Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images

QIAN Qiwei; ZHANG Xin; YANG Zhenjun; SHEN Zhen; XIAO Jinyou

Volume 41 Issue 7

Jul. 2024

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Article Navigation > Acta Materiae Compositae Sinica > 2024 > 41(7): 3540-3547

QIAN Qiwei, ZHANG Xin, YANG Zhenjun, et al. Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3540-3547.

Citation:

QIAN Qiwei, ZHANG Xin, YANG Zhenjun, et al. Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3540-3547.

Citation:

QIAN Qiwei, ZHANG Xin, YANG Zhenjun, et al. Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images[J]. Acta Materiae Compositae Sinica, 2024, 41(7): 3540-3547.

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Intelligent identification of micro components and defects of 3D braided C/C composites based on deep learning of X-ray CT images

1.
School of Civil Engineering, Wuhan University, Wuhan 430072, China
2.
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
3.
Xi'an Aerospace Composite Research Institute,Xi′an710025, China
4.
School of Astronautics, Northwestern Polytechnical University, Xi′an 710072, China

Funds: National Natural Science Foundation of China (52173300); Key Research and Development Programme of Hubei Province (2020BAB052)

Received Date: 2023-09-05
Accepted Date: 2023-10-26
Rev Recd Date: 2023-10-13

Available Online: 2023-11-04

Publish Date: 2024-07-15

Abstract

Abstract

Four 20 mm cubic 3D braided carbon/carbon (C/C) composite specimens were scanned by micro X-ray computed tomography (XCT) to obtain internal microstructure images with a voxel resolution of 18 μm. A deep learning based semantic segmentation algorithm was then used to train a large number of 2D XCT images to achieve intelligent identification and segmentation of rods, fiber bundles, matrix, pores, delamination and cracks of these specimens. The results show that (1) the XCT scanning can characterize the distribution and morphology of the above components and defects with high resolutions, and the dominant defect is delamination between adjacent fiber bundle layers; (2) Since the grey values in the CT images of all micro components of C/C composites are very close, it is impossible for the traditional threshold segmentation method to segment the different components, whereas the deep learning based algorithm is able to effectively filter noise and artifacts and segment all the components and defects with high accuracy and at a prediction speed of about two orders faster than manual image labelling. This deep learning algorithm thus provides a promising tool to construct high-resolution numerical models for further studies such as performance optimization of C/C composites.
- carbon/carbon composites,
- micro X-ray computed tomography,
- micro components,
- defects,
- deep learning,
- semantic segmentation
Long Abstrsct
Innovation Points

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References(27)

References

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