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    Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

    Source: Journal of Manufacturing Science and Engineering:;2020:;volume( 142 ):;issue: 008
    Author:
    You, Shangting
    ,
    Guan, Jiaao
    ,
    Alido, Jeffrey
    ,
    Hwang, Henry H.
    ,
    Yu, Ronald
    ,
    Kwe, Leilani
    ,
    Su, Hao
    ,
    Chen, Shaochen
    DOI: 10.1115/1.4046986
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.
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      Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4273389
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    • Journal of Manufacturing Science and Engineering

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    contributor authorYou, Shangting
    contributor authorGuan, Jiaao
    contributor authorAlido, Jeffrey
    contributor authorHwang, Henry H.
    contributor authorYu, Ronald
    contributor authorKwe, Leilani
    contributor authorSu, Hao
    contributor authorChen, Shaochen
    date accessioned2022-02-04T14:18:21Z
    date available2022-02-04T14:18:21Z
    date copyright2020/05/14/
    date issued2020
    identifier issn1087-1357
    identifier othermanu_142_8_081002.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4273389
    description abstractWhen using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleMitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning
    typeJournal Paper
    journal volume142
    journal issue8
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4046986
    page81002
    treeJournal of Manufacturing Science and Engineering:;2020:;volume( 142 ):;issue: 008
    contenttypeFulltext
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