A simulation method for in-layer of composite materials based on microscopic observation

Abstract

A simulation method for in - layer of composite materials based on microscopic observation. The present invention relates to a simulation method for in - layer of composite materials. To solve the problem that existing simulation methods cannot accurately simulate various damage types that occur in reality, resulting in misunderstandings of material properties and inaccurate predictions, the steps of the present invention are as follows: First, conduct a tensile experiment on 0° unidirectional tape; Second, conduct a tensile experiment on 90° unidirectional tape; Third, establish a mesoscopic - scale unit cell simulation model of fabric composite materials based on fiber bundles and matrix, conduct simulation calculations, and extract the number of damaged units in three directions of in - layer fiber bundles and the number of damaged units of the matrix; Fourth, classify the in - layer damage in the simulation as matrix damage, interface damage, and fiber damage according to the damage ratios calculated in the first and second steps; Fifth, conduct microscopic observation on the damaged cross - section of fabric composite materials and calculate the ratios of the three damage modes. The present invention belongs to the technical field of simulation measurement.

Description

Technical Field

[0001] The present invention relates to a method for simulating within a composite material layer, and more particularly to a method for simulating within a composite material layer based on microscopic observation. The present invention belongs to the technical field of simulation measurement. Background Art

[0002] Composite materials, due to their excellent strength and lightweight characteristics, have become indispensable materials in modern industrial design. These materials enhance the overall structural strength and durability by embedding fibers or other reinforcing materials into the matrix material. Widely used in the fields of aviation, automotive, aerospace, and high-performance engineering, the design and application requirements of composite materials have strict demands for understanding their damage behavior. Therefore, accurately predicting and analyzing damage modes in composite materials is crucial for ensuring their performance and safety.

[0003] The in-layer mesoscopic simulation method is an important tool for analyzing the structure and properties of composite materials. These methods typically evaluate the overall behavior of the material by simulating the interaction between fiber bundles and the matrix. In the existing simulation framework, the directional damage of fiber bundles is mainly classified as fiber damage, while the directional damage perpendicular to the fiber bundles is considered matrix damage. This classification method simplifies the analysis of damage modes and makes the simulation process more efficient.

[0004] Although the existing in-layer mesoscopic simulation methods play a key role in composite material analysis, they have obvious limitations in damage mode classification. In particular, this method ignores matrix damage and interface damage between fibers and the matrix in one-directional damage, while ignoring fiber damage and interface damage in two-directional damage.

[0005] In view of these deficiencies of the prior art, the present invention proposes a new method for simulating within a composite material layer based on microscopic observation. Summary of the Invention

[0006] In order to solve the problem that the existing simulation methods cannot accurately simulate various damage types that occur in practice, resulting in misunderstandings of material properties and inaccurate predictions, the present invention further proposes a method for simulating within a composite material layer based on microscopic observation.

[0007] The technical solution adopted by the present invention to solve the above problems is:

[0008] The present invention includes:

[0009] A tensile experiment is conducted on the 0° unidirectional tape. By observing the cross-section through a microscope, matrix damage, interface damage, and fiber damage are distinguished. At the same time, machine learning methods are used to train typical microscopic damage pattern pictures. According to the areas of various damage patterns in the cross-section, the proportions of different damage patterns are calculated, and the mean value is taken for multiple groups of data. Furthermore, the damage of the 0° unidirectional tape can be divided into matrix damage, interface damage, and fiber damage, which are composed of three damages in a certain proportion. This proportion is the microscopic damage composition of the one-direction damage of the fiber bundle. Based on this method, tensile experiments are carried out on 0° unidirectional tapes with different fiber volume fractions to obtain the relationship between the damage proportion and the fiber volume fraction.

