Method and device for acquiring geometric uncertainty of fabric composite yarn

Abstract

The invention discloses a method and a device for acquiring geometric uncertainty of fabric composite material yarns, and belongs to the field of composite material process mechanics. The method comprises the following steps: establishing a plurality of digital unit models of a target multi-layer fabric based on a distribution rule of interlayer phase differences of the target multi-layer fabric; wherein the interlayer phase differences of the plurality of digital unit models are different; performing compaction simulation on each digital unit model, and slicing the digital unit models subjected to compaction simulation to obtain a plurality of yarn section images so as to extract yarn geometric parameters; and establishing a multivariate Gaussian model, and fitting the multivariate Gaussian model by using the extracted yarn geometric parameters to obtain mixed probability density distribution of the yarn geometric parameters. According to the scheme, the labor cost and the time cost of composite material preparation, sampling and scanning can be reduced, the efficiency is improved, and the precision can be ensured.

Description

Technical Field

[0001] The invention relates to the technical field of composite material process mechanics, and in particular to a method and device for acquiring geometric uncertainty of yarn of a fabric composite material. Background Art

[0002] During the preparation of composite materials, the irregular deformation of the yarn is caused by the squeezing between fiber bundles. In order to achieve a more reliable and stable design, the uncertainty of the yarn geometric characteristics must be quantified.

[0003] To quantify the geometric uncertainty of composite yarns, it is first necessary to extract the yarn cross-sectional image. Micro-computed tomography (Micro-CT) is a common method for obtaining internal images of yarns. However, the Micro-CT method has obvious limitations: in order to obtain clear yarn cross-sectional images, Micro-CT scanning has strict requirements on the size of the sample, but the feature information extracted from small-sized samples is not widely representative. Therefore, increasing the number of Micro-CT experiments is the only way to solve this problem, but repeated Micro-CT experiments increase the time cost. Therefore, the traditional Micro-CT method requires a large number of experimental samples, and obtaining the uncertainty of yarn geometric features requires a high time cost, and efficiency and accuracy cannot be guaranteed.

[0004] Therefore, there is an urgent need to provide a new method for obtaining the geometric uncertainty of yarns in fabric composite materials. Summary of the invention

[0005] In order to solve the problems that the traditional Micro-CT method requires a large number of experimental samples, obtains the uncertainty data of yarn geometric characteristics, requires a high time cost, and has low efficiency and accuracy, the embodiments of the present invention provide a method and device for obtaining the geometric uncertainty of yarn of a fabric composite material.

[0006] In one aspect, a method for obtaining geometric uncertainty of yarn of a fabric composite material is provided, the method comprising:

[0007] Based on the distribution law of the interlayer phase difference of the target multilayer fabric, a plurality of digital unit models of the target multilayer fabric are established; wherein the interlayer phase differences of the plurality of digital unit models are different;

[0008] Performing compaction simulation on each of the digital unit models, and slicing the digital unit models after compaction simulation to obtain a number of yarn cross-sectional images, so as to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement;

[0009] A multivariate Gaussian model is established, and the extracted yarn geometric parameters are used to fit the multivariate Gaussian model to obtain a mixed probability density distribution of the yarn geometric parameters.

[0010] On the other hand, a device for obtaining geometric uncertainty of yarn of a fabric composite material is provided, which is used to implement the steps described in any method embodiment of the specification, and the device comprises:

[0011] A modeling unit, used for establishing a plurality of digital unit models of the target multi-layer fabric based on the distribution law of the inter-layer phase difference of the target multi-layer fabric; wherein the inter-layer phase differences of the plurality of digital unit models are different;

[0012] An extraction unit, used for performing compaction simulation on each of the digital unit models, and slicing the digital unit models after compaction simulation to obtain a plurality of yarn cross-sectional images, so as to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement;

[0013] The fitting unit is used to establish a multivariate Gaussian model and fit the multivariate Gaussian model using the extracted yarn geometric parameters to obtain a mixed probability density distribution of the yarn geometric parameters.

