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Real-Time Object Detection for Smart Connected Worker in 3D Printing

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

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Abstract

IoT and smart systems have been introduced into the advanced manufacturing, especially 3D printing with the trend of the fourth industrial revolution. The rapid development of computer vision and IoT devices in recent years has led the fruitful direction to the development of real-time machine state monitoring. In this study, computer vision technology was adopted into the Smart Connected Worker (SCW) system with the use case of 3D printing. Specifically, artificial intelligence (AI) models were investigated instead of discrete labor-intensive methods to monitor the machine state and predict the errors and risks for the advanced manufacturing. The model achieves accurate supervision in real-time for twenty-four hours a day, which can reduce human resource costs significantly. At the same time, the experiments demonstrate the feasibility of adopting AI technology to more aspects of the advanced manufacturing.

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Acknowledgement

This research was mainly supported by the Technical Roadmap Project “Establishing Smart Connected Workers Infrastructure for Enabling Advanced Manufacturing: A Pathway to Implement Smart Manufacturing for Small to Medium Sized Enterprises (SMEs)” funded by the Clean Energy Smart Manufacturing Innovation Institute (CESMII) sponsored through the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (Award Number DOE: DE-EE0007613). This work was also supported by the project “Autonomy Research Center for STEAHM” sponsored through the U.S. NASA Minority University Research and Education Project (MUREP) Institutional Research Opportunity (MIRO) program (Award Number: 80NSSC19M0200).

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Authors and Affiliations

  1. Autonomy Research Center for STEAHM, California State University, Northridge, CA, 91324, USA

    Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu & Bingbing Li

  2. Department of Manufacturing Systems Engineering and Management, California State University, Northridge, CA, 91330, USA

    Yongwei Fu & Bingbing Li

  3. Department of Mathematics, University of California, Los Angeles, CA, 90095, USA

    Shijie Bian & Chen Li

  4. Department of Electrical and Computer Engineering, California State University, Northridge, CA, 91330, USA

    Xiyi Hang

Authors
  1. Shijie Bian
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  2. Tiancheng Lin
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  3. Chen Li
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  4. Yongwei Fu
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  5. Mengrui Jiang
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  6. Tongzi Wu
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  7. Xiyi Hang
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  8. Bingbing Li
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Corresponding author

Correspondence to Bingbing Li .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Bian, S. et al. (2021). Real-Time Object Detection for Smart Connected Worker in 3D Printing. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_42

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77969-6

  • Online ISBN: 978-3-030-77970-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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