Method and device for obtaining the geometric uncertainty of a textile composite yarn

Abstract

The application discloses a kind of acquisition method and device of fabric composite material yarn geometry uncertainty, belong to composite material process mechanics field.Method includes: based on the distribution law of interlaminar phase difference of target multilayer fabric, establishes several digital unit models of target multilayer fabric;Wherein, the interlaminar phase difference of several digital unit models is different;Each digital unit model is respectively compacted simulation, and the digital unit model after compacted simulation is sliced to obtain several yarn cross-sectional images, to extract yarn geometry parameter;Establish multivariate Gaussian model, and utilize the extracted yarn geometry parameter to fit multivariate Gaussian model, obtain the mixed probability density distribution of yarn geometry parameter.This scheme not only can reduce the human cost and time cost of composite material preparation, sampling and scanning, improve efficiency, can also guarantee accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of composite material process mechanics, and particularly relates to a method and device for obtaining yarn geometric uncertainty of a fabric composite material. BACKGROUND

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

[0003] Quantification of yarn geometric uncertainty of a composite material first requires extraction of a yarn cross-section image. Micro-CT (Micro-CT) is a common method for obtaining internal images of yarns. However, the Micro-CT method has obvious limitations: in order to obtain a clear yarn cross-section image, the Micro-CT scanning has strict requirements for the size of the sample, but the feature information extracted from a small-size sample does not have wide representativeness, so increasing the number of Micro-CT experiments is the only method 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 characteristics requires a high time cost, which cannot guarantee efficiency and accuracy.

[0004] Therefore, there is an urgent need to provide a new method for obtaining yarn geometric uncertainty of a fabric composite material. SUMMARY

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

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

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

[0008] Each of the digital unit models is subjected to compaction simulation, and a plurality of yarn cross-section images are obtained by slicing the digital unit models after compaction simulation to extract yarn geometric parameters; wherein the yarn geometric parameters at least include yarn width, yarn height and yarn centroid transverse shift;

[0009] A multi-Gaussian model is established, and the multi-Gaussian model is fitted by using the extracted yarn geometric parameters to obtain the mixed probability density distribution of the yarn geometric parameters.

[0010] In another aspect, a device for obtaining yarn geometric uncertainty of a fabric composite material is provided for implementing the steps of any method embodiment described in the specification, and the device comprises:

[0011] A modeling unit is configured to establish a plurality of digital unit models of a target multi-layer fabric based on a distribution rule of interlayer phase difference of the target multi-layer fabric, wherein the interlayer phase difference of the plurality of digital unit models is different.

[0012] An extraction unit is configured to perform compaction simulation on each digital unit model respectively, and to obtain a plurality of yarn cross-section images by slicing the digital unit model after compaction simulation to extract yarn geometric parameters, wherein the yarn geometric parameters at least include yarn width, yarn height and yarn centroid transverse shift.

[0013] A fitting unit is configured to establish a multi-Gaussian model, and to fit the multi-Gaussian model by using the extracted yarn geometric parameters to obtain the mixed probability density distribution of the yarn geometric parameters.

[0014] In another aspect, a computer device is provided, which comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to implement the steps of the above-mentioned method.

[0015] In another aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned method.

[0016] In another aspect, a computer program product is provided, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned method.

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

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

[0019] 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.

[0020] Figure 1 This is a flowchart of a method for obtaining the geometric uncertainty of yarn in a fabric composite material according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the interlayer phase difference of a target multilayer fabric provided in an embodiment of the present invention;

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

[0023] Figure 4 This is a schematic diagram illustrating the generation of a single-layer fabric model according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of a digital unit model of a target multilayer fabric provided in an embodiment of the present invention;

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

[0026] Figure 7 This is a structural diagram of a device for obtaining the geometric uncertainty of fabric composite yarn according to an embodiment of the present invention;

[0027] Figure 8 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0028] 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.

[0029] The following describes the specific implementation of the above concept.