[0014] On the other hand, a computer device is provided, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of the above-mentioned method.

[0015] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0016] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0017] The technical solution provided by the present invention can at least bring the following beneficial effects:

[0018] The target multi-layer fabric is modeled based on the digital unit compaction simulation method to obtain a large number of yarn geometric parameters, and then the mixed probability density distribution of the yarn geometric parameters is fitted by the multivariate Gaussian model. This can not only reduce the labor and time costs of composite material preparation, sampling and scanning, improve efficiency, but also ensure accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of a method for obtaining geometric uncertainty of yarn of a fabric composite material provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of interlayer phase difference of a target multi-layer fabric provided by an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of a digital unit chain model provided by an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of generating a single-layer fabric model provided by an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of a digital unit model of a target multi-layer fabric provided by an embodiment of the present invention;

[0025] Figure 6 is a Gaussian distribution fitting result of a yarn height provided by an embodiment of the present invention;

[0026] Figure 7 It is a structural diagram of a device for obtaining geometric uncertainty of yarn of a fabric composite material provided by an embodiment of the present invention;

[0027] Figure 8 It is a hardware architecture diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] The specific implementation of the above concept is described below.

[0030] Please refer to Figure 1 , an embodiment of the present invention provides a method for obtaining geometric uncertainty of yarn of a fabric composite material, the method comprising:

[0031] Step 100: establishing a plurality of digital unit models of the target multi-layer fabric based on the distribution law of the inter-layer phase difference of the target multi-layer fabric; wherein the inter-layer phase differences of the plurality of digital unit models are different;

[0032] Step 102: performing compaction simulation on each digital unit model, and slicing the digital unit model after compaction simulation to obtain a number of yarn cross-sectional images to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement;

[0033] Step 104: Establish a multivariate Gaussian model, and use the extracted yarn geometric parameters to fit the multivariate Gaussian model to obtain a mixed probability density distribution of the yarn geometric parameters.

[0034] In an embodiment of the present invention, the target multi-layer fabric is modeled based on a digital unit compaction simulation method to obtain a large number of yarn geometric parameters, and then the mixed probability density distribution of the yarn geometric parameters is fitted by a multivariate Gaussian model. This can not only reduce the labor cost and time cost of composite material preparation, sampling and scanning, improve efficiency, but also ensure accuracy.

[0035] Described below Figure 1 How the various steps are performed.

[0036] For step 100:

[0037] In some embodiments, the distribution law of the interlayer phase difference of the target multi-layer fabric is obtained by:

[0038] Cutting a number of target single-layer fabrics of fixed size;

[0039] The cut target single-layer fabric is stacked layer by layer in a set number of groups to obtain a target multi-layer fabric;

[0040] Using a micro-computed tomography device, an internal slice image of the target multi-layer fabric is obtained to extract the inter-layer phase difference of the target multi-layer fabric and to calculate the distribution law of the inter-layer phase difference of the target multi-layer fabric.

[0041] In this embodiment, only a small amount of multi-layer fabrics need to be scanned by micro-computed tomography, the distribution law of the inter-layer phase difference of the target multi-layer fabric is statistically analyzed, and then digital unit modeling is performed based on the distribution law. Compared with the traditional Micro-CT method, which requires a large number of experimental samples to obtain the geometric characteristics of the yarn, this embodiment uses a small amount of experimental samples to obtain the distribution law and then modeling, which can greatly reduce the time cost.

[0042] In order to reduce the number of experimental samples and further improve efficiency, this embodiment sets the number to 10, and stacks 10 layers of target single-layer fabrics as a group, with the layering method uniformly set to 0° to obtain the target multi-layer fabric. The inter-layer phase difference can be referred to Figure 2 As shown, it can be understood that for a target multi-layer fabric with 10 layers, there are 9 inter-layer phase differences. If the number is set to be small, more multi-layer fabrics need to be made to accurately count the distribution law of inter-layer phase differences.

