Machine learning for managing damage on NIF optics

Laura Mascio-Kegelmeyer

doi:10.1117/12.2571016

11 September 2020 Machine learning for managing damage on NIF optics

Laura Mascio-Kegelmeyer

Proceedings Volume 11514, Laser-induced Damage in Optical Materials 2020; 1151409 (2020) https://doi.org/10.1117/12.2571016
Event: SPIE Laser Damage, 2020, Online Only

Abstract

The National Ignition Facility in northern California routinely operates at twice the intensity (fluence) known to damage fused silica optics. With this in mind, the facility was designed and built with removable optic modules that allow for optic exchanges which in turn enable an optics "recycle loop" to extend the life of highly specialized optics. The recycle loop includes automated optics inspection whereby every damage site is identified, measured, tracked through time, protected (once it approaches an optic-specific size limit), and then repaired in a laboratory 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. Since 2007 we’ve used machine learning to improve accuracy and automate tedious processes to enable and inform an efficient optics recycle loop. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-CONF-749953

Conference Presentation

Citation Download Citation

Laura Mascio-Kegelmeyer "Machine learning for managing damage on NIF optics", Proc. SPIE 11514, Laser-induced Damage in Optical Materials 2020, 1151409 (11 September 2020); https://doi.org/10.1117/12.2571016

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