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    Electrical Discharge/Electrochemical Hybrid Machining Based on the Same Machine and Tool Electrode 

    Source: Journal of Micro and Nano-Manufacturing:;2020:;volume( 008 ):;issue: 001
    Author(s): Kishi, Ryoichiro; Yan, Jiwang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Electrical discharge machining (EDM) causes surface defects such as resolidified layer and microcracks, and a finishing process is usually needed to remove these defects. In this paper, a hybrid process was proposed where ...
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    Multiscale Surface Patterning of Zirconia by Picosecond Pulsed Laser Irradiation 

    Source: Journal of Micro and Nano-Manufacturing:;2020:;volume( 008 ):;issue: 001
    Author(s): Yamamuro, Yuka; Shimoyama, Tomotaka; Yamashita, Isao; Yan, Jiwang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Irradiation of yttria-stabilized zirconia (YSZ) was performed by a picosecond pulsed laser to investigate the possibility for multiscale surface patterning. Nanoscale laser-induced periodic surface structures (LIPSS) were ...
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    Femtosecond Pulsed Laser Machining of Fused Silica for Micro-Cavities With Sharp Corners 

    Source: Journal of Manufacturing Science and Engineering:;2024:;volume( 147 ):;issue: 001:;page 11007-1
    Author(s): Brinkmann, Moritz; Qian, Yongfeng; Huang, Hu; Yan, Jiwang
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Fused silica is an important material for applications requiring high temperature resistance, low thermal expansion coefficient, and excellent optical properties. The machining of micro-cavities on fused silica surfaces ...
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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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    DSpace software copyright © 2002-2015  DuraSpace
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