[0043] In some implementations, step 100 may include S1-S5:

[0044] S1, based on the distribution law of the inter-layer phase difference of the target multi-layer fabric, the warp digital unit chain phase and the weft digital unit chain phase are randomly selected respectively.

[0045] It can be understood that the warp digital unit chain phase and the weft digital unit chain phase are randomly selected based on the distribution law, and the warp phase and weft phase between each single-layer fabric model are random and different, which can simulate the diversity of the real inter-layer phase difference.

[0046] In this embodiment, the interlayer phase difference distribution law obtained by statistics of the selected carbon fiber plain weave fabric is a random number at (0, l), where l is the length of a single-layer fabric model, which can be obtained by scanning using a micro-computed tomography device.

[0047] S2, the phase of the warp digital unit chain and the phase of the weft digital unit chain are respectively substituted into the path curve equations of the warp digital unit chain and the weft digital unit chain to respectively establish the warp digital unit chain model and the weft digital unit chain model, and the warp digital unit chain model and the weft digital unit chain model are given size parameters and transverse shear stiffness.

[0048] In some embodiments, the path curve equations of the warp yarn digital unit chain and the weft yarn digital unit chain are:

[0049] z 1 =h×sin[2π / l×(xa)]

[0050] z 2 =h×sin[2π / l×(yb)]

[0051] In the formula, z 1 and z 2 are the path curve equations of the warp digital unit chain and the weft digital unit chain, respectively. h is the height of the fiber path vertex. l is the length of the single-layer fabric model, which is also the length of the sinusoidal function period. a is the phase of the warp digital unit chain, and b is the phase of the weft digital unit chain. a and b are used to characterize the phase changes of the fabric in two directions in the plane.

[0052] In this embodiment, reference can be made to Figure 3 The warp digital unit chain model and the weft digital unit chain model can be understood that the digital unit chain is composed of a plurality of beam units connected transversely, and the diameter, bending stiffness and transverse shear stiffness of the beam unit are the diameter, bending stiffness and transverse shear stiffness of the digital unit chain. In this embodiment, the length of each beam unit of the digital unit chain is 0.08 mm.

[0053] Since the diameter of the digital unit chain is significantly larger than the actual fiber diameter during modeling, the bending stiffness of the yarn in the fabric model is overestimated. This problem can be effectively avoided by using customized beam elements with reduced bending stiffness. By specifying the transverse shear stiffness of the beam element, the bending stiffness can be decoupled from the Young's modulus, thereby achieving the purpose of reducing the bending stiffness of the beam element without reducing the diameter of the digital unit chain.

[0054] Next, the calculation method of the transverse shear stiffness of the digital unit chain is explained in detail.

[0055] In some embodiments, the transverse shear stiffness of the warp yarn digital unit chain model and the weft yarn digital unit chain model is determined by the following steps B1-B3:

[0056] B1, based on the diameter of the actual carbon fiber of the target multilayer fabric, Young's modulus and the diameter of the digital unit chain model, the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit are calculated respectively; wherein the digital unit chain is composed of several sections of beam units connected in a chain manner, and the diameter of the beam unit is equal to the diameter of the digital unit chain model.

[0057] In this step, the bending stiffness of the beam element and the actual carbon fiber is expressed as:

[0058]

[0059] Where E is the Young's modulus of the actual carbon fiber, B vf is the bending stiffness of the beam element, B f is the bending stiffness of the actual carbon fiber, d vf is the diameter of the digital unit chain model, d f is the actual diameter of the carbon fiber.

[0060] B2, based on the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit, the bending stiffness of the actual yarn and the bending stiffness of the yarn model are calculated respectively, and the bending stiffness of the actual yarn and the bending stiffness of the yarn model are made equal; wherein the yarn model includes a warp model and a weft model.

