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    Physics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition

    Source: Journal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 008::page 81007-1
    Author:
    Zamiela, Christian
    ,
    Stokes, Ryan
    ,
    Tian, Wenmeng
    ,
    Doude, Haley
    ,
    Priddy, Matthew W.
    ,
    Bian, Linkan
    DOI: 10.1115/1.4065416
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This work presents a physics-informed fusion methodology for deformation detection using multi-sensor thermal data. A challenge with additive manufacturing (AM) is that abnormalities commonly occur due to rapid changes in the thermal gradient. Different non-destructive in-situ thermal sensors capture parts of the thermal history but are limited by the visible temperature spectrum and sensor field of view of the fabrication process. Various sensors mitigate problems with the loss of thermal history information; however, it brings forth challenges with integrating different data streams and the need to interpolate the internal thermal history. This study develops a thermal data-informed heat flux methodology that fills the gap in fusing numerical temperature approximation with data-driven knowledge of the surface of additive manufactured components. First, this study fuses infrared (IR) thermal data complexities during the AM process with the Goldak double ellipsoidal heat flux to model the energy input into the component. Second, a thermal physics-informed model input (PIMI) is created with thermal data-informed heat flux to capture internal thermal history. Lastly, a regression convolutional neural network (CNN) captures the relationship between the three-dimensional thermal gradient and the resulting surface deformation. The rapid thermal gradient formation and identification of deformation is a key step toward using thermal history data and machine learning to improve quality control in AM. The proposed surface deformation detection model achieved an mean squared error of 1.14 mm and an R2 of 0.89 in the case study when fabricating thin-walled structures.
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      Physics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303459
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    contributor authorZamiela, Christian
    contributor authorStokes, Ryan
    contributor authorTian, Wenmeng
    contributor authorDoude, Haley
    contributor authorPriddy, Matthew W.
    contributor authorBian, Linkan
    date accessioned2024-12-24T19:11:24Z
    date available2024-12-24T19:11:24Z
    date copyright5/21/2024 12:00:00 AM
    date issued2024
    identifier issn1087-1357
    identifier othermanu_146_8_081007.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303459
    description abstractThis work presents a physics-informed fusion methodology for deformation detection using multi-sensor thermal data. A challenge with additive manufacturing (AM) is that abnormalities commonly occur due to rapid changes in the thermal gradient. Different non-destructive in-situ thermal sensors capture parts of the thermal history but are limited by the visible temperature spectrum and sensor field of view of the fabrication process. Various sensors mitigate problems with the loss of thermal history information; however, it brings forth challenges with integrating different data streams and the need to interpolate the internal thermal history. This study develops a thermal data-informed heat flux methodology that fills the gap in fusing numerical temperature approximation with data-driven knowledge of the surface of additive manufactured components. First, this study fuses infrared (IR) thermal data complexities during the AM process with the Goldak double ellipsoidal heat flux to model the energy input into the component. Second, a thermal physics-informed model input (PIMI) is created with thermal data-informed heat flux to capture internal thermal history. Lastly, a regression convolutional neural network (CNN) captures the relationship between the three-dimensional thermal gradient and the resulting surface deformation. The rapid thermal gradient formation and identification of deformation is a key step toward using thermal history data and machine learning to improve quality control in AM. The proposed surface deformation detection model achieved an mean squared error of 1.14 mm and an R2 of 0.89 in the case study when fabricating thin-walled structures.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePhysics-Informed Approximation of Internal Thermal History for Surface Deformation Predictions in Wire Arc Directed Energy Deposition
    typeJournal Paper
    journal volume146
    journal issue8
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4065416
    journal fristpage81007-1
    journal lastpage81007-16
    page16
    treeJournal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 008
    contenttypeFulltext
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