织物增强复合材料Micro-CT辅助数值仿真技术研究进展

杨斌; 王继辉; 冯雨薇; 杨超; 倪爱清

织物增强复合材料Micro-CT辅助数值仿真技术研究进展

杨斌^{1, 2},
王继辉¹,
冯雨薇¹,
杨超¹,
倪爱清^3, ,

1.
武汉理工大学材料科学与工程学院武汉 430070
2.
Polytechnique Montreal, Department of Mechanical Engineering, Montreal H3T 1J4
3.
武汉理工大学材料复合新技术国家重点实验室, 武汉 430070

基金项目: 国家重点研发计划资助 (2022YFB4300102)

详细信息

通讯作者:
倪爱清，博士，副研究员，硕士生导师，研究方向为先进复合材料　E-mail: aqni21 stcn@163.com

中图分类号: TB332
计量
- 文章访问数: 92
- HTML全文浏览量: 68
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-14
- 修回日期: 2023-04-03
- 录用日期: 2023-04-21
- 网络出版日期: 2023-05-06

Advances in Micro-CT aided numerical simulation of fabric-reinforced composites

Bin YANG^{1, 2},
Jihui WANG¹,
Yuwei FENG¹,
Chao YANG¹,
Aiqing NI^{3
, ,}

1.
School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
Polytechnique Montreal, Department of Mechanical Engineering, Montreal H3T 1J4, Canada
3.
State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China

Funds: National Key R&D Program of China(2022YFB4300102)

摘要

摘要: 精确的数值模型是获得可靠的数值仿真结果的前提。显微计算机断层扫描(Micro-CT)技术可无损成像复合材料的内部结构，据此建立的数值模型比理想化模型更具代表性。本文综述了基于Micro-CT图像的复合材料介观模型构建方法及其在材料虚拟测试中的应用，提出了Micro-CT辅助数值仿真的概念。首先，对Micro-CT成像原理、设备特点和织物增强复合材料的成像难点进行了讨论。其次，梳理了现有的Micro-CT辅助建模技术的特点，将其建立的模型划分为间接模型、体素模型和数字材料孪生模型，重点介绍了构建各类模型的理论基础和技术途径，指出了各自的优势和局限性。然后对Micro-CT辅助数值仿真技术在织物增强复合材料的成型工艺和力学性能预测等方面的应用进行了总结，表明了该技术的重要价值。最后，对Micro-CT辅助数值仿真技术的未来发展进行了展望。
- 织物增强复合材料 /
- Micro-CT /
- 数值模型 /
- 图像重建 /
- 数字孪生
Abstract: Reliable numerical simulation results depend on the use of accurate numerical models. Micro-computed tomography (Micro-CT) technology is capable of non-destructively imaging the internal structure of composite materials, hence, numerical models reconstructed from it are more representative than the idealized ones. Here, a review is presented for constructing mesoscopic models of composite materials based on Micro-CT images and its application in virtual testing. A novel concept, Micro-CT aided numerical simulation, is proposed. First, the principle of Micro-CT imaging, the characteristics of equipment, and the difficulties with scanning fabric-reinforced composites are discussed. Second, the characteristics of existing Micro-CT aided modeling techniques are analyzed and compared. The models are divided into three types including indirect, voxel, and digital material twin ones. The theoretical basis and technical approaches for constructing each type of model are highlighted, and the advantages and limitations of each are analyzed. Then recent applications of Micro-CT aided numerical simulation in composite molding process and mechanical property prediction of fabric reinforced composites are summarized, showing the potential and importance of the technology. Finally, the future of Micro-CT aided numerical simulation technology is anticipated.
- fabric-reinforced composites /
- Micro-CT /
- numerical simulation /
- image reconstruction /
- digital twin

HTML全文

图 1 织物增强复合材料多尺度结构

Figure 1. Multi-scale structure of textile reinforcements

下载: 全尺寸图片幻灯片

图 2 增强织物多尺度结构对复合材料加工和力学性能的影响

Figure 2. Effect of multi-scale structure on manufacturing process and mechanical properties of composites