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

[0031] Step 100: Based on the distribution law of the interlayer phase difference of the target multilayer fabric, establish several digital unit models of the target multilayer fabric; wherein the interlayer phase difference of the several digital unit models is different;

[0032] Step 102: Perform compaction simulation on each digital unit model, and slice the compacted digital unit model to obtain several yarn cross-sectional images to extract yarn geometric parameters; wherein, the yarn geometric parameters include at least yarn width, yarn height and yarn centroid lateral displacement;

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

[0034] In this embodiment of the invention, the target multilayer fabric is modeled based on the digital unit compaction simulation method to obtain a large number of yarn geometric parameters. Then, the mixed probability density distribution of the yarn geometric parameters is fitted by a multivariate Gaussian model. This not only reduces the labor and time costs of composite material preparation, sampling and scanning, and improves efficiency, but also ensures accuracy.

[0035] The following description Figure 1 The execution method for each step is shown.

[0036] For step 100:

[0037] In some implementations, the distribution pattern of the interlayer phase difference of the target multilayer fabric is obtained in the following manner:

[0038] Cut several target single-layer fabrics of fixed size;

[0039] The cut single-layer fabric is stacked layer by layer in a set quantity to obtain the target multi-layer fabric.

[0040] Using a microcomputed tomography (CT) device, internal slice images of the target multilayer fabric are acquired to extract the interlayer phase difference of the target multilayer fabric and to statistically analyze the distribution pattern of the interlayer phase difference of the target multilayer fabric.

[0041] In this embodiment, only a small number of multilayer fabrics need to be scanned using micro-computed tomography (MCT) to statistically analyze the distribution pattern of the interlayer phase difference in the target multilayer fabric. Then, digital cell modeling is performed based on this distribution pattern. Compared to the traditional Micro-CT method, which requires a large number of experimental samples to obtain yarn geometric features, this embodiment utilizes a small number of experimental samples to obtain the distribution pattern before modeling, which can greatly reduce time costs.

[0042] To reduce the number of experimental samples and further improve efficiency, this embodiment sets the quantity to 10, stacking 10 layers of the target single-layer fabric as a group, with a uniform layup angle of 0°, to obtain the target multi-layer fabric. The interlayer phase difference can be referenced... Figure 2 As shown, a target multilayer fabric with 10 layers can be understood to have 9 interlayer phase differences. If the number of layers is set to be small, more multilayer fabrics need to be made to accurately statistically analyze the distribution pattern of the interlayer phase differences.

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

[0044] S1, based on the distribution law of the interlayer phase difference of the target multilayer fabric, randomly select the phase of the warp digital unit chain and the phase of the weft digital unit chain respectively.

[0045] It is understandable that by randomly selecting the warp and weft digital unit chain phases based on the distribution pattern, the warp and weft phases between each single-layer fabric model are random and different, which can simulate the diversity of real interlayer phase differences.

[0046] In this embodiment, the interlayer phase difference distribution pattern obtained from the statistical analysis of the selected carbon fiber plain weave fabric is a random number in (0, l), where l is the length of a single-layer fabric model, which can be obtained by scanning with a microcomputer computed tomography (CT) device.

[0047] S2, substitute the phases of the warp digital unit chain and the weft digital unit chain into the path curve equations of the warp digital unit chain and the weft digital unit chain respectively, to establish the warp digital unit chain model and the weft digital unit chain model respectively, and assign size parameters and transverse shear stiffness to the warp digital unit chain model and the weft digital unit chain model respectively.

[0048] In some implementations, the path curve equations for the warp digital unit chain and the weft digital unit chain are as follows:

[0049] z1=h×sin[2π / l×(xa)]

[0050] z² = h × sin[2π / l × (yb)]

[0051] In the formula, 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 period of the sine function; 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 change of the fabric in two directions in the plane.

