Automated optics inspection analysis for NIF

Kegelmeyer, Laura M.; Clark, Raelyn; Leach, Richard R.; McGuigan, David; Kamm, Victoria Miller; Potter, Daniel; Salmon, J. T.; Senecal, Joshua G.; Conder, A.; Nostrand, Mike C.; Whitman, Pamela K.

doi:10.1016/j.fusengdes.2012.09.017

Cited by 21 publications

(7 citation statements)

References 5 publications

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“…The detection of surface defects is one of the main items of surface quality evaluation for optical elements. In optical systems, scattering induced by surface defects will reduce the utilization efficiency of energy or even lead to severe damage to those with high-energy lasers, such as the inertial confinement fusion system (ICF) [1][2][3]. With the wide application of the optical elements and the development of manufacturing technology, the requirements for defect detection become higher.…”

Section: Introductionmentioning

confidence: 99%

Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

Wang

Cao²,

et al. 2020

Applied Sciences

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Compared to traditional approaches, the spot scanning surface defect evaluation system (SS-SDES) has better performances on the detection of small defects and defect classification for optical surfaces. However, the existing system deviations will cause distortions and even a missing area in the defect image which is reconstructed from the acquired raw data based on the scanning trace, thus degrading the reliability of detection results. To solve these problems, a system calibration method is proposed with the parameterization of these deviations and the modeling of practical scanning trace. A constraint function, to characterize the straightness and scale errors in the image, is defined. Then an optimization is implemented to minimize it and hence to obtain the optimal estimate of the system deviations, which is subsequently used to adjust the system and reconstruct reliable defect images. Additionally, to further enhance the image quality, an image reconstruction method capable of suppressing signal noise through a weighted average strategy is proposed. Experiments show that with our methods, the system deviations are effectively corrected, and a complete and precise defect image with low distortions that are within 1.8 pixels is reconstructed. Therefore, the detection accuracy and reliability of the system can be improved.

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Section: Introductionmentioning

confidence: 99%

Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

Wang

Cao²,

et al. 2020

Applied Sciences

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“…In order to extend optics' lifetimes, NIF has developed a recycle loop whereby optics can be removed, repaired and reused many times . Custom camera and analysis systems automatically inspect images of the optics while in situ, on the laser beamline, and report their status . These systems constantly monitor damage on optics and track its growth from as small as around 10 µm.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

Amorin

Kegelmeyer

2019

Statistical Analysis

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Accurately classifying microscopic damage helps automate the repair and recycling of National Ignition Facility optics and informs the study of damage initiation and growth. This complex 12-class problem previously required human experts to distinguish and label the various damage morphologies. Finding image analysis methods to extract and calculate distinguishing features would be time consuming and challenging, so we sought to automate this task by using convolutional neural networks (CNNs) pretrained on the ImageNet database to take advantage of its automated feature discovery and extraction. We compared three model architectures on this dataset and found the one with highest overall accuracy, 99.17%, was a novel hybrid architecture, one in which we removed the final decision-making layer of the deep learner and replaced it with an ensemble of decision trees (EDT). This combines the power of feature extraction by CNNs with the decision-making strength of EDT.The accuracy of the hybrid architecture over the deep learning alone is shown to be significantly improved. Furthermore, we applied this novel hybrid architecture to an entirely different dataset, one containing images of repaired damage sites, and improved on the previously published findings, also with a demonstrably significant increase in accuracy over using the deep learner alone. K E Y W O R D Sautomation, deep learning, laser optic damage, machine learning

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“…From the literature published in recent years, there are mainly two research teams in the field of damage online detection in ICF experiments for large aperture optics: the Lawrence Livermore National Laboratory (LLNL) and the China Academy of Engineering Physics (CAEP). Each team has developed its own experimental version of the final optics damage inspection (FODI) system, namely, the NIF FODI developed for the United States National Ignition Facility (NIF) by LLNL scientists 8,9 and the SG-III FODI we developed for the SG-III laser facility at the CAEP. 10 In general, FODI is an online imaging system based on machine vision for rapidly observing laser-induced damage.…”

Section: Introductionmentioning

confidence: 99%

“…An FODI system uses edge illumination as an imaging technology. 8,16,17 As shown in Fig. 2, all optics have two microlaser diodes (MLDs) fixed in a metal frame as the illumination light source.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of true and false laser-induced damage in large aperture optics

et al. 2018

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Abstract. An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell's equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Automated optics inspection analysis for NIF

Cited by 21 publications

References 5 publications

Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects

A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

Automatic classification of true and false laser-induced damage in large aperture optics

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