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16 November 2018 Application of image processing and machine learning for classification of laser-induced damage morphology
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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.
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Linas Smalakys, Erikas Švažas, Robertas Grigutis, and Andrius Melninkaitis "Application of image processing and machine learning for classification of laser-induced damage morphology", Proc. SPIE 10805, Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference, 108052B (16 November 2018); https://doi.org/10.1117/12.2500335
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