[0052] In this embodiment, reference can be made to Figure 3The warp and weft digital unit chain models can be understood as follows: the digital unit chain is composed of several beam units spliced ​​laterally. The diameter, bending stiffness, and lateral shear stiffness of the beam units are the same as the diameter, bending stiffness, and lateral shear stiffness of the digital unit chain. In this embodiment, the length of each beam unit in the digital unit chain is 0.08 mm.

[0053] Because 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 custom beam elements that reduce bending stiffness. By specifying the transverse shear stiffness of the beam element, the bending stiffness and Young's modulus can be decoupled, thereby reducing the bending stiffness of the beam element without reducing the diameter of the digital unit chain.

[0054] Next, the calculation method for the lateral shear stiffness of the digital unit chain will be explained in detail.

[0055] In some implementations, the transverse shear stiffness of the warp digital unit chain model and the weft digital unit chain model is determined as shown in steps B1-B3 below:

[0056] B1. Based on the actual carbon fiber diameter, Young's modulus, and the diameter of the digital unit chain model of the target multilayer fabric, calculate the bending stiffness of the actual carbon fiber and the bending stiffness of the beam element, respectively. The digital unit chain is composed of several beam element segments connected in a chain, and the diameter of the beam element 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] In the formula, E is the Young's modulus of the actual carbon fiber, and B... vf B is the bending stiffness of the beam element. f d represents the bending stiffness of actual carbon fiber. vf Let d be the diameter of the digital unit chain model. f This represents 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 element, calculate the bending stiffness of the actual yarn and the bending stiffness of the yarn model respectively, and make the bending stiffness of the actual yarn and the bending stiffness of the yarn model equal; wherein, the yarn model includes the warp model and the weft model.

[0061] In this step, the bending stiffness of the actual yarn can be approximated as the sum of the bending stiffnesses of the actual carbon fibers. Similarly, the bending stiffness of the yarn model can be approximated as the sum of the bending stiffnesses of the digital unit chains. 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] In the formula, N f and B f N represents the number of carbon fibers in the actual yarn and the bending stiffness of the carbon fibers, respectively. vf B represents the number of digital unit chains in the yarn model. vf This represents the bending stiffness of the beam element. In this embodiment, each yarn bundle model consists of 338 digital unit chains.

[0064] B3. By combining the calculation formulas for the bending stiffness of actual carbon fibers, the bending stiffness of beam elements, the bending stiffness of actual yarns, the bending stiffness of yarn models, and the transverse shear stiffness, the transverse shear stiffness of the digital unit chain model is obtained.

[0065] In this step, the formula for calculating the transverse shear stiffness is:

[0066] K = EA / (2 + 2υ)

[0067] In the formula, 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 in steps B1 and B2 with the above formula for calculating transverse shear stiffness, we can obtain the formula for calculating the transverse shear stiffness of the digital element chain model as follows:

[0069]

[0070] In the formula, K is the transverse shear stiffness of the digital unit chain model, and B... f d represents the bending stiffness of actual carbon fiber. vf Let d be the diameter of the digital unit chain model. f υ represents the actual diameter of the carbon fiber, and υ represents the Poisson's ratio of the actual carbon fiber.

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

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

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

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

[0075] S5. Several single-layer fabric models with different phases are stacked layer by layer in groups of three to obtain several digital unit models of the target multi-layer fabric with different inter-layer phase differences.

[0076] In this embodiment, reference can be made to Figure 5 Stacking multiple layers of fabric in groups of three can reduce computational load and prevent computer 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, and 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 conducted. The compacted digital unit model was then sliced ​​at equal intervals, with the slice images perpendicular to both the warp and weft directions and spaced 1.5 mm apart, to extract yarn geometric parameters from each slice image. These geometric parameters included yarn height, yarn width, and yarn centroid lateral displacement.

