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    A Hybrid Deep Generative Network for Pore Morphology Prediction in Metal Additive Manufacturing

    Source: Journal of Manufacturing Science and Engineering:;2023:;volume( 145 ):;issue: 007::page 71005-1
    Author:
    Song, Zheren
    ,
    Wang, Xinming
    ,
    Gao, Yuanyuan
    ,
    Son, Junbo
    ,
    Wu, Jianguo
    DOI: 10.1115/1.4057012
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Metal additive manufacturing (AM) has been receiving unprecedented attention for its transformational role in extending the AM materials from polymers to various metals. However, various quality issues, especially porosity, significantly impacts the mechanical properties and fatigue life of the final products, which imposes barriers for the widespread adoption of metal AM processes. In this study, we use the deep learning (DL) techniques to comprehensively investigate the relationships between pore microstructure and processing parameters. Specifically, a novel hybrid deep generative prediction network (HDGPN) that leverages both variational autoencoder and generative adversarial network is proposed to characterize the complex pore microstructure with in-depth representations and predict pore morphology under arbitrary processing parameters. By visualizing the predicted pore morphology, the complicated interaction dynamics between the processing parameters and pore microstructure are directly revealed, which may guide the optimization of metal AM manufacturing processes to fabricate defect-free products. A case study of a selective laser melting (SLM) process is conducted to validate the proposed modeling and prediction framework.
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      A Hybrid Deep Generative Network for Pore Morphology Prediction in Metal Additive Manufacturing

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4294749
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    contributor authorSong, Zheren
    contributor authorWang, Xinming
    contributor authorGao, Yuanyuan
    contributor authorSon, Junbo
    contributor authorWu, Jianguo
    date accessioned2023-11-29T19:25:35Z
    date available2023-11-29T19:25:35Z
    date copyright3/15/2023 12:00:00 AM
    date issued3/15/2023 12:00:00 AM
    date issued2023-03-15
    identifier issn1087-1357
    identifier othermanu_145_7_071005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294749
    description abstractMetal additive manufacturing (AM) has been receiving unprecedented attention for its transformational role in extending the AM materials from polymers to various metals. However, various quality issues, especially porosity, significantly impacts the mechanical properties and fatigue life of the final products, which imposes barriers for the widespread adoption of metal AM processes. In this study, we use the deep learning (DL) techniques to comprehensively investigate the relationships between pore microstructure and processing parameters. Specifically, a novel hybrid deep generative prediction network (HDGPN) that leverages both variational autoencoder and generative adversarial network is proposed to characterize the complex pore microstructure with in-depth representations and predict pore morphology under arbitrary processing parameters. By visualizing the predicted pore morphology, the complicated interaction dynamics between the processing parameters and pore microstructure are directly revealed, which may guide the optimization of metal AM manufacturing processes to fabricate defect-free products. A case study of a selective laser melting (SLM) process is conducted to validate the proposed modeling and prediction framework.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Hybrid Deep Generative Network for Pore Morphology Prediction in Metal Additive Manufacturing
    typeJournal Paper
    journal volume145
    journal issue7
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4057012
    journal fristpage71005-1
    journal lastpage71005-15
    page15
    treeJournal of Manufacturing Science and Engineering:;2023:;volume( 145 ):;issue: 007
    contenttypeFulltext
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