[0061] In this step, the bending stiffness of the actual yarn can be approximated as the sum of the bending stiffness of the actual carbon fiber. Similarly, the bending stiffness of the yarn model can be approximated as the sum of the bending stiffness of the digital unit chain. The bending stiffness of the actual yarn and the bending stiffness of the yarn model can be expressed as:

[0062] N f B f =N vf B vf

[0063] Where N f and B f They represent the number of carbon fibers in the actual yarn and the bending stiffness of the carbon fibers, N vf is the number of digital unit chains in the yarn model, B vf is the bending stiffness of the beam element. In this embodiment, each yarn model is composed of 338 digital unit chains.

[0064] B3, the bending stiffness calculation formula of the actual carbon fiber, the bending stiffness calculation formula of the beam unit, the bending stiffness calculation formula of the actual yarn, the bending stiffness calculation formula of the yarn model and the transverse shear stiffness calculation formula are combined to obtain the transverse shear stiffness of the digital unit chain model.

[0065] In this step, the transverse shear stiffness is calculated as:

[0066] K=EA / (2+2υ)

[0067] Where E is the Young's modulus of the actual carbon fiber, A is the cross-sectional area of ​​the fiber, and υ is the Poisson's ratio of the carbon fiber.

[0068] Combining the calculation formulas of steps B1 and B2 with the above-mentioned calculation formula for transverse shear stiffness, the calculation formula for the transverse shear stiffness of the digital unit chain model can be obtained as follows:

[0069]

[0070] Where K is the transverse shear stiffness of the digital unit chain model, B f is the bending stiffness of the actual carbon fiber, d vf is the diameter of the digital unit chain model, d f is the actual carbon fiber diameter, and υ is the actual carbon fiber Poisson’s ratio.

[0071] S3, based on the fact that each bundle of yarn is composed of a set number of digital unit chains, the warp digital unit chain model and the weft digital unit chain model are arrayed respectively to obtain a warp model and a weft model, and then the warp model and the weft model are combined into a single-layer fabric model.

[0072] In this step, each bundle of yarn is composed of 338 digital unit chains. The warp digital unit chain model and the weft digital unit chain model are arrayed to obtain the warp model and the weft model, as shown in FIG. Figure 4 As shown. The yarn has an elliptical cross section, the initial width of the yarn is w, and the initial height is u. Continue to refer to Figure 4 , the warp yarn model and the weft yarn model are copied and combined to weave into a single-layer fabric model.

[0073] S4, repeating S1-S3 for several times to obtain several single-layer fabric models with different phases.

[0074] In this embodiment, since the warp digital unit chain phase and the weft digital unit chain phase are randomly selected in S1, the phase of the single-layer fabric model obtained each time is different, thereby obtaining a digital unit model of the target multi-layer fabric with diversified inter-layer phase differences.

[0075] S5, stacking a plurality of single-layer fabric models with different phases in groups of three layer by layer to obtain a plurality of digital unit models of target multi-layer fabrics with different inter-layer phase differences.

[0076] In this embodiment, reference can be made to Figure 5 , stacking the target multi-layer fabrics in groups of three can reduce the amount of calculation and avoid computer jams and crashes.

[0077] Regarding step 102:

[0078] In this step, continue to refer to Figure 5 , periodic boundary conditions were applied to the digital unit model, two rigid planes were used as compaction molds, displacement loads were applied to the model, and a total of 20 sets of random phase fabric compaction simulations were carried out. Equidistant parallel slices were made for the digital unit model after compaction simulation, and the slice images were perpendicular to the warp and weft directions with a spacing of 1.5 mm to extract the yarn geometric parameters from each slice image. The geometric parameters include yarn height, yarn width, and yarn center of mass lateral displacement.

