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    Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

    Source: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006::page 061015-1
    Author:
    Zhang, Ziyang
    ,
    Shi, Junchuan
    ,
    Yu, Tianyu
    ,
    Santomauro, Aaron
    ,
    Gordon, Ali
    ,
    Gou, Jihua
    ,
    Wu, Dazhong
    DOI: 10.1115/1.4047477
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Carbon fiber-reinforced polymer (CFRP) composites have been used extensively in the aerospace and automotive industries due to their high strength-to-weight and stiffness-to-weight ratios. Compared with conventional manufacturing processes for CFRP, additive manufacturing (AM) can facilitate the fabrication of CFRP components with complex structures. While AM offers significant advantages over conventional processes, establishing the structure–property relationships in additively manufactured CFRP remains a challenge because the mechanical properties of additively manufactured CFRP depend on many design parameters. To address this issue, we introduce a data-driven modeling approach that predicts the flexural strength of continuous carbon fiber-reinforced polymers (CCFRP) fabricated by fused deposition modeling (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength of the CCFRP specimens is quantified.
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      Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4275338
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    • Journal of Computing and Information Science in Engineering

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    contributor authorZhang, Ziyang
    contributor authorShi, Junchuan
    contributor authorYu, Tianyu
    contributor authorSantomauro, Aaron
    contributor authorGordon, Ali
    contributor authorGou, Jihua
    contributor authorWu, Dazhong
    date accessioned2022-02-04T22:19:26Z
    date available2022-02-04T22:19:26Z
    date copyright7/9/2020 12:00:00 AM
    date issued2020
    identifier issn1530-9827
    identifier othersol_143_1_011005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4275338
    description abstractCarbon fiber-reinforced polymer (CFRP) composites have been used extensively in the aerospace and automotive industries due to their high strength-to-weight and stiffness-to-weight ratios. Compared with conventional manufacturing processes for CFRP, additive manufacturing (AM) can facilitate the fabrication of CFRP components with complex structures. While AM offers significant advantages over conventional processes, establishing the structure–property relationships in additively manufactured CFRP remains a challenge because the mechanical properties of additively manufactured CFRP depend on many design parameters. To address this issue, we introduce a data-driven modeling approach that predicts the flexural strength of continuous carbon fiber-reinforced polymers (CCFRP) fabricated by fused deposition modeling (FDM). The predictive model of flexural strength is trained using machine learning and validated on experimental data. The relationship between three structural design factors, including the number of fiber layers, the number of fiber rings as well as polymer infill patterns, and the flexural strength of the CCFRP specimens is quantified.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePredicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning
    typeJournal Paper
    journal volume20
    journal issue6
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4047477
    journal fristpage061015-1
    journal lastpage061015-13
    page13
    treeJournal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006
    contenttypeFulltext
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