[0010] A tensile experiment is conducted on the 90° unidirectional tape. By observing the cross-section through a microscope, matrix damage, interface damage, and fiber damage are distinguished. At the same time, machine learning methods are used to train typical microscopic damage pattern pictures. According to the areas of various damage patterns in the cross-section, the proportions of different damage patterns are calculated, and the mean value is taken for multiple groups of data. Furthermore, the damage of the 90° unidirectional tape can be divided into matrix damage, interface damage, and fiber damage, which are composed of three damages in a certain proportion. This proportion is the microscopic damage composition of the two-direction and three-direction damages of the fiber bundle. Based on this method, tensile experiments are carried out on 90° unidirectional tapes with different fiber volume fractions to obtain the relationship between the damage proportion and the fiber volume fraction.

[0011] A single-layer fabric tensile experiment is designed. By observing the cross-section through a microscope, matrix damage, interface damage, and fiber damage are distinguished. At the same time, machine learning methods are used to train typical microscopic damage pattern pictures. According to the areas of various damage patterns in the cross-section, the proportions of different damage patterns are calculated, and the mean value is taken for multiple groups of data. Furthermore, the fabric tensile damage can be divided into matrix damage, interface damage, and fiber damage, which are composed of three damages in a certain proportion.

[0012] The fiber volume fraction of the fiber bundle in the fabric is analyzed and calculated. According to the relationship between the damage proportion and the fiber volume fraction in the 0° unidirectional tape and the 90° unidirectional tape, the proportion input parameters in the simulation are determined.

[0013] A mesoscopic simulation model of the in-plane of the fabric is established. According to the proportion of each damage pattern, combined with the simulation results, the one-direction damage quantity, two-direction damage quantity, three-direction damage quantity, and matrix damage quantity of the fiber bundle are extracted. All in-plane damage types can be classified into matrix damage, interface damage, and fiber damage. Furthermore, the simulation results are verified through the fabric tensile experiment.

[0014] The beneficial effects of the present invention are as follows:

[0015] 1. In current simulations, the one-direction damage of the fiber bundle is usually considered as fiber damage. However, in fact, the one-direction damage of the fiber bundle also includes matrix damage and interface damage. The method proposed by the present invention can subdivide the one-direction damage mode of the fiber bundle and accurately verify the effectiveness of the simulation results in combination with machine learning.

[0016] 2. The damage in the second and third directions of the fiber bundle is usually considered as matrix damage. However, the damage in the second and third directions of the actual fiber bundle also includes interface damage and a small amount of fiber damage. The method proposed in the present invention can subdivide the damage modes in the second and third directions of the fiber bundle and accurately verify the effectiveness of the simulation results by combining machine learning.

[0017] 3. The present invention first identifies typical fiber damage, interface damage, and matrix damage modes through a machine learning model. Then, through tensile experiments on 0° and 90° unidirectional tapes and combining the machine learning model for damage analysis and classification, the proportion of different damage modes is calculated. This method not only improves the accuracy of damage classification but also provides more accurate data support for the design and application of composite materials, thus greatly enhancing the practicability and reliability of the mesoscopic simulation model of composite materials. Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the working process of the present invention. Detailed Embodiments

[0019] Detailed Embodiment 1: Combined with Figure 1 To illustrate this embodiment, a method for in - layer simulation of composite materials based on microscopic observation includes the following steps:

[0020] Step 1: Conduct a tensile experiment on a 0° unidirectional tape and calculate the proportion of three damage modes;

[0021] Step 2: Conduct a tensile experiment on a 90° unidirectional tape and calculate the proportion of three damage modes;

[0022] Step 3: Establish a mesoscopic - scale unit cell simulation model of the fabric composite material based on fiber bundles and matrix, conduct simulation calculations, and extract the number of damaged elements in three directions of the in - layer fiber bundles and the number of damaged elements of the matrix;

[0023] Step 4: According to the damage proportions calculated in Steps 1 and 2, the tensile damage of the 0° unidirectional tape represents the damage in the first direction of the fiber bundle, and the tensile damage of the 90° unidirectional tape represents the damage in the second and third directions of the fiber bundle. Classify the in - layer damage in the simulation into matrix damage, interface damage, and fiber damage;

[0024] Step 5: Conduct microscopic observation on the damaged cross - section of the fabric composite material and calculate the proportion of three damage modes. This proportion can effectively verify the accuracy of the simulation model.

