期刊
JOURNAL OF MATERIALS SCIENCE
卷 55, 期 34, 页码 16273-16289出版社
SPRINGER
DOI: 10.1007/s10853-020-05148-7
关键词
-
资金
- US National Science Foundation PIRE program [1743701]
- Office Of The Director
- Office Of Internatl Science &Engineering [1743701] Funding Source: National Science Foundation
A deep learning procedure has been examined for automatic segmentation of 3D tomography images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and thus identical image intensities. The analysis uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extraction of quantitative information from such images. The analysis was performed using recently developed software that provides a general framework for executing both training and inference. Graphic abstract
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据