Defect classification using machine learning

Adra Carr; L. Kegelmeyer; Z. M. Liao; G. Abdulla; D. Cross; W. P. Kegelmeyer; F. Ravizza; C. Wren Carr

doi:10.1117/12.817418

30 December 2008 Defect classification using machine learning

Adra Carr, L. Kegelmeyer, Z. M. Liao, G. Abdulla, D. Cross, W. P. Kegelmeyer, F. Ravizza, C. Wren Carr

Author Affiliations +

Proceedings Volume 7132, Laser-Induced Damage in Optical Materials: 2008; 713210 (2008) https://doi.org/10.1117/12.817418
Event: Boulder Damage Symposium XL Annual Symposium on Optical Materials for High Power Lasers, 2008, Boulder, Colorado, United States

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.

Citation Download Citation

Adra Carr, L. Kegelmeyer, Z. M. Liao, G. Abdulla, D. Cross, W. P. Kegelmeyer, F. Ravizza, and C. Wren Carr "Defect classification using machine learning", Proc. SPIE 7132, Laser-Induced Damage in Optical Materials: 2008, 713210 (30 December 2008); https://doi.org/10.1117/12.817418

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