[0079] Regarding step 104:

[0080] In this embodiment, a multivariate Gaussian model is established. For an input of x=[x1,…,xd], an output of y=[y1,…,yq], and a multivariate Gaussian random field with a constant mean, the best linear unbiased estimation prediction function is:

[0081] y=N q (β,c(x,x'))

[0082] Among them, N q represents a q-dimensional Gaussian process, β=[β 1 ,....,βq ] T is the mean vector of the characteristic parameters. c(x,x') represents the covariance of each geometric parameter at different locations, usually expressed as:

[0083]

[0084] Among them, ∑ represents a q×q positive definite matrix, which is used to capture the covariance and marginal variance between different outputs and can be expressed as:

[0085]

[0086] Here, σ represents the variance. is the roughness parameter vector that controls the smoothness of the random field, and d represents the spatial dimension. The hyperparameters β, ∑ and W of the multivariate Gaussian model are obtained by maximum likelihood estimation.

[0087] By fitting the multivariate Gaussian model using the yarn geometric parameters extracted in step 102, a mixed probability density distribution of the yarn geometric parameters can be obtained. Figure 6 Shown are the Gaussian distribution fitting results of yarn height.

[0088] Please refer to Figure 7 The embodiment of the present invention provides a device for obtaining geometric uncertainty of yarn of a fabric composite material, which is used to implement the steps of any method embodiment in the specification, and the device includes:

[0089] A modeling unit 701 is used to establish a plurality of digital unit models of the target multi-layer fabric based on the distribution law of the inter-layer phase difference of the target multi-layer fabric; wherein the inter-layer phase differences of the plurality of digital unit models are different;

[0090] The extraction unit 702 is used to perform compaction simulation on each digital unit model, and slice the digital unit model after compaction simulation to obtain a number of yarn cross-section images to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement;

[0091] The fitting unit 703 is used to establish a multivariate Gaussian model and fit the multivariate Gaussian model using the extracted yarn geometric parameters to obtain a mixed probability density distribution of the yarn geometric parameters.

[0092] In one embodiment of the present invention, the distribution law of the interlayer phase difference of the target multi-layer fabric in the modeling unit 701 is obtained in the following manner:

[0093] Cutting a number of target single-layer fabrics of fixed size;

[0094] The cut target single-layer fabric is stacked layer by layer in a set number of groups to obtain a target multi-layer fabric;

[0095] Using a micro-computed tomography device, an internal slice image of the target multi-layer fabric is obtained to extract the inter-layer phase difference of the target multi-layer fabric and to calculate the distribution law of the inter-layer phase difference of the target multi-layer fabric.

[0096] In one embodiment of the present invention, the modeling unit 701 is used to perform:

[0097] S1, based on the distribution law of the inter-layer phase difference of the target multi-layer fabric, the warp digital unit chain phase and the weft digital unit chain phase are randomly selected respectively;

[0098] S2, bringing the warp digital unit chain phase and the weft digital unit chain phase into the path curve equations of the warp digital unit chain and the weft digital unit chain respectively, so as to establish the warp digital unit chain model and the weft digital unit chain model respectively, and assigning size parameters and transverse shear stiffness to the warp digital unit chain model and the weft digital unit chain model;

[0099] S3, based on the fact that each bundle of yarn is composed of a set number of digital unit chains, the warp digital unit chain model and the weft digital unit chain model are arrayed to obtain a warp model and a weft model, and then the warp model and the weft model are combined into a single-layer fabric model;

[0100] S4, repeating S1-S3 for several times to obtain several single-layer fabric models with different phases;

[0101] S5, stacking a plurality of single-layer fabric models with different phases in groups of three layer by layer to obtain a plurality of digital unit models of target multi-layer fabrics with different inter-layer phase differences.

[0102] In one embodiment of the present invention, the path curve equations of the warp digital unit chain and the weft digital unit chain in the modeling unit 701 are:

[0103] z 1 =h×sin[2π / l×(xa)]

[0104] z 2 =h×sin[2π / l×(yb)]

[0105] In the formula, z 1 and z 2 are the path curve equations of the warp digital unit chain and the weft digital unit chain, respectively. h is the height of the fiber path vertex. l is the length of the single-layer fabric model, which is also the length of the sinusoidal function period. a is the phase of the warp digital unit chain, and b is the phase of the weft digital unit chain. a and b are used to characterize the phase changes of the fabric in two directions in the plane.

