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Title: Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns

Abstract

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 ({lambda}=0.632{mu}) has been used to obtain scatter patterns from hot pressed Si{sub 3}N{sub 4} 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 Si{sub 3}N{sub 4} ball bearings. The data were confirmed by destructive analysis.

Authors:
 [1];  [2]; ;  [3]
  1. Central Michigan Univ., (United States). Dept. of Computer Science
  2. Univ. of California, Los Angeles, CA (United States). Dept. of Electrical Engineering
  3. Argonne National Lab., IL (United States)
Publication Date:
Research Org.:
Argonne National Lab., IL (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States); Department of Defense, Washington, DC (United States)
OSTI Identifier:
10180036
Report Number(s):
ANL/ET/CP-80540; CONF-940135-11
ON: DE94018373
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Conference
Resource Relation:
Conference: 18. annual conference on composites and advanced ceramics,Cocoa Beach, FL (United States),9-14 Jan 1994; Other Information: PBD: [1994]
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 42 ENGINEERING; SILICON NITRIDES; INSPECTION; BALL BEARINGS; LIGHT SCATTERING; LASER RADIATION; LIGHT TRANSMISSION; OPACITY; MATERIALS TESTING; 360200; 420500; CERAMICS, CERMETS, AND REFRACTORIES

Citation Formats

Stinson, M C, Lee, O W, Steckenrider, J S, and Ellingson, W A. Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns. United States: N. p., 1994. Web.
Stinson, M C, Lee, O W, Steckenrider, J S, & Ellingson, W A. Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns. United States.
Stinson, M C, Lee, O W, Steckenrider, J S, and Ellingson, W A. Thu . "Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns". United States. https://www.osti.gov/servlets/purl/10180036.
@article{osti_10180036,
title = {Recognition of subsurface defects in machined ceramics by application of neural networks to laser scatter patterns},
author = {Stinson, M C and Lee, O W and Steckenrider, J S and Ellingson, W A},
abstractNote = {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 ({lambda}=0.632{mu}) has been used to obtain scatter patterns from hot pressed Si{sub 3}N{sub 4} 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 Si{sub 3}N{sub 4} ball bearings. The data were confirmed by destructive analysis.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1994},
month = {9}
}

Conference:
Other availability
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