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    Genetic algorithm-powered non-sequential dwell time optimization for large optics fabrication

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    oe-30-10-16442.pdf
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    Author
    Kang, H.
    Wang, T.
    Choi, H.
    Kim, D.
    Affiliation
    James C. Wyant College of Optical Sciences, University of Arizona
    Large Binocular Telescope Observatory, University of Arizona
    Department of Astronomy, Steward Observatory, University of Arizona
    Issue Date
    2022
    
    Metadata
    Show full item record
    Publisher
    Optica Publishing Group (formerly OSA)
    Citation
    Kang, H., Wang, T., Choi, H., & Kim, D. (2022). Genetic algorithm-powered non-sequential dwell time optimization for large optics fabrication. Optics Express, 30(10), 16442–16458.
    Journal
    Optics Express
    Rights
    Copyright © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Computer Controlled Optical Surfacing (CCOS) is widely applied for fabricating large aspheric optical surfaces. For large optics fabrication, various sizes of polishing tools are used sequentially. This raises the importance of efficient and globally optimized dwell time map of each tool. In this study, we propose a GEnetic Algorithm-powered Non-Sequential (GEANS) optimization technique to improve the feasibility of the conventional non-sequential optimization technique. GEANS consists of two interdependent parts: i) compose an influence matrix by imposing constraints on adjacent dwell points and ii) induce the desired dwell time map through the genetic algorithm. CCOS simulation results show that GEANS generates a preferable dwell time map that provides high figuring efficiency and structural similarity with the shape of target removal map, while improving computational efficiency more than 1000 times over the conventional non-sequential optimization method. The practicability of GEANS is demonstrated through error analyses. Random tool positioning error and tool influence function errors are imposed on dwell time maps. Compared to the conventional non-sequential optimization method, the power spectral density values of residual surface error from GEANS remain stable. GEANS also shows superior applicability when the maximum acceleration of a tool is applied. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
    Note
    Open access journal
    ISSN
    1094-4087
    DOI
    10.1364/OE.457505
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1364/OE.457505
    Scopus Count
    Collections
    UA Faculty Publications

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