Journal
JOURNAL OF MATERIALS SCIENCE
Volume 55, Issue 34, Pages 16273-16289Publisher
SPRINGER
DOI: 10.1007/s10853-020-05148-7
Keywords
-
Categories
Funding
- US National Science Foundation PIRE program [1743701]
- Office Of The Director
- Office Of Internatl Science &Engineering [1743701] Funding Source: National Science Foundation
Ask authors/readers for more resources
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
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available