Effective and efficient optics inspection approach using machine learning algorithms

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The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the ... continued below

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Abdulla, G; Kegelmeyer, L; Liao, Z & Carr, W November 2, 2010.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 117 times . More information about this article can be viewed below.

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Description

The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

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PDF-file: 9 pages; size: 0.6 Mbytes

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  • Presented at: SPIE Laser Damage Conference, Boulder, CO, United States, Sep 26 - Sep 29, 2010

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  • Report No.: LLNL-PROC-462149
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 1016304
  • Archival Resource Key: ark:/67531/metadc840108

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  • November 2, 2010

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  • May 19, 2016, 3:16 p.m.

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  • Nov. 23, 2016, 5:18 p.m.

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Abdulla, G; Kegelmeyer, L; Liao, Z & Carr, W. Effective and efficient optics inspection approach using machine learning algorithms, article, November 2, 2010; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc840108/: accessed August 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.