[0106] In one embodiment of the present invention, the transverse shear stiffness of the warp digital unit chain model and the weft digital unit chain model in the modeling unit 701 is determined in the following manner:

[0107] Based on the diameter of the actual carbon fiber of the target multilayer fabric, Young's modulus and the diameter of the digital unit chain model, the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit are calculated respectively; wherein the digital unit chain is composed of a number of beam units connected in a chain manner, and the diameter of the beam unit is equal to the diameter of the digital unit chain model;

[0108] Based on the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit, the bending stiffness of the actual yarn and the bending stiffness of the yarn model are calculated respectively, and the bending stiffness of the actual yarn and the bending stiffness of the yarn model are made equal; wherein the yarn model includes a warp yarn model and a weft yarn model;

[0109] The calculation formulas for the bending stiffness of the actual carbon fiber, the bending stiffness of the beam unit, the bending stiffness of the actual yarn, the bending stiffness of the yarn model and the transverse shear stiffness are combined to obtain the transverse shear stiffness of the digital unit chain model.

[0110] In one embodiment of the present invention, the calculation formula of the transverse shear stiffness of the digital unit chain model in the modeling unit 701 is:

[0111]

[0112] Where K is the transverse shear stiffness of the digital unit chain model, B f is the bending stiffness of the actual carbon fiber, d vf is the diameter of the digital unit chain model, d f is the actual carbon fiber diameter, and υ is the actual carbon fiber Poisson’s ratio.

[0113] It should be noted that the device for obtaining geometric uncertainty of yarn of a fabric composite material provided in the above embodiment is only illustrated by the division of the above functional units. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. In addition, the above device embodiment and the method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0114] The embodiment of the present application also provides a computer device, please refer to Figure 8The computer device includes a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the method for obtaining the geometric uncertainty of the yarn of the fabric composite material provided by the above-mentioned method embodiments.

[0115] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the method for obtaining the geometric uncertainty of yarn of a fabric composite material provided in the above-mentioned method embodiments.

[0116] An embodiment of the present application also provides 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 the processor executes the computer program, so that the computer device executes any method for obtaining the geometric uncertainty of yarn of a fabric composite material in the above-mentioned embodiments.

[0117] For the convenience of description, the above system or device is described as being divided into various modules or units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0118] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present application or certain parts of the embodiments.

[0119] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0120] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for obtaining geometric uncertainty of yarn of a fabric composite material, characterized in that: The method comprises: Based on the distribution law of the interlayer phase difference of the target multilayer fabric, a plurality of digital unit models of the target multilayer fabric are established; wherein the interlayer phase differences of the plurality of digital unit models are different; Performing compaction simulation on each of the digital unit models, and slicing the digital unit models after compaction simulation to obtain a number of yarn cross-sectional images, so as to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement; A multivariate Gaussian model is established, and the extracted yarn geometric parameters are used to fit the multivariate Gaussian model to obtain a mixed probability density distribution of the yarn geometric parameters.

2. The method according to claim 1, characterized in that The distribution law of the interlayer phase difference of the target multi-layer fabric is obtained by the following method: Cutting a number of target single-layer fabrics of fixed size; The target single-layer fabric obtained by cutting is stacked layer by layer in a set number as a group to obtain a target multi-layer fabric; Using a microcomputer tomography device, an internal slice image of the target multi-layer fabric is obtained to extract the inter-layer phase difference of the target multi-layer fabric, so as to calculate the distribution law of the inter-layer phase difference of the target multi-layer fabric.

