A digital unit method composite bolt strength prediction method and device

Abstract

The application provides a digital unit method composite bolt strength prediction method and device, relates to the technical field of composite material mechanics strength analysis, and comprises the following steps: obtaining fiber orientation distribution characteristics based on an electron micrograph of a composite bolt; constructing a bolt structure digital unit model based on the fiber orientation distribution characteristics by using a digital unit method; respectively establishing a first strength constitutive model of fibers in the composite bolt and a second strength constitutive model of a resin matrix; sequentially assembling, finite element mesh dividing and then loading the bolt structure digital unit model, and then solving based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud picture, so as to determine the tensile strength based on the damage cloud picture. The application can accurately simulate a damage evolution process and realize high-precision prediction of the tensile strength.

Description

Technical Field

[0001] This invention relates to the field of composite material mechanical strength analysis technology, and in particular to a digital element method and apparatus for predicting the strength of composite bolts. Background Technology

[0002] Composite bolts possess advantages such as lightweight, high strength, and corrosion resistance, and have broad application prospects in aerospace, automotive, and other fields. However, due to the orientation of their fibers, they lack the isotropic properties of metal bolts and fasteners, exhibiting highly complex failure modes and damage mechanisms. This is especially true within the fine threads, where fibers exhibit multiple complex distribution patterns, directly impacting the bolt's mechanical behavior. Therefore, effectively constructing refined distribution models for bolts with complex fiber distributions, evaluating anisotropic behavior and failures, and achieving performance prediction are of great significance for their application design and service safety assurance.

[0003] Existing methods for predicting and evaluating material failure have the following shortcomings, making it difficult to meet practical needs: The thread structure of composite bolts is a key load-bearing component of the connection system, and its morphological design differs significantly from common composite structures (such as laminates and shells). The fiber arrangement at the threads of composite bolts is complex, leading to a more complex failure mechanism under load: multi-directional stress coupling and tensile-compressive load asymmetry. However, commonly used macroscopic homogeneous model methods cannot accurately characterize the entire process of fiber-matrix interaction from damage initiation to failure, or the interaction between fibers and the matrix during failure. While multi-scale RVE model analysis methods consider the influence of fibers on bolt tensile strength to some extent, they are actually simplifications of the model and still cannot account for the fiber distribution in the bolt structure, nor can they analyze the damage evolution and the interaction between fibers and the matrix during the failure process. Therefore, traditional methods have low accuracy in predicting the tensile strength of thermoplastic composite bolts and are difficult to accurately simulate the entire process from damage initiation to final failure, as well as the influence of fiber orientation on structural failure modes.

[0004] Therefore, there is an urgent need for a digital unit method for predicting the strength of composite bolts. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a digital element method for predicting the strength of composite bolts. This method directly constructs a refined model of the bolt based on the digital element method, taking into account fiber morphology and distribution, accurately simulating the damage evolution process, and achieving high-precision prediction of tensile strength.

[0006] In a first aspect, embodiments of the present invention provide a method for predicting the strength of composite material bolts using the digital element method, comprising: The fiber orientation distribution characteristics were obtained from the electron micrographs of the composite material bolts. A digital element model of the bolt structure is constructed based on the fiber orientation and distribution characteristics using the digital element method. A first strength constitutive model for the fibers and a second strength constitutive model for the resin matrix in the composite bolt are established respectively. The bolt structure digital element model is assembled and meshed sequentially. Then, load conditions are applied, and the model is solved based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map. The tensile strength is then determined based on the damage cloud map.

[0007] Secondly, embodiments of the present invention also provide a digital unit method composite material bolt strength prediction device, comprising: The acquisition module is used to acquire fiber orientation distribution characteristics based on electron micrographs of the composite material bolts; The first construction module is used to construct a digital element model of the bolt structure based on the fiber orientation distribution characteristics using the digital element method. The second construction module is used to establish the first strength constitutive model of the fiber in the composite bolt and the second strength constitutive model of the resin matrix, respectively. The simulation module is used to assemble and mesh the digital unit model of the bolt structure in sequence, then apply load conditions, and solve the model based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map, and determine the tensile strength based on the damage cloud map.