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图 3 显微计算机断层扫描成像的x射线源：同步辐射光源(上)和锥形束光源(下)

Figure 3. Imaging of microcomputed tomography: X-ray beam initiates from synchrotron light sources (top) and from cone-beam source (bottom)

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图 4 图像分割与重建

Figure 4. Image segmentation and reconstruction

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图 5 间接建模技术的纤维束路径、轮廓特征定义和尺寸数据分析^[67]

Figure 5. Fiber yarn paths, profile definition and dimensional analysis for indirect modeling techniques^[67]

下载: 全尺寸图片幻灯片

图 6 织物结构直接建模技术流程图

Figure 6. Flow chart of direct textile geometrical modeling technique

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图 7 织物增强复合材料体素模型

Figure 7. Voxel model for fabric-reinforced composites

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图 8 基于Kriging的显式参数化表面重建方法^[6]

Figure 8. Kriging-based explicit parametric surface reconstruction method^[6]

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图 9 隐式表面重建方法生成的共形网格^[64]

Figure 9. Conformal mesh generated by implicit surface reconstruction method^[64]

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图 10 采用显微计算机断层扫描技术研究复合材料的历年文献发表数量

Figure 10. Number of scientific literatures applying micro tomography in investigations

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图 11 复合材料数字孪生技术应用图谱

Figure 11. Application of digital twin technology for composite materials

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图 12 基于体素模型的渗透率预测：(a) 边界条件和(b)速度云图^[109]

Figure 12. Permeability prediction based on voxel model^[109]

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图 13 不同精度的数值模型和X方向的拉伸应力云图^[72]

Figure 13. Numerical models with different accuracy and tensile stress field in $ x $-direction^[72]

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表 1 Micro-CT 图像预处理和图像分割方法

Table 1. Micro-CT image pre-processing and image segmentation methods

Textile	Resolution/$ \mathrm{\mu }\mathrm{m} $	Denosing	Smoothing	Segmentation	Software	Reference
Plain weave	10.4	/	/	Manual segmentation	VG studio	^[67]
	25	Pixel intensity averaged with neighbors		Sobel operator & Structure tensor	MATLAB	^[77]
	1 - 10	Median filter		Threshold-texture feature-morphology	/	^[78]
	5	A 1 D uniform filter		Morphological gradient & Deep Learning	ImageJ & SciPy	^[82]
	18.6	Non-local mean filter		thresholding based on seed region growing	Avizo	^[83]
5-directional braided composite	22.1	√	√	√	VG studio/ImageJ	^[65]
Fiber tow	9.3	/	Nugget effect	Implicit kriging & Indicator function	In-house Python code	^[84]
3 D fabric	2	Median filter		Texture analysis	/	^[74]
3 D fabric	/	Gaussian smoothing		Structure tensor & Signed distances	MATLAB	^[64]
Note: “/” means information of the item was not given by authors, while “√” means the item was done but the method was not given.

下载: 导出CSV

表 2 五种常用的多孔介质渗流力学求解器

Table 2. Five commonly used solvers for percolation mechanics in porous media

Category	Governing equation	Meshing	software	Reference
Computational Fluid Dynamic (CFD)	Stokes and Navier-Stokes equations on discretized grid mesh	Yes	Fluent, OpenFOAM, et al,	^[92,110,111]
Lattice-Boltzmann Method (LBM)	Boltzmann equation on voxel mesh	No	OpenLB et al.	^[112,113]
Voxel Based direct Navier-Stokes Solvers (VBS)	Stokes and Navier-Stokes equations on segmented Micro-CT images	No	VG studio, Avizo et al.	^{[73,105,107,109,114]}
Semi-Analytical Solvers (SAS)	Analytical up-scaling solutions	No	/	^[115]
Empirical Kozeny–Carman type model (EMP)	pore size distribution extracted from	No	/	^[5,88]

下载: 导出CSV

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