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    Toward Sub-Surface Pore Prediction Capabilities for Laser Powder Bed Fusion Using Data Science 

    Source: Journal of Manufacturing Science and Engineering:;2021:;volume( 143 ):;issue: 007:;page 071016-1
    Author(s): Ertay, Deniz Sera; Kamyab, Shima; Vlasea, Mihaela; Azimifar, Zohreh; Ma, Thanh; Rogalsky, Allan D.; Fieguth, Paul
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Achieving defect-free parts is traditionally challenging in laser powder bed fusion (LPBF). The mechanical properties of additively manufactured parts are highly affected by their density; as such, research in defect ...
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