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Proceedings Paper

Application of image processing and machine learning for classification of laser-induced damage morphology
Author(s): Linas Smalakys; Erikas Švažas; Robertas Grigutis; Andrius Melninkaitis
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Paper Abstract

Interest in qualitative analysis of damage morphology of laser-induced damage test sites has increased in recent years. Such analysis can potentially provide valuable information about underlying damage mechanisms and can be used for separation of different damage modes. However, morphological analyses are currently performed manually and only on a few test sites at a time. In this work, a novel computational approach to the analysis of damaged test sites is presented. Image processing algorithms were applied to images of test sites in order to identify damaged test sites and extract features of damage morphology. Unsupervised machine learning was performed to automatically cluster damaged test sites. It was shown that ZrO2 single layer’s laser-induced damage can be separated into well defined clusters. The clusters were grouped to distinct catastrophic and color-change modes. Characteristic damage curves of different damage modes were investigated to reveal different fatigue behavior.

Paper Details

Date Published: 16 November 2018
PDF: 7 pages
Proc. SPIE 10805, Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference, 108052B (16 November 2018); doi: 10.1117/12.2500335
Show Author Affiliations
Linas Smalakys, Vilnius Univ. (Lithuania)
LIDARIS Ltd. (Lithuania)
Erikas Švažas, Vilnius Univ. (Lithuania)
LIDARIS Ltd. (Lithuania)
Robertas Grigutis, Vilnius Univ. (Lithuania)
LIDARIS Ltd. (Lithuania)
Andrius Melninkaitis, Vilnius Univ. (Lithuania)
LIDARIS Ltd. (Lithuania)

Published in SPIE Proceedings Vol. 10805:
Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference
Christopher Wren Carr; Gregory J. Exarhos; Vitaly E. Gruzdev; Detlev Ristau; M.J. Soileau, Editor(s)

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