Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns [electronic resource].
- Washington, D.C. : United States. Dept. of Defense, 1994.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Physical Description:
- 4 pages : digital, PDF file
- Additional Creators:
- Argonne National Laboratory
United States. Department of Defense
United States. Department of Energy. Office of Scientific and Technical Information
- Laser scatter has shown promise as a method to characterize damage microstructural variations as well as a method to characterize surfaces in optical translucent ceramics. Because large volumes of data need to be handled (and sorted) quickly, automated pattern recognition methods using neural networks have been implemented to recognize differences in patterns. A He-Ne laser (λ=0.632μ) has been used to obtain scatter patterns from hot pressed Si₃N₄ with various microstructural variations. By use of a backpropagation neural network running on an IBM PC clone 486/33 machine, a correlation was established between subsurface microstructure and position in Si₃N₄ ball bearings. The data were confirmed by destructive analysis.
- Published through SciTech Connect.
18. annual conference on composites and advanced ceramics,Cocoa Beach, FL (United States),9-14 Jan 1994.
Ellingson, W.A.; Stinson, M.C.; Steckenrider, J.S.; Lee, O.W.
- Funding Information:
View MARC record | catkey: 14112286