulation model are set; According to the mechanical property simulation model, a first load displacement curve is calculated and obtained; The undercut amount and the bottom thickness size error of the cross-section in the cross-section data and the root mean square error of the first load displacement curve are taken as the optimization target, and the parameters of the material failure model are optimized; The parameters obtained by optimizing the material failure model are substituted into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, and the corresponding boundary conditions are set for simulation to obtain a simulation result.

2. The method according to claim 1, wherein, Before the step of constructing the material failure model according to the material failure information, the method further comprises the step of obtaining the material failure information, comprising the following steps: Tensile tests are performed on uniaxial tensile samples and notched tensile samples to obtain tensile test data; According to the tensile test data, a corresponding finite element model is established; The second load displacement curve of the uniaxial tensile sample and the third load displacement curve of the notched tensile sample are obtained by using the finite element model; The mechanical property parameters, the true stress-strain curve, and the failure strain and stress triaxiality under different stress states of the material are obtained by comparing the second load displacement curve and the third load displacement curve.

3. The method of claim 2, wherein, The step of obtaining the true stress-strain curve comprises the following steps: The true stress-strain curve is fitted by using the Swift hardening criterion; The expression of the true stress-strain curve is: σ = A(ε p + ε0) n ; Wherein, σ is the true stress-strain curve, A is a material constant, ε0 is the yield strain, and n is the stress hardening index.

4. The method of claim 2, wherein, The step of constructing the material failure model according to the material failure information comprises the following steps: The relationship between the failure strain and the stress triaxiality under different stress states is fitted to obtain a failure strain curve; The damage evolution parameters are adjusted and selected by comparing the uniaxial tensile sample and the notched tensile sample; The material failure model comprises the failure strain curve and the damage evolution parameters.

5. The method of self-piercing riveting simulation, prediction and parameter optimization according to claim 4, characterized in that, The step of fitting the relationship between the failure strain and the stress triaxiality under different stress states to obtain the failure strain curve comprises the following steps: The relationship between the failure strain and the stress triaxiality under different stress states is fitted by using the fracture failure criterion to obtain an initial failure strain curve; The initial failure strain curve is substituted into the self-piercing riveting joint forming simulation model for correction, and when the load and failure displacement error are both less than a preset threshold, a target failure strain curve is obtained.

6. The method of self-piercing riveting simulation, prediction and parameter optimization of claim 1, wherein, The step of establishing the joint model comprises the following steps: The joint model is established by using the FEM-SPG hybrid modeling method; The central region of the joint model is established by using SPG particles, and the remaining part of the joint model except the central region is established by using finite element units.

7. The method of self-piercing riveting simulation, prediction and parameter optimization according to any one of claims 1 to 6, characterized in that, The undercut amount and bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve are taken as optimization targets to optimize parameters of the material failure model, including the following steps: The undercut amount and bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve are taken as optimization targets, and stress attenuation index, damage accumulation index and fracture criterion fitting parameters in the material failure information are taken as parameters of the material failure model, and the genetic particle swarm hybrid algorithm is used to optimize the parameters of the material failure model.

8. A self-piercing riveting simulation prediction and parameter optimization apparatus, characterized by, The device comprises: A failure model establishing unit configured to construct a material failure model according to material failure information; A simulation model setting unit configured to set boundary conditions of a self-piercing riveted joint forming simulation model according to test conditions to obtain section data and a joint model of the self-piercing riveted joint; A mechanical model establishing unit configured to establish a mechanical property simulation model of the self-piercing riveted joint based on the joint model according to a mechanical property sample and set boundary conditions of the mechanical property simulation model; A curve calculating unit configured to calculate a first load displacement curve according to the mechanical property simulation model; A parameter optimizing unit configured to take the undercut amount and bottom thickness size error of the section in the section data and the root mean square error of the first load displacement curve as optimization targets to optimize parameters of the material failure model; A simulation test unit configured to substitute the optimized parameters of the material failure model into the self-piercing riveted joint forming simulation model and the mechanical property simulation model, set corresponding boundary conditions for simulation, and obtain simulation results.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the method of any one of claims 1 to 7.

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