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    In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy

    Source: Journal of Manufacturing Science and Engineering:;2025:;volume( 147 ):;issue: 003::page 31011-1
    Author:
    Stegman, Benjamin
    ,
    Raj, Anant
    ,
    Owen, Charlie
    ,
    Abdel-Khalik, Hany
    ,
    Sutherland, John W.
    ,
    Zhang, Xinghang
    DOI: 10.1115/1.4067613
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Additive manufacturing has found its niche in critical component applications in the aerospace and nuclear industries. For these industries, there is an increasing need for a cost-effective quality assurance method. For laser powder bed fusion (LPBF), in-situ sensing has shown promise with various forms of defect detection but has only shown limited success in microstructural characterization. Utilizing concurrent in-situ data collection from a complementary metal oxide semiconductor (CMOS) and photodiode sensor, this work establishes a relationship between in-situ sensor monitoring, crystallographic texture, and mechanical properties through machine learning (ML). By combining the in-situ monitoring data, ML, and a dataset of over 100 samples, including X-ray diffraction and tensile testing results, the model successfully predicts textures of 718 Ni alloy with up to 90% accuracy and identifies the correlation between texture and mechanical properties. Furthermore, three key characteristic samples were investigated via electron backscatter diffraction to delve deeper into mechanical property differences brought by microstructural features. While the model requires future datasets to improve reliability, it opens a pathway to use in-situ processing data to predict the microstructure and mechanical properties of LPBF materials.
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      In-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy

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    contributor authorStegman, Benjamin
    contributor authorRaj, Anant
    contributor authorOwen, Charlie
    contributor authorAbdel-Khalik, Hany
    contributor authorSutherland, John W.
    contributor authorZhang, Xinghang
    date accessioned2025-04-21T10:20:02Z
    date available2025-04-21T10:20:02Z
    date copyright2/11/2025 12:00:00 AM
    date issued2025
    identifier issn1087-1357
    identifier othermanu-24-1374.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305962
    description abstractAdditive manufacturing has found its niche in critical component applications in the aerospace and nuclear industries. For these industries, there is an increasing need for a cost-effective quality assurance method. For laser powder bed fusion (LPBF), in-situ sensing has shown promise with various forms of defect detection but has only shown limited success in microstructural characterization. Utilizing concurrent in-situ data collection from a complementary metal oxide semiconductor (CMOS) and photodiode sensor, this work establishes a relationship between in-situ sensor monitoring, crystallographic texture, and mechanical properties through machine learning (ML). By combining the in-situ monitoring data, ML, and a dataset of over 100 samples, including X-ray diffraction and tensile testing results, the model successfully predicts textures of 718 Ni alloy with up to 90% accuracy and identifies the correlation between texture and mechanical properties. Furthermore, three key characteristic samples were investigated via electron backscatter diffraction to delve deeper into mechanical property differences brought by microstructural features. While the model requires future datasets to improve reliability, it opens a pathway to use in-situ processing data to predict the microstructure and mechanical properties of LPBF materials.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleIn-Situ Monitoring and Its Correlation to Mechanical Properties in Additively Manufactured 718 Ni Alloy
    typeJournal Paper
    journal volume147
    journal issue3
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4067613
    journal fristpage31011-1
    journal lastpage31011-9
    page9
    treeJournal of Manufacturing Science and Engineering:;2025:;volume( 147 ):;issue: 003
    contenttypeFulltext
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