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Proceedings Paper

Defect classification using machine learning
Author(s): Adra Carr; L. Kegelmeyer; Z. M. Liao; G. Abdulla; D. Cross; W. P. Kegelmeyer; F. Ravizza; C. Wren Carr
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Paper Abstract

Laser-induced damage growth on the surface of fused silica optics has been extensively studied and has been found to depend on a number of factors including fluence and the surface on which the damage site resides. It has been demonstrated that damage sites as small as a few tens of microns can be detected and tracked on optics installed a fusion-class laser, however, determining the surface of an optic on which a damage site resides in situ can be a significant challenge. In this work demonstrate that a machine-learning algorithm can successfully predict the surface location of the damage site using an expanded set of characteristics for each damage site, some of which are not historically associated with growth rate.

Paper Details

Date Published: 30 December 2008
PDF: 6 pages
Proc. SPIE 7132, Laser-Induced Damage in Optical Materials: 2008, 713210 (30 December 2008); doi: 10.1117/12.817418
Show Author Affiliations
Adra Carr, Univ. of Colorado at Boulder (United States)
L. Kegelmeyer, Lawrence Livermore National Lab. (United States)
Z. M. Liao, Lawrence Livermore National Lab. (United States)
G. Abdulla, Lawrence Livermore National Lab. (United States)
D. Cross, Lawrence Livermore National Lab. (United States)
W. P. Kegelmeyer, Sandia National Labs. (United States)
F. Ravizza, Lawrence Livermore National Lab. (United States)
C. Wren Carr, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 7132:
Laser-Induced Damage in Optical Materials: 2008
Gregory J. Exarhos; Detlev Ristau; M. J. Soileau; Christopher J. Stolz, Editor(s)

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