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Industrial Internet of Things Solution for Real-Time Monitoring of the Additive Manufacturing Process

  • Mahmoud Salama
  • Ahmed ElkaseerEmail author
  • Mohamed Saied
  • Hazem Ali
  • Steffen Scholz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 852)

Abstract

Modern cyber manufacturing has been introduced into a broad range of manufacturing processes to ease their digital reconfigurability and enhance flexibility while retaining a high throughput of quality products. Such a system provides real-time data acquisition, enabling monitoring of the actual condition of the manufacturing process. The Industrial Internet of Things (IIoT) facilitates such real-time monitoring and optimization of the fabricating system, which reduces time necessary for maintenance with the possibility of almost instantaneously taking any necessary corrective measures with respect to either human to the machine/process from learned algorithms. In this research, an original IIoT approach has been proposed to monitor the process conditions, including nozzle temperature and filament breakage/runout, of the additive manufacturing process. In particular, concurrent multi-task IIoT communication was developed for a network of five nodes. This was implemented to ensure real time monitoring of the manufacturing process via multi sensors and empowered by an embedded software design. The proposed embedded software architecture offers a reliable solution to eliminate communication latency and provides real-time response to acquired information. It is worth emphasizing that the embedded software was designed so that it optimally exploits the very great potential of the hardware resources, with the ability to detect run-time issues in the nodes’ performance and re-try to address such issues to maintain a high capability networking communication. The designed architecture also offers auto-scaling throughput of the data transferred to the cloud to minimize the bandwidth.

Keywords

