Defect Classification Using Machine Learning
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.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 945842
- Report Number(s):
- LLNL-PROC-408339; TRN: US200903%%829
- Resource Relation:
- Journal Volume: 7132; Conference: Presented at: Boulder Damage Symposium, Boulder, CO, United States, Sep 22 - Sep 24, 2008
- Country of Publication:
- United States
- Language:
- English
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