[0008] Thirdly, embodiments of the present invention also provide a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the digital unit method for predicting the strength of composite bolts as described above.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the digital unit method for predicting the strength of composite bolts as described above.

[0010] Fifthly, embodiments of the present invention also provide a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the digital unit method composite material bolt strength prediction method described in any of the first aspects of this specification.

[0011] Compared with the prior art, the present invention has at least the following beneficial effects: This invention provides a method and apparatus for predicting the strength of composite bolts using the digital element method. This method acquires the actual fiber orientation distribution characteristics and then uses the digital element method to directly construct a realistic microscopic model (i.e., a bolt structure digital element model) based on these characteristics. This allows for accurate characterization of the fiber orientation distribution in the composite bolt structure. Such a bolt structure digital element model can completely simulate and reflect the entire process of the bolt structure under load, from damage initiation to failure, and the interaction between the fibers and the matrix during failure. Therefore, it has higher calculation accuracy for stress concentration in the bolt structure and shear stress distribution between the fibers and the resin matrix, thereby improving the prediction accuracy of tensile strength. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a digital unit method for predicting the strength of composite bolts according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a digital unit model of a bolt structure provided in an embodiment of the present invention; Figure 3 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention; Figure 4 This is a structural diagram of a composite material bolt strength prediction device provided in an embodiment of the present invention using the digital unit method. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] The following is the concept of the present invention, such as Figure 1 As shown, this embodiment of the invention provides a method for predicting the strength of composite bolts using the digital element method. The method includes: Step 100: Obtain fiber orientation distribution characteristics based on electron micrographs of composite bolts; Step 102: Construct a digital element model of the bolt structure based on the fiber orientation distribution characteristics using the digital element method; Step 104: Establish the first strength constitutive model of the fiber in the composite bolt and the second strength constitutive model of the resin matrix, respectively. Step 106: The bolt structure digital element model is assembled and meshed sequentially. Then, load conditions are applied, and the model is solved based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map. The tensile strength is then determined based on the damage cloud map.

[0016] In this embodiment of the invention, by obtaining the actual fiber orientation distribution characteristics, a realistic microscopic model containing the fiber distribution (i.e., a bolt structure digital element model) is directly constructed based on these characteristics using the digital element method. This allows for accurate characterization of the fiber orientation distribution in the composite bolt structure. Then, load conditions are applied to the bolt structure digital element model for simulation, and the constructed strength constitutive models for the fibers and resin matrix are solved to obtain a damage cloud map. Further analysis of this damage process is then used to determine the predicted tensile strength. This bolt structure digital element model can completely simulate and reflect the entire process of damage initiation and failure of the bolt structure under load, including the interaction between the fibers and the matrix during failure. Therefore, it has higher calculation accuracy for stress concentration in the bolt structure and the shear stress distribution between the fibers and the resin matrix, thereby improving the prediction accuracy of tensile strength.

[0017] The following description Figure 1 The execution method of each step is shown.

[0018] In step 100, the electron micrographs include, but are not limited to, SEM images. Statistical analysis of fiber distribution is performed based on the electron micrographs to obtain fiber orientation distribution characteristics. The composite bolt is made by combining fibers and a resin matrix, specifically by impregnating fibers in a resin matrix to obtain a prepreg, which is then laid and cured. It should be noted that in this invention, the resin matrix is ​​a thermoplastic resin matrix.

[0019] In step 102, a digital element model of the bolt structure is constructed based on the fiber orientation distribution characteristics using the digital element method, including: For the fiber bundles included in the fiber orientation distribution characteristics, the fiber bundles are discretized into multiple digital chains based on the digital unit method; wherein, each digital chain is a virtual fiber, and the digital chain contains multiple digital unit chains and nodes; A solid unit is created to represent the resin matrix, and the digital chain is wrapped with the solid unit to obtain a bolt structure digital unit model.

