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    Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing 

    Source: Journal of Manufacturing Science and Engineering:;2020:;volume( 143 ):;issue: 004:;page 041011-1
    Author(s): Tian, Qi; Guo, Shenghan; Melder, Erika; Bian, Linkan; Guo, Weihong “Grace”
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures ...
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    Three-Dimensional X-Ray Computed Tomography Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused Parts 

    Source: Journal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 009:;page 91002-1
    Author(s): Xu, Boyang; Ouidadi, Hasnaa; Handel, Nicole Van; Guo, Shenghan
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical ...
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    Manufacturing Process Monitoring With Nonparametric Change-Point Detection in Automotive Industry 

    Source: Journal of Manufacturing Science and Engineering:;2019:;volume( 141 ):;issue: 007:;page 71013
    Author(s): Guo, Shenghan; Guo, Weihong (Grace); Abolhassani, Amir; Kalamdani, Rajeev; Puchala, Saumuy; Januszczak, Annette; Jalluri, Chandra
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process ...
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