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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)


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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  1. Schaller, R.R.: Moore’s law: past, present and future. IEEE Spectrum 34(6), 52–59 (1997)

    Article  Google Scholar 

  2. Weinstein, B.G.: A computer vision for animal ecology. J. Anim. Ecol. 87(3), 533–545 (2018)

    Article  Google Scholar 

  3. Wang, Y., Zheng, P., Xun, X., Yang, H., Zou, J.: Production planning for cloud-based additive manufacturing–a computer vision-based approach. Robot. Comput.-Integr. Manuf. 58, 145–157 (2019)

    Article  Google Scholar 

  4. Pathak, A.R., Pandey, M., Rautaray, S., Pawar, K.: Assessment of object detection using deep convolutional neural networks. In: Bhalla, S., Bhateja, V., Chandavale, A.A., Hiwale, A.S., Satapathy, S.C. (eds.) Intelligent Computing and Information and Communication. AISC, vol. 673, pp. 457–466. Springer, Singapore (2018).

    Chapter  Google Scholar 

  5. Zhang, B., Jaiswal, P., Rai, R., Guerrier, P., Baggs, G.: Convolutional neural network-based inspection of metal additive manufacturing parts. Rapid Prototyping J. 25(3), 530–540 (2019)

    Article  Google Scholar 

  6. Li, J., Götvall, P., Provost, J., Åkesson, K.: Training convolutional neural networks with synthesized data for object recognition in industrial manufacturing. In: 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1544–1547 (2019)

    Google Scholar 

  7. Beschi, M., Villagrossi, E., Molinari Tosatti, L., Surdilovic, D.: Sensorless model-based object-detection applied on an underactuated adaptive hand enabling an impedance behavior. Robot. Comput.-Integr. Manuf. 46, 38–47 (2017)

    Google Scholar 

  8. Khodabandeh, M., Vahdat, A., Ranjbar, M., Macready, W.G.: A robust learning approach to domain adaptive object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  9. Anitha, R., Jayalakshmi, S.: A systematic hybrid smart region based detection (SRBD) method for object detection. In: 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 139–145 (2020)

    Google Scholar 

  10. Mehmood, F., Ullah, I., Ahmad, S., Kim, D.: Object detection mechanism based on deep learning algorithm using embedded IOT devices for smart home appliances control in cot. Journal of Ambient Intelligence and Humanized Computing (2019)

    Google Scholar 

  11. Hu, L., Ni, Q.: Iot-driven automated object detection algorithm for urban surveillance systems in smart cities. IEEE Internet of Things J. 5(2), 747–754 (2018)

    Article  Google Scholar 

  12. Sudharsan, B., Kumar, S.P., Dhakshinamurthy, R.: Ai vision: smart speaker design and implementation with object detection custom skill and advanced voice interaction capability. In: 2019 11th International Conference on Advanced Computing (ICoAC), pp. 97–102 (2019)

    Google Scholar 

  13. Wilson, G., et al.: Robot-enabled support of daily activities in smart home environments. Cognitive Syst. Res. 54, 258–272 (2019)

    Article  Google Scholar 

  14. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  15. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014)

    Google Scholar 

  16. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  17. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).

    Chapter  Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)

    Google Scholar 

  19. Redmon, J., Divvala, S., Girshick, R., Ali, F.: Unified, real-time object detection, You only look once (2016)

    Google Scholar 

  20. Chen, S.L., Lin, S.C., Huang, Y., Jen, C.W., Lin, Z.L., Su, S.F.: A vision-based dual-axis positioning system with yolov4 and improved genetic algorithms. In: 2020 Fourth IEEE International Conference on Robotic Computing (IRC), pp. 127–134 (2020)

    Google Scholar 

  21. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)

    Google Scholar 

  22. San Diego Pacific Research Platform University of California. Nautilus

    Google Scholar 

  23. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv (2018)

    Google Scholar 

  24. Lin, T.-Y., et al.: Microsoft coco: Common objects in context (2015)

    Google Scholar 

  25. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn (2018)

    Google Scholar 

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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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Correspondence to Bingbing Li .

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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.

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