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    Diffusion Generative Model-Based Learning for Smart Layer-Wise Monitoring of Additive Manufacturing 

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 006:;page 60903-1
    Author(s): Yangue, Emmanuel; Fullington, Durant; Smith, Owen; Tian, Wenmeng; Liu, Chenang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Despite the rapid adoption of deep learning models in additive manufacturing (AM), significant quality assurance challenges continue to persist. This is further emphasized by the limited availability of sample objects for ...
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    Adaptive Thermal History De-Identification for Privacy-Preserving Data Sharing of Directed Energy Deposition Processes 

    Source: Journal of Computing and Information Science in Engineering:;2025:;volume( 025 ):;issue: 003:;page 31006-1
    Author(s): Bappy, Mahathir Mohammad; Fullington, Durant; Bian, Linkan; Tian, Wenmeng
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In collaborative additive manufacturing (AM), sharing process data across multiple users can provide small- to medium-sized manufacturers (SMMs) with enlarged training data for part certification, facilitating accelerated ...
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    Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes 

    Source: Journal of Manufacturing Science and Engineering:;2023:;volume( 145 ):;issue: 005:;page 51004-1
    Author(s): Fullington, Durant; Bian, Linkan; Tian, Wenmeng
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning ...
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