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Machine Learning Algorithms for Automated NIF Capsule Mandrel Selection
Fusion Science and Technology ( IF 0.9 ) Pub Date : 2020-08-12 , DOI: 10.1080/15361055.2020.1777673
K.-J. Boehm 1 , Y. Ayzman 2 , R. Blake 2 , A. Garcia 1 , K. Sequoia 1 , S. Sundram 2 , W. Sweet 1
Affiliation  

Abstract Small shells, approximately 2 mm in diameter, made from Poly(α-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation. As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., “Automation in Target Fabrication” [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. As an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.

中文翻译:

用于自动 NIF 胶囊心轴选择的机器学习算法

摘要 由聚(α-甲基苯乙烯)(PAMS)制成的直径约 2 mm 的小壳用作心轴,用于生产位于惯性约束聚变实验中心的辉光放电聚合物胶囊。这些壳芯棒的显微镜图像的目视检查过程,包括检测壳表面微米级缺陷以及处理和分类,是一个非常劳动密集、重复和高度主观的过程,将从中受益匪浅。自动化。作为减少胶囊处理、检查和计量工作时间的一部分,Carlson 等人在一篇论文“目标制造自动化”[Fusion Sci. 技术,卷。70,第。274 (2016)]。目前的工作扩展了之前开发的自动图像采集系统,并增加了卷积神经网络的使用,以选择最适合在下游生产过程中使用的胶囊。通过使用这些机器学习算法,选择过程变得稳健、可重复且独立于操作员。作为这项工作的一部分开发的系统的一个额外好处是能够提供整个外壳批次的缺陷统计数据,并将此信息上游提供给生产团队。
更新日期:2020-08-12
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