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2021 | OriginalPaper | Chapter

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

Authors : Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu, Xiyi Hang, Bingbing Li

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

The document delves into the innovative use of real-time object detection for monitoring 3D printers, a crucial aspect of modern manufacturing. By leveraging advanced computer vision models, the authors present a sophisticated system that can identify the positions of critical components and predict machine states with remarkable precision. The YOLO-based object detection model, trained on a meticulously curated dataset, demonstrates exceptional performance in real-time applications. The integration of this model with a filtering algorithm allows for continuous monitoring and prediction of machine states, significantly enhancing the efficiency and reliability of 3D printing processes. This work not only showcases the potential of AI in manufacturing but also paves the way for future advancements in smart connected worker systems.
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Metadata
Title
Real-Time Object Detection for Smart Connected Worker in 3D Printing
Authors
Shijie Bian
Tiancheng Lin
Chen Li
Yongwei Fu
Mengrui Jiang
Tongzi Wu
Xiyi Hang
Bingbing Li
Copyright Year
2021
Book
Computational Science – ICCS 2021

Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021

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

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