[0020] It should be noted that the digital element model of the bolt structure also defines the material properties of the fibers and resin matrix (including but not limited to elastic modulus, Poisson's ratio, etc.) and damage parameters. The solid elements are not independent geometric bodies, but rather constitute multiphase coupled mesoscopic structural elements together with the virtual fibers, achieving integrated modeling of the fibers and resin matrix. Specifically, the digital element model of the bolt structure is constructed using ABAQUS finite element software, followed by simulation calculations under applied load conditions.

[0021] In a preferred embodiment, such as Figure 2 As shown, beam elements are used as the digital unit chain; the digital chain is formed by connecting several beam elements through nodes; and generalized contact is provided between the digital unit chains.

[0022] It should be noted that in the threaded portion of the composite material bolt, the digital chain is wavy, and the nodes can correspond to the peaks or troughs of the wavy digital chain. Figure 2 The enlarged view in the image shows only one digital chain. In this embodiment of the invention, if the digital unit chain is small enough, the bending of the virtual fiber will be more flexible, thereby more accurately simulating the morphology of the fiber bundle.

[0023] In a more preferred embodiment, the beam element has a circular cross-section, the radius of which is determined based on the fiber volume fraction in the composite bolt and the number of beam elements.

[0024] Specifically, in the cross-section of the composite bolt structure, the volume fraction of fibers in the composite bolt is equal to the number of beam elements multiplied by the area of ​​the circular cross-section, which is then divided by the total area of ​​the cross-section of the composite bolt structure. In this case, the number of beam elements corresponds to the number of digital chains in the composite bolt structure; preferably, the number of digital chains is close to one thousand.

[0025] More specifically, it also includes: A suspension test was conducted on the prepreg used to make the composite bolts to obtain the bending stiffness of the prepreg. The ratio of the bending stiffness of the prepreg to the number of beam elements in the cross section of the composite bolt structure is taken as the expected bending stiffness of the beam element. The desired transverse shear stiffness is calculated based on the desired bending stiffness, the diameter of the circular section of the beam element, and the Poisson's ratio of the fiber, with the desired transverse shear stiffness and the tensile modulus of the fiber used as input parameters.

[0026] Specifically, the flexural modulus of prepreg at different temperatures can be measured experimentally. Since prepreg is a composite of resin matrix and fiber, and the flexural modulus of the fiber is much greater than that of the resin matrix, the flexural modulus of the resin matrix can be ignored. in, B For the bending stiffness of the prepreg; N f The number of beam elements in the cross-section of the composite bolt structure; B f Let be the desired bending stiffness of the beam element; Meanwhile, the bending stiffness of the beam element can be expressed by the following formula: The transverse shear stiffness can be expressed by the following formula: Furthermore, the ratio of bending stiffness to transverse shear stiffness is: At this point, the transverse shear stiffness can be expressed by the following formula: in, E f The tensile modulus of the fiber; d f ν is the diameter of the circular cross-section of the beam element; ν is the Poisson's ratio of the fiber; Since the fiber can be represented by prepreg after ignoring the resin matrix, the expected flexural stiffness of the beam element calculated earlier can be substituted into the above formula for calculating the transverse shear stiffness to obtain the expected transverse shear stiffness of the prepreg.

[0027] In this embodiment of the invention, a bolt structure digital unit model is constructed, and the fibers are discretized into an array of thousands of digital unit chains to simulate fiber deformation. Each digital unit chain is composed of beam elements connected by nodes, and generalized contact is established between the digital unit chains. Theoretically, the best simulation effect is achieved when the number of digital unit chains is close to the actual number of fibers, but this would result in excessively high computational costs. Therefore, the beam elements of the digital unit chains adopt circular cross-sections, with the radius determined based on the prepreg fiber volume fraction and the number of beam elements, and the tensile stiffness is taken as the carbon fiber modulus. However, since the diameter of the digital unit chain is larger than the actual fiber size, it will cause the problem of excessively high bending stiffness. Therefore, it is necessary to re-measure the bending stiffness of the beam elements using the above method, and to decouple the tensile modulus from the transverse shear stiffness so that the beam elements have the desired bending stiffness.

