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    Chapter Real-Time Geometry Assessment Using Laser Line Scanner During Laser Powder Directed Energy Deposition Additive Manufacturing of SS316L Component with Sharp Feature

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    Author(s)
    Liu, Peipei
    Yang, Liu
    Wang, Boyu
    Cheng, Jack C. P.
    Sohn, Hoon
    Language
    English
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    Abstract
    Directed energy deposition (DED) is a major metal additive manufacturing (AM) technology that is increasingly used in many industries due to its ability to manufacture complex components of arbitrary shapes and sizes. However, a lack of timely geometry assessment and the consequent geometry control hinders the development of DED towards zero defect manufacturing. In this study, a real-time geometry assessment methodology is developed for laser pow-der directed energy deposition (LP-DED). A geometry assessment system is developed using a laser line scanner capable of inspecting the melt pool area, the just solidified area, as well as layer-wise inspection. An image processing method with an encoder-decoder based profile completion network was developed to obtain accurate track profile in images from real-time inspection. Experiments have been conducted to validate the proposed methodology by depositing multi-layer X-shape objects
    URI
    https://library.oapen.org/handle/20.500.12657/89035
    Keywords
    Additive Manufacturing; Directed energy deposition; Real-time geometry assessment; Laser line scanning
    DOI
    10.36253/979-12-215-0289-3.97
    ISBN
    9791221502893, 9791221502893
    Publisher
    Firenze University Press
    Publisher website
    https://www.fupress.com/
    Publication date and place
    Florence, 2023
    Series
    Proceedings e report, 137
    Classification
    Computing and Information Technology
    Pages
    12
    Rights
    https://creativecommons.org/licenses/by-nc/4.0/legalcode
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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