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    Bridging Data Gaps: A Federated Learning Approach to Heat Emission Prediction in Laser Powder Bed Fusion 

    Source: Journal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 010:;page 101002-1
    Author(s): Lei, Rong; Guo, Y. B.; Yan, Jiwang; Guo, Weihong “Grace”
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Deep learning has impacted defect prediction in additive manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogeneous ...
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    Physics-Guided Long Short-Term Memory Networks for Emission Prediction in Laser Powder Bed Fusion 

    Source: Journal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 001:;page 11006-1
    Author(s): Lei, Rong; Guo, Y. B.; Guo, Weihong “Grace”
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Powder bed fusion (PBF) is an additive manufacturing process in which laser heat liquefies blown powder particles on top of a powder bed, and cooling solidifies the melted powder particles. During this process, the laser ...
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