[0028] In step 104, the first strength constitutive model is a transversely isotropic linear elastic model, and the maximum stress criterion is used as the fracture criterion. The second strength constitutive model adopts an isotropic elastoplastic constitutive relation based on the elastic modulus and yield strength.

[0029] Specifically, the second strength constitutive model adopts the Johnson-Cook elastoplastic constitutive model.

[0030] In a preferred embodiment, based on the measured material parameter data, the ExpDec1 function is used for fitting, and the elastic modulus and yield strength of the resin matrix are determined by the following formulas: in, C The crystallinity is determined based on the processing temperature of the composite material bolt; E ( C ) represents the degree of crystallinity. C The elastic modulus of the resin matrix at that time; σ y ( C ) represents the degree of crystallinity. C The yield strength of the resin matrix.

[0031] In a preferred embodiment, determining the crystallinity based on the processing temperature of the composite bolt includes: Differential scanning calorimetry (DSC) curves of the resin matrix at different cooling rates were obtained. For differential scanning calorimetry curves at any cooling rate, the following steps are performed: The semi-crystallization time is determined based on the differential scanning calorimetry curve; the crystallization rate constant is determined based on the semi-crystallization time; and the data corresponding to the differential scanning calorimetry curve are fitted and analyzed to obtain the Avrami index. Substituting the crystallization rate constant and the Avrami exponent into the Avrami equation, the relative crystallinity equation at this cooling rate is obtained; The processing temperature and cooling rate at which the composite bolt is made are obtained. Based on the cooling rate, the corresponding relative crystallinity equation is determined, and the processing temperature is substituted into the relative crystallinity equation to output the crystallinity.

[0032] It should be noted that the cooling rate when producing composite bolts is the cooling rate of the thermoplastic resin prepreg; the relative crystallinity equations are different for different cooling rates.

[0033] Specifically, under the assumption of a constant crystallization temperature, the Avrami equation describes the crystal growth process: By taking the logarithm of both sides of the above equation, we obtain the following expression: In the formula, X c (t )for t Relative crystallinity at any given time; Z t Here, denoted as the crystallization rate constant, is expressed as a nucleation and growth rate parameter, with units of min. -n ; n The Avrami index represents the nucleation and growth mechanism during crystallization. Its value is equal to the spatial dimension of crystal growth plus the temporal dimension of the nucleation process. Further integration of the above formula yields the following expression: in, k It is a non-isothermal crystallization rate constant related to the cooling rate; k With the isothermal crystallization rate constant Z t It has the following relationship: However, it is difficult to quantitatively measure the crystallization rate during the entire crystallization process, but the half-crystallization time t can be measured relatively accurately. 1 / 2 Methods for determining the semi-crystallization time include, but are not limited to, differential scanning calorimetry, polarized light microscopy (for direct observation of spherulite growth), and X-ray diffraction (for quantitative analysis of crystalline phase evolution). If 50% relative crystallinity is observed at this semi-crystallization time, then... X c ( t Substituting 0.5 into the equation, for primary nucleation, the crystallization rate constant and the half-crystallization time t... 1 / 2 There is a certain relationship between them, so Z t It can be expressed as the following formula: Thus, by fitting the results of different heating and cooling rates, the relationship function between the half-crystallization time and the Avrami index under any operating condition can be obtained.

[0034] Therefore, for any cooling rate, the differential scanning calorimetry curve is subjected to the following: Based on this differential scanning calorimetry curve, the crystallization initiation temperature and semi-crystallization time (t) were determined. 1 / 2 ); and perform fitting analysis on the data corresponding to the differential scanning calorimetry curve to obtain the Avrami index ( ); n ); based on the half-crystallization time (t) 1 / 2 ) and Avrami index ( n Determine the crystallization rate constant ( ); The crystallization rate constant ( Z t) and the Avrami index ( n Substituting these values ​​into the Avrami equation, we obtain the relative crystallinity equation at this cooling rate; the relative crystallinity equation at this point is: The molding process temperature data of the composite bolts is obtained, the corresponding relative crystallinity equation is determined based on the cooling rate contained in the molding process temperature data, and the crystallinity is output based on the molding process temperature data and the relative crystallinity equation.