[0079] Regarding step 104:

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

[0081] y = N q (β,c(x,x'))

[0082] Where, N q Let β represent a q-dimensional Gaussian process, where β = [β1,...,β2]. q ]T It is the mean vector of the feature parameters. c(x,x') represents the covariance of each geometric parameter at different locations, usually expressed as:

[0083]

[0084] Where ∑ represents a 1 q×q positive definite matrix used to capture the covariance and marginal variance between different outputs, and can be expressed as:

[0085]

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

[0087] By fitting the above multivariate Gaussian model with the yarn geometric parameters extracted in step 102, the mixed probability density distribution of the yarn geometric parameters can be obtained. For example... Figure 6 The figure shows the Gaussian distribution fitting results for the yarn height.

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

[0089] Modeling unit 701 is used to establish several digital unit models of the target multilayer fabric based on the distribution law of the interlayer phase difference of the target multilayer fabric; wherein the interlayer phase difference of the several digital unit models is different;

[0090] Extraction unit 702 is used to perform compaction simulation on each digital unit model and slice the compacted digital unit model to obtain several yarn cross-sectional images to extract yarn geometric parameters; wherein, the yarn geometric parameters include at least yarn width, yarn height and yarn centroid lateral displacement;

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

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

[0093] Cut several target single-layer fabrics of fixed size;

[0094] The cut single-layer fabric is stacked layer by layer in a set quantity to obtain the target multi-layer fabric.

[0095] Using a microcomputed tomography (CT) device, internal slice images of the target multilayer fabric are acquired to extract the interlayer phase difference of the target multilayer fabric and to statistically analyze the distribution pattern of the interlayer phase difference of the target multilayer 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 interlayer phase difference of the target multilayer fabric, randomly select the phase of the warp digital unit chain and the phase of the weft digital unit chain respectively;

[0098] S2, substitute the phase of the warp digital unit chain and the phase of the weft digital unit chain 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 assign 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 the warp model and the weft model, and then the warp model and the weft model are combined into a single-layer fabric model.

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

[0101] S5. Several single-layer fabric models with different phases are stacked layer by layer in groups of three to obtain several digital unit models of the target multi-layer fabric 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 as follows:

[0103] z1=h×sin[2π / l×(xa)]

[0104] z² = h × sin[2π / l × (yb)]

[0105] In the formula, 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 period of the sine function; 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 change 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 actual carbon fiber diameter, Young's modulus, and the diameter of the digital unit chain model of the target multilayer fabric, the bending stiffness of the actual carbon fiber and the bending stiffness of the beam element are calculated respectively; wherein, the digital unit chain is composed of several beam element segments connected in a chain, and the diameter of the beam element 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 element, 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 the warp model and the weft model.

[0109] By combining the formulas for calculating the bending stiffness of actual carbon fibers, beam elements, actual yarns, yarn models, and transverse shear stiffness, the transverse shear stiffness of the digital unit chain model is obtained.

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

[0111]

[0112] In the formula, K is the transverse shear stiffness of the digital unit chain model, and B... f d represents the bending stiffness of actual carbon fiber. vf Let d be the diameter of the digital unit chain model. f υ represents the actual diameter of the carbon fiber, and υ represents the Poisson's ratio of the actual carbon fiber.

[0113] It should be noted that the device for obtaining the geometric uncertainty of fabric composite yarns provided in the above embodiments is only an example of 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 embodiments and method embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0114] Embodiments of this application also provide a computer device, please refer to... Figure 8 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the method for obtaining the geometric uncertainty of fabric composite yarn provided in the above method embodiments.

[0115] The embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein 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 fabric composite yarn provided in the above-described method embodiments.

[0116] 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 any of the methods for obtaining the geometric uncertainty of fabric composite yarn in the above embodiments.

[0117] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0118] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of the embodiments of this application.

[0119] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used merely 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 the element.

