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    Diagnosing Multistage Manufacturing Processes With Engineering Driven Factor Analysis Considering Sampling Uncertainty 

    Source: Journal of Manufacturing Science and Engineering:;2013:;volume( 135 ):;issue: 004:;page 41020
    Author(s): Liu, Jian; Jin, Jionghua
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A new engineeringdriven factor analysis (EDFA) method has been developed to assist the variation source identification for multistage manufacturing processes (MMPs). The proposed method investigated how to fully utilize ...
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    Online Eccentricity Monitoring of Seamless Tubes in Cross Roll Piercing Mill 

    Source: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 002:;page 21007
    Author(s): Guo, Weihong; Chen, Rui; Jin, Jionghua (Judy)
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Wallthickness eccentricity is a major dimensional deviation problem in seamless steel tube production. Although eccentricity is mainly caused by abnormal process conditions in the crossroll piercing mill, most seamless ...
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    Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding 

    Source: Journal of Manufacturing Science and Engineering:;2019:;volume( 141 ):;issue: 008:;page 81001
    Author(s): Guo, Weihong (Grace); Jin, Jionghua (Judy); Jack Hu, S.
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, and quick diagnosis of fault ...
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    Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing 

    Source: Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 010:;page 101002
    Author(s): Shao, Chenhui; Jin, Jionghua (Judy); Jack Hu, S.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, ...
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    Quantifying Dispersion of Nanoparticles in Polymer Nanocomposites Through Transmission Electron Microscopy Micrographs 

    Source: Journal of Micro and Nano-Manufacturing:;2014:;volume( 002 ):;issue: 002:;page 21008
    Author(s): Li, Xiaodong; Zhang, Hui; Jin, Jionghua; Huang, Dawei; Qi, Xiaoying; Zhang, Zhong; Yu, Dan
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The property of nanocomposites is crucially affected by nanoparticle dispersion. Transmission electron microscopy (TEM) is the “golden standardâ€‌ in nanoparticle dispersion characterization. A TEM Micrograph is a ...
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    Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling 

    Source: Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 001:;page 11014
    Author(s): Shao, Chenhui; Ren, Jie; Wang, Hui; Jin, Jionghua (Judy); Hu, S. Jack
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-of-the-art approaches for surface shape ...
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    Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries 

    Source: Journal of Manufacturing Science and Engineering:;2016:;volume( 138 ):;issue: 005:;page 51005
    Author(s): Shao, Chenhui; Hyung Kim, Tae; Jack Hu, S.; (Judy) Jin, Jionghua; Abell, Jeffrey A.; Patrick Spicer, J.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, ...
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    MVGCN: Multi-View Graph Convolutional Neural Network for Surface Defect Identification Using Three-Dimensional Point Cloud 

    Source: Journal of Manufacturing Science and Engineering:;2022:;volume( 145 ):;issue: 003:;page 31004-1
    Author(s): Wang, Yinan; Sun, Wenbo; Jin, Jionghua (Judy); Kong, Zhenyu (James); Yue, Xiaowei
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been ...
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    Predicting Subjective Responses From Human Motion: Application to Vehicle Ingress Assessment 

    Source: Journal of Manufacturing Science and Engineering:;2016:;volume( 138 ):;issue: 006:;page 61001
    Author(s): Masoud, Hadi I.; Reed, Matthew P.; Paynabar, Kamran; Wang, Nanxin; (Judy) Jin, Jionghua; Wan, Jian; Kozak, Ksenia K.; Gomez-Levi, Gianna
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The ease of entering a car is one of the important ergonomic factors that car manufacturers consider during the process of car design. This has motivated many researchers to investigate factors that affect discomfort during ...
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