[0035] Specifically, the molding process temperature data includes complete temperature-time data during the fabrication of composite bolts, including the melting and holding phase, the cooling phase, and the low-temperature setting phase. Based on this molding process temperature data, information such as the cooling rate, crystallization initiation temperature / time, and crystallization termination temperature / time can be further determined, thereby determining the integral time. t The crystallinity is obtained by substituting the integral time into the relative crystallinity equation.

[0036] In this embodiment of the invention, for thermoplastic matrix materials, crystallinity has a significant impact on the elastic modulus and material strength of the resin matrix, and different composite bolt manufacturing processes affect crystallinity. However, in the prior art, it is assumed that the mechanical properties of the resin matrix are the same under different manufacturing processes. Therefore, in order to achieve high-precision prediction of the tensile strength of composite bolts, crystallinity is introduced into the functional relationship of the strength parameters of the resin matrix.

[0037] In step 106, the digital element model of the bolt structure is assembled and meshed sequentially, including: The digital element model of the bolt structure is assembled with the digital element model of the nut structure, and then a tetrahedral mesh is used for finite element mesh generation; wherein the mesh size of the bolt region is smaller than the mesh size of the nut region.

[0038] Specifically, after completing the assembly of the bolt and nut structures, they are meshed. For example, the global mesh size for the bolt area is set to 0.5mm, and the global mesh size for the nut area is set to 1mm, both using tetrahedral meshes. Applying tensile load conditions includes applying preload, and setting tensile load and boundary condition constraints. For applying preload: first, a reference point is created on the bolt shank surface of the bolt structure digital element model. An axial preload (e.g., 100N) is applied through the Bolt load step, and the Adjust length mode is selected to simulate the tightening effect. For setting tensile load and boundary condition constraints: all degrees of freedom at the lower end of the bolt are fixed, i.e., the Fixed constraint type is selected, and a tensile displacement load is applied to the nut.

[0039] For step 106, the damage contour map is obtained by solving the first strength constitutive model and the second strength constitutive model, and the tensile strength is determined based on the damage contour map, including: The damage evolution process of the fiber and matrix is ​​calculated in real time based on the first strength constitutive model and the second strength constitutive model, and the damage variable field is output. The tensile load-displacement curve is determined based on the damage variable field, and the peak load in the tensile load-displacement curve is extracted as the tensile strength.

[0040] It should be noted that in step 106, the bolt structure digital element model is integrated with the first strength constitutive model and the second strength constitutive model, and the UMAT subroutine is called for nonlinear solution. Specifically, the fiber orientation distribution characteristics from step 100 are transformed into a material orientation field in the spatial coordinate system of the bolt structure digital element model after finite element mesh generation. This field can then be directly used as input parameters for the transversely isotropic linear elastic model in the UMAT subroutine, thereby achieving a mapping from microstructure to macroscopic response. Furthermore, both strength constitutive models reside within the same UMAT subroutine, with regions distinguished by material partitioning identifiers, allowing for dynamic calling of different strength constitutive models and achieving synergistic response of multiphase materials. Specifically, this invention only demonstrates a method for predicting tensile strength; similarly, methods for predicting other strengths can also be implemented based on the concept of this invention.

