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Title: Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics

Abstract

A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser`s wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known ``feature masks`` of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the storedmore » feature masks allows for automatic classification of detected defects. 29 figs.« less

Inventors:
;
Issue Date:
Research Org.:
Univ. of Chicago, IL (United States)
OSTI Identifier:
87733
Patent Number(s):
5,426,506
Application Number:
PAN: 8-036,320
Assignee:
Univ. of Chicago, IL (United States)
DOE Contract Number:  
W-31109-ENG-38
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 20 Jun 1995
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING NOT INCLUDED IN OTHER CATEGORIES; CERAMICS; NONDESTRUCTIVE TESTING; USES; DEFECTS; SCATTERING; FREQUENCY SELECTION; VISIBLE RADIATION; OPTICAL SYSTEMS; DIGITIZERS; AUTOMATION; DATA PROCESSING

Citation Formats

Ellingson, W A, and Brada, M P. Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics. United States: N. p., 1995. Web.
Ellingson, W A, & Brada, M P. Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics. United States.
Ellingson, W A, and Brada, M P. Tue . "Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics". United States.
@article{osti_87733,
title = {Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics},
author = {Ellingson, W A and Brada, M P},
abstractNote = {A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser`s wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known ``feature masks`` of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the stored feature masks allows for automatic classification of detected defects. 29 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1995},
month = {6}
}