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@unpublished{kegelmeyer:icalepcs2019-thcpl02,
author = {L.M. Kegelmeyer},
title = {{Evolution of Machine Learning for NIF Optics Inspection}},
booktitle = {Proc. ICALEPCS'19},
language = {english},
intype = {presented at the},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
venue = {New York, NY, USA},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {aug},
year = {2020},
note = {presented at ICALEPCS2019 in New York, NY, USA, unpublished},
abstract = {The National Ignition Facility (NIF) is the most energetic laser in the world where scientists from around the world conduct experiments supporting fields like astrophysics, materials science, nuclear science, and using fusion as a clean, safe energy source. In doing so, the NIF routinely operates above the damage threshold for its optics. To extend optic lifetimes, we developed a recycle loop during which each damage site on an optic is tracked through time, protected when it approaches an optic-specific size limit, and then repaired so the optic can be reused. Here we describe an overview of custom image analysis, machine learning, and deep learning methods used throughout the recycle loop for optics inspection on the NIF beamlines and off. Most recently, we helped automate the optic repair process by identifying microscopic damage before its repair and then evaluating the repaired site for quality control. Since 2007 we’ve used machine learning to improve accuracy and automate tedious processes to enable and inform an efficient optics recycle loop.},
}