[0025] In Step 1 of this embodiment, by combining experimental and simulation methods, the true damage mode composition in the first direction of the fiber bundle in the simulation is clarified;

[0026] In the second step of this embodiment, by combining experimental and simulation methods, the true damage mode composition in the second and third directions of the fiber bundle in the simulation was clarified;

[0027] In the fourth step of this embodiment, by combining experimental and simulation methods, the true damage mode composition within the layer in the simulation was clarified.

[0028] Specific Embodiment 2: Combining Figure 1 To illustrate this embodiment, the first step of this embodiment includes: taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find three typical microscopic damage mode pictures, use machine learning methods for training, identify the three damage modes of the 0° unidirectional tape fracture surface according to the trained model, project the damage area corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes;

[0029]

[0030] In the formula, p is the damage proportion.

[0031] The other components and connection relationships of this embodiment are the same as those of Specific Embodiment 1.

[0032] Specific Embodiment 3: Combining Figure 1 To illustrate this embodiment, the second step of this embodiment includes: taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find three typical microscopic damage mode pictures, use machine learning methods for training, identify the three damage modes of the 90° unidirectional tape fracture surface according to the trained model, project the damage area corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes.

[0033] The other components and connection relationships of this embodiment are the same as those of Specific Embodiment 1 or 2.

[0034] Specific Embodiment 4: Combining Figure 1 To illustrate this embodiment, the fifth step of this embodiment includes: taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find three typical microscopic damage mode pictures, use machine learning methods for training, identify the three damage modes of the damage cross-section of the fabric composite material according to the trained model, project the damage area corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes.

[0035] The other components and connection relationships of this embodiment are the same as those of Specific Embodiment 1, 2, or 3.

[0036] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the above-disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent replacement, and improvement made to the above embodiments within the spirit and principle of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A simulation method for in - layer of composite materials based on microscopic observation, characterized in that: The method includes the following steps: Step 1: Conduct a tensile experiment on the 0° unidirectional tape and calculate the proportion of the three damage modes; Step 2: Conduct a tensile experiment on the 90° unidirectional tape and calculate the proportion of the three damage modes; Step 3: Establish a mesoscale unit cell simulation model of the fabric composite based on fiber bundles and matrix, conduct simulation calculations, and extract the number of damaged elements in three directions of the in-layer fiber bundles and the number of damaged elements of the matrix; Step 4: According to the damage proportions calculated in Steps 1 and 2, the tensile damage of the 0° unidirectional tape represents the damage of the fiber bundle in one direction, and the tensile damage of the 90° unidirectional tape represents the damage of the fiber bundles in the second and third directions. Classify the in-layer damage in the simulation as matrix damage, interface damage, and fiber damage; Step 5: Conduct a microscopic observation of the damaged cross-section of the fabric composite and calculate the proportion of the three damage modes.

2. The simulation method for in-layer of composite materials based on microscopic observation according to claim 1, wherein: Step 1 includes: Taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find typical pictures of the three microscopic damage modes, train using machine learning methods, identify the three damage modes of the 0° unidirectional tape fracture surface according to the trained model, project the damaged area areas corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes.

3. A method for simulating within a composite material layer based on microscopic observation according to claim 1, characterized in that: Step 2 includes: Taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find typical pictures of the three microscopic damage modes, train using machine learning methods, identify the three damage modes of the 90° unidirectional tape fracture surface according to the trained model, project the damaged area areas corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes.

4. A simulation method for in-layer of composite materials based on microscopic observation according to claim 1, characterized in that: Step 5 includes: Taking microscopic photos of the fracture surface. The basic damage forms of the composite material are matrix damage, interface damage, and fiber damage. Find typical pictures of the three microscopic damage modes, train using machine learning methods, identify the three damage modes of the damaged cross-section of the fabric composite according to the trained model, project the damaged area areas corresponding to the three damage modes onto the same horizontal plane, and calculate the proportion of the three damage modes according to their respective area sizes.

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