3. The method according to claim 1, characterized in that The method of establishing a plurality of digital unit models of the target multi-layer fabric based on the distribution law of the inter-layer phase difference of the target multi-layer fabric comprises: S1, based on the distribution law of the inter-layer phase difference of the target multi-layer fabric, the warp digital unit chain phase and the weft digital unit chain phase are randomly selected respectively; S2, bringing the warp digital unit chain phase and the weft digital unit chain phase into the path curve equations of the warp digital unit chain and the weft digital unit chain respectively, so as to establish a warp digital unit chain model and a weft digital unit chain model respectively, and assigning size parameters and transverse shear stiffness to the warp digital unit chain model and the weft digital unit chain model; S3, based on the fact that each bundle of yarn is composed of a set number of digital unit chains, the warp digital unit chain model and the weft digital unit chain model are arrayed to obtain a warp model and a weft model, and then the warp model and the weft model are combined into a single-layer fabric model; S4, repeating S1-S3 for several times to obtain several single-layer fabric models with different phases; S5, stacking a plurality of single-layer fabric models with different phases in groups of three layer by layer to obtain a plurality of digital unit models of the target multi-layer fabric with different inter-layer phase differences.

4. The method according to claim 3, characterized in that The path curve equations of the warp yarn digital unit chain and the weft yarn digital unit chain are: z1=h×sin[2π / l×(xa)] z2=h×sin[2π / l×(yb)] Wherein, z1 and z2 are the path curve equations of the warp digital unit chain and the weft digital unit chain, respectively, h is the height of the fiber path vertex, l is the length of the single-layer fabric model, which is also the length of the sine function period, a is the phase of the warp digital unit chain, b is the phase of the weft digital unit chain, and a and b are used to characterize the phase changes of the fabric in two directions in the plane.

5. The method according to claim 3, characterized in that: The transverse shear stiffness of the warp digital unit chain model and the weft digital unit chain model is determined as follows: Based on the diameter of the actual carbon fiber of the target multilayer fabric, the Young's modulus and the diameter of the digital unit chain model, respectively calculating the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit; wherein the digital unit chain is composed of a plurality of sections of beam units connected in a chain manner, and the diameter of the beam unit is equal to the diameter of the digital unit chain model; Based on the bending stiffness of the actual carbon fiber and the bending stiffness of the beam unit, the bending stiffness of the actual yarn and the bending stiffness of the yarn model are calculated respectively, and the bending stiffness of the actual yarn and the bending stiffness of the yarn model are made equal; wherein the yarn model includes a warp yarn model and a weft yarn model; The calculation formulas for the bending stiffness of the actual carbon fiber, the bending stiffness of the beam unit, the bending stiffness of the actual yarn, the bending stiffness of the yarn model and the transverse shear stiffness are combined to obtain the transverse shear stiffness of the digital unit chain model.

6. The method according to claim 5, characterized in that The calculation formula of the transverse shear stiffness of the digital unit chain model is: Where K is the transverse shear stiffness of the digital unit chain model, B f is the bending stiffness of the actual carbon fiber, d vf is the diameter of the digital unit chain model, d f is the actual carbon fiber diameter, and υ is the actual carbon fiber Poisson’s ratio.

7. A device for obtaining geometric uncertainty of yarn of a fabric composite material, used to implement the steps of any of the methods described in claims 1 to 6, characterized in that: The device comprises: A modeling unit, used for establishing a plurality of digital unit models of the target multi-layer fabric based on the distribution law of the inter-layer phase difference of the target multi-layer fabric; wherein the inter-layer phase differences of the plurality of digital unit models are different; An extraction unit, used for performing compaction simulation on each of the digital unit models, and slicing the digital unit models after compaction simulation to obtain a plurality of yarn cross-sectional images, so as to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn mass center lateral displacement; The fitting unit is used to establish a multivariate Gaussian model and fit the multivariate Gaussian model using the extracted yarn geometric parameters to obtain a mixed probability density distribution of the yarn geometric parameters.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the steps of any one of the methods described in claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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