Automated repair of laser damage on National Ignition Facility optics using machine learning

S. Trummer; G. Larkin; L. Kegelmeyer; M. Nostrand; C. Karkazis; D. Martin; R. Aboud; T. Suratwala

doi:10.1117/12.2501826

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16 November 2018 Automated repair of laser damage on National Ignition Facility optics using machine learning

S. Trummer, G. Larkin, L. Kegelmeyer, M. Nostrand, C. Karkazis, D. Martin, R. Aboud, T. Suratwala

Author Affiliations +

Proceedings Volume 10805, Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference; 108050L (2018) https://doi.org/10.1117/12.2501826
Event: SPIE Laser Damage, 2018, Boulder, Colorado, United States

Abstract

The National Ignition Facility (NIF) regularly operates at fluences above the onset of laser-induced optics damage. To do so, it is necessary to routinely recycle the NIF final optics, which involves removing an optic from a beamline, inspecting and repairing the laser-induced damage sites, and re-installing the optic. The inspection and repair takes place in our Optics Mitigation Facility (OMF), consisting of four identical processing stations for performing the repair protocols. Until recently, OMF has been a labor-intensive facility, requiring 10 skilled operators over two shifts to meet the throughput requirements. Here we report on the implementation of an automated control system—informed by machine learning— that significantly improves the throughput capability for recycling of NIF optics while reducing staffing requirements. Performance metrics for mid-2018 show that approximately 85% of all damage sites can be automatically inspected and repaired without any required operator input. Computer keystrokes have been reduced from about 6000 per optic to under 300.

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S. Trummer, G. Larkin, L. Kegelmeyer, M. Nostrand, C. Karkazis, D. Martin, R. Aboud, and T. Suratwala "Automated repair of laser damage on National Ignition Facility optics using machine learning", Proc. SPIE 10805, Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference, 108050L (16 November 2018); https://doi.org/10.1117/12.2501826

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