30 December 2008 Defect classification using machine learning
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adra Carr, Adra Carr, L. Kegelmeyer, L. Kegelmeyer, Z. M. Liao, Z. M. Liao, G. Abdulla, G. Abdulla, D. Cross, D. Cross, W. P. Kegelmeyer, W. P. Kegelmeyer, F. Ravizza, F. Ravizza, C. Wren Carr, C. Wren Carr, } "Defect classification using machine learning", Proc. SPIE 7132, Laser-Induced Damage in Optical Materials: 2008, 713210 (30 December 2008); doi: 10.1117/12.817418; https://doi.org/10.1117/12.817418

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