[0120] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for obtaining the geometric uncertainty of yarn in a fabric composite material, characterized in that, The method includes: Based on the distribution pattern of the interlayer phase difference of the target multilayer fabric, several digital unit models of the target multilayer fabric are established; wherein the interlayer phase difference of the several digital unit models is different. Each of the digital unit models is subjected to a compaction simulation, and the compacted digital unit models are sliced ​​to obtain several yarn cross-sectional images to extract yarn geometric parameters; wherein, the yarn geometric parameters include at least yarn width, yarn height and yarn centroid lateral displacement; A multivariate Gaussian model is established, and the extracted yarn geometric parameters are used to fit the multivariate Gaussian model to obtain the mixed probability density distribution of the yarn geometric parameters. Based on the distribution law of the interlayer phase difference of the target multilayer fabric, several digital unit models of the target multilayer fabric are established, including: S1, based on the distribution law of the interlayer phase difference of the target multilayer fabric, randomly select the phase of the warp digital unit chain and the phase of the weft digital unit chain respectively; S2, Substitute the phase of the warp digital unit chain and the phase of the weft digital unit chain 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 assign 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 respectively to obtain the warp model and the weft model, and then the warp model and the weft model are combined into a single-layer fabric model. S4, repeat S1-S3 several times to obtain several single-layer fabric models with different phases; S5, stack several single-layer fabric models with different phases in groups of three to obtain several digital unit models of the target multilayer fabric with different interlayer phase differences.

2. The method as described in claim 1, characterized in that, The distribution pattern of the interlayer phase difference of the target multilayer fabric was obtained in the following way: Cut several target single-layer fabrics of fixed size; The cut target single-layer fabric is stacked layer by layer in a set quantity to obtain the target multi-layer fabric. Using a microcomputed tomography (CT) device, internal slice images of the target multilayer fabric are acquired to extract the interlayer phase difference of the target multilayer fabric, and to statistically analyze the distribution pattern of the interlayer phase difference of the target multilayer fabric.

3. The method according to claim 1, characterized in that, The path curve equations for the warp digital unit chain and the weft digital unit chain are as follows: z1=h×sin[2π / l×(xa)] z² = h × sin[2π / l × (yb)] In the formula, 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 period of the sine function; 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 change of the fabric in two directions in the plane.

4. The method according to claim 1, characterized in that, The transverse shear stiffness of the warp digital unit chain model and the weft digital unit chain model is determined in the following manner: Based on the actual carbon fiber diameter, Young's modulus, and the diameter of the digital unit chain model of the target multilayer fabric, the bending stiffness of the actual carbon fiber and the bending stiffness of the beam element are calculated respectively; wherein, the digital unit chain is composed of several beam element segments connected in a chain, and the diameter of the beam element 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 element, 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. By combining the formulas for calculating the bending stiffness of actual carbon fibers, beam elements, actual yarns, yarn models, and transverse shear stiffness, the transverse shear stiffness of the digital unit chain model is obtained.

5. The method according to claim 4, characterized in that, The formula for calculating the transverse shear stiffness of the digital unit chain model is as follows: In the formula, K is the transverse shear stiffness of the digital unit chain model, and B... f d represents the bending stiffness of actual carbon fiber. vf Let d be the diameter of the digital unit chain model. f υ represents the actual diameter of the carbon fiber, and υ represents the Poisson's ratio of the actual carbon fiber.

6. A device for obtaining the geometric uncertainty of yarn in a fabric composite material, used to implement the steps of the method described in any one of claims 1-5, characterized in that, The device includes: A modeling unit is used to establish several digital unit models of the target multilayer fabric based on the distribution pattern of the interlayer phase difference; wherein the interlayer phase difference of the several digital unit models is different; An extraction unit is used to perform compaction simulation on each of the digital unit models, and to slice the compacted digital unit models to obtain several yarn cross-sectional images in order to extract yarn geometric parameters; wherein, the yarn geometric parameters include at least yarn width, yarn height and yarn centroid lateral displacement; The fitting unit is used to establish a multivariate Gaussian model and fit the multivariate Gaussian model with the extracted yarn geometric parameters to obtain the mixed probability density distribution of the yarn geometric parameters.

7. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-5.

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