[0041] In this embodiment of the invention, the damage cloud map is a visualization of the spatial distribution of damage variables driven by the UMAT subroutine, representing the elastic modulus and yield strength. Essentially, it is the output of the two strength constitutive models mentioned above. The damage cloud map includes fiber fractures appearing as strips (along the fiber direction) and resin matrix damage appearing as rings (around the bolt hole). The damage evolution sequence is resin matrix microcracks – interface debonding – fiber fracture – overall failure. Then, based on the damage variable field, the tensile load-displacement curve can be extracted using existing techniques. The peak load in this curve represents the tensile strength of the composite bolt, a value consistent with the definition of tensile strength in composite materials in ASTM D3039, representing the limit of load-bearing capacity before failure.

[0042] Compared to the traditional solid element method, which typically treats fibers and resins as anisotropic continuum models based on macroscopic homogenization assumptions, the digital element method proposed in this invention simplifies each fiber bundle or even a single fiber into a digital chain connected by discrete nodes. This method can accurately reconstruct the mesoscopic topology of fibers, including fiber bundle compression, buckling deformation, and complex contact relationships, and can more realistically reflect the complex geometric nonlinearities within composite materials. In contrast, the traditional solid element method omits the precise spatial path of individual fibers (such as fiber buckling and interlacing relationships in woven composites), making it difficult to capture performance fluctuations caused by local fiber orientation deviations. Moreover, the traditional solid element method outputs the average stress of the elements, which can easily mask local stress peaks at the fiber-resin interface and cannot accurately analyze the force transmission process between fibers. The digital element method proposed in this invention, by directly and independently modeling fibers and resins (using embedded constraints), can clearly reveal the stress situation of fiber bundles. Therefore, it has higher calculation accuracy for stress concentration in structures and shear stress distribution between fibers and the resin matrix. Thus, the digital element method proposed in this invention has higher tensile strength prediction accuracy for composite bolt strength prediction.

[0043] like Figure 3 , Figure 4 As shown, this invention provides a digital unit method composite bolt strength prediction device. The device can be implemented via software, hardware, or a combination of both. From a hardware perspective, such as... Figure 3 The diagram shown is a hardware architecture diagram of a computing device for a digital unit method composite material bolt strength prediction device provided in an embodiment of the present invention. (Except for...) Figure 3 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 4 As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into memory and running it. This embodiment provides a digital unit method composite material bolt strength prediction device, comprising: The acquisition module 400 is used to acquire fiber orientation distribution characteristics based on the electron micrograph of the composite material bolt; The first construction module 402 is used to construct a digital unit model of the bolt structure based on the fiber orientation distribution characteristics using the digital unit method. The second construction module 404 is used to establish the first strength constitutive model of the fiber in the composite bolt and the second strength constitutive model of the resin matrix, respectively. The simulation module 406 is used to assemble and mesh the digital unit model of the bolt structure in sequence, then apply load conditions, and solve the model based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map, and determine the tensile strength based on the damage cloud map.

[0044] In some specific implementations, the acquisition module 400 can be used to perform the above step 100, the first construction module 402 can be used to perform the above step 102, the second construction module 404 can be used to perform the above step 104, and the simulation module 406 can be used to perform the above step 106.

[0045] Since the contents of the above-described apparatus are based on the same concept as the method embodiments of the present invention, the specific contents can be found in the descriptions in the method embodiments of the present invention, and will not be repeated here.

[0046] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a digital unit method composite material bolt strength prediction device. In other embodiments of the present invention, a digital unit method composite material bolt strength prediction device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0047] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0048] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a digital unit method for predicting the strength of composite bolts according to any embodiment of this invention.

[0049] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a digital unit method for predicting the strength of composite bolts according to any embodiment of this invention.

[0050] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform a digital unit method for predicting the strength of composite bolts as described in any of the above embodiments.

[0051] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0052] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0053] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0054] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, system, or device.

[0055] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0056] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0057] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0058] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0060] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the strength of composite bolts using the digital element method, characterized in that, include: The fiber orientation distribution characteristics were obtained from the electron micrographs of the composite material bolts. A digital element model of the bolt structure is constructed based on the fiber orientation and distribution characteristics using the digital element method. A first strength constitutive model for the fibers and a second strength constitutive model for the resin matrix in the composite bolt are established respectively. The bolt structure digital element model is assembled and meshed sequentially. Then, load conditions are applied, and the model is solved based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map. The tensile strength is then determined based on the damage cloud map.

