Structure recognition from high resolution images of ceramic composites
Abstract
Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1167569
- Report Number(s):
- LBNL-6900E
- DOE Contract Number:
- DE-AC02-05CH11231
- Resource Type:
- Conference
- Resource Relation:
- Conference: IEEE Int. Conf. on Big Data
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; Material Inspection; Fiber Detection; ImageJ/Fiji plug-in; GPU; OpenCL
Citation Formats
Ushizima, Daniela, Perciano, Talita, Krishnan, Harinarayan, Loring, Burlen, Bale, Hrishikesh, Parkinson, Dilworth, and Sethian, James. Structure recognition from high resolution images of ceramic composites. United States: N. p., 2015.
Web.
Ushizima, Daniela, Perciano, Talita, Krishnan, Harinarayan, Loring, Burlen, Bale, Hrishikesh, Parkinson, Dilworth, & Sethian, James. Structure recognition from high resolution images of ceramic composites. United States.
Ushizima, Daniela, Perciano, Talita, Krishnan, Harinarayan, Loring, Burlen, Bale, Hrishikesh, Parkinson, Dilworth, and Sethian, James. 2015.
"Structure recognition from high resolution images of ceramic composites". United States. https://www.osti.gov/servlets/purl/1167569.
@article{osti_1167569,
title = {Structure recognition from high resolution images of ceramic composites},
author = {Ushizima, Daniela and Perciano, Talita and Krishnan, Harinarayan and Loring, Burlen and Bale, Hrishikesh and Parkinson, Dilworth and Sethian, James},
abstractNote = {Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks and fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.},
doi = {},
url = {https://www.osti.gov/biblio/1167569},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 05 00:00:00 EST 2015},
month = {Mon Jan 05 00:00:00 EST 2015}
}