Abstract—In the field of materials science, the mesoscopic
geometry of materials is of great significance for the research
and development of materials and materials. This paper mainly
focuses on the image data of existing ceramic matrix composites,
and studies the characterization method of grain image of
ceramic matrix, which realizes the accurate characterization of
grain size. It has important practical research on the
mesostructure of ceramic matrix composites. Value. Taking the
SEM grain image of 5μm resolution of self-toughening silicon
nitride (Si3N4) ceramic as an example, the grain image is
segmented by median filtering, image binarization and
watershed algorithm, and then used to directional bounding box
(Oriented). The Bounding Boxes, OBB) algorithm finds the
rectangular outline bounding box of the grain, enabling
accurate measurement and statistics of the grain size.
Index Terms—Ceramic Matrix Composites (CMC), grain
size characterization, digital image processing, Oriented
Bounding Boxes (OBB).
Gao Xiang and Li Guanghui are with the School of Computer Science,
Northwestern Polytechnical University, Xi’an, Shaanxi 710129 China
(e-mail: gaoxg@nwpu.edu.cn, sxlllslgh@mail.nwpu.edu.cn).
Tan Rong is with the School of Software, Northwestern Polytechnical
University, Xi’an, Shaanxi 710129 China (e-mail:
tanrong@mail.nwpu.edu.cn).
Yao Leijiang is with the School of Laboratory of Science and Technology
on UAV, Northwestern Polytechnical University, Xi’an 710072 China
(e-mail: yaolj@nwpu.edu.cn).
Cite: Gao Xiang, Tan Rong, Li Guanghui, and Yao Leijiang, "Grain Size Characterization of Ceramic Matrix Composites," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 317-322, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).