2. The method according to claim 1, characterized in that, The method of constructing a digital element model of the bolt structure based on the fiber orientation distribution characteristics using the digital element method includes: For the fiber bundles included in the fiber orientation distribution characteristics, the fiber bundles are discretized into multiple digital chains based on the digital unit method; wherein, each digital chain is a virtual fiber, and the digital chain contains multiple digital unit chains and nodes; A solid unit is created to represent the resin matrix, and the digital chain is wrapped with the solid unit to obtain a bolt structure digital unit model.

3. The method according to claim 2, characterized in that, Beam elements are used as the digital unit chain; the digital chain is formed by connecting several beam elements through nodes; generalized contact is provided between the digital unit chains; Preferably, the beam unit has a circular cross-section, and the radius of the circular cross-section is determined according to the fiber volume fraction in the composite bolt and the number of beam units.

4. The method according to claim 1, characterized in that, The first strength constitutive model is a transversely isotropic linear elastic model, and the maximum stress criterion is used as the fracture criterion. The second strength constitutive model adopts an isotropic elastoplastic constitutive relation based on the elastic modulus and yield strength.

5. The method according to claim 4, characterized in that, The elastic modulus and the yield strength are determined by the following formulas: in, C The crystallinity is determined based on the processing temperature of the composite material bolt; E ( C ) represents the degree of crystallinity. C The elastic modulus of the resin matrix at that time; σ y ( C ) represents the degree of crystallinity. C Yield strength of the resin matrix at that time; Preferably, determining the crystallinity based on the processing temperature of the composite material bolt includes: Differential scanning calorimetry (DSC) curves of the resin matrix at different cooling rates were obtained. For differential scanning calorimetry curves at any cooling rate, the following steps are performed: The semi-crystallization time is determined based on the differential scanning calorimetry curve; and the data corresponding to the differential scanning calorimetry curve are fitted and analyzed to obtain the Avrami index; the crystallization rate constant is determined based on the semi-crystallization time and the Avrami index. Substituting the crystallization rate constant and the Avrami exponent into the Avrami equation, the relative crystallinity equation at this cooling rate is obtained; Obtain the molding process temperature data when the composite material bolt is made, determine the corresponding relative crystallinity equation based on the cooling rate contained in the molding process temperature data, and output the crystallinity based on the molding process temperature data and the relative crystallinity equation.

6. The method according to any one of claims 1 to 5, characterized in that, The assembly and finite element mesh generation of the digital unit model of the bolt structure are performed sequentially, including: The digital element model of the bolt structure is assembled with the digital element model of the nut structure, and then finite element meshing is performed using tetrahedral meshes; wherein the mesh size of the bolt region is smaller than the mesh size of the nut region; and / or, The step of solving the first strength constitutive model and the second strength constitutive model to obtain a damage contour map, and determining the tensile strength based on the damage contour map, includes: The damage evolution process of the fiber and matrix is ​​calculated in real time based on the first strength constitutive model and the second strength constitutive model, and the damage variable field is output. The tensile load-displacement curve is determined based on the damage variable field, and the peak load in the tensile load-displacement curve is extracted as the tensile strength.

7. A digital unit method composite material bolt strength prediction device, characterized in that, include: The acquisition module is used to acquire fiber orientation distribution characteristics based on electron micrographs of the composite material bolts; The first construction module is used to construct a digital element model of the bolt structure based on the fiber orientation distribution characteristics using the digital element method. The second construction module is used to establish the first strength constitutive model of the fiber in the composite bolt and the second strength constitutive model of the resin matrix, respectively. The simulation module is used to assemble and mesh the digital unit model of the bolt structure in sequence, then apply load conditions, and solve the model based on the first strength constitutive model and the second strength constitutive model to obtain a damage cloud map, and determine the tensile strength based on the damage cloud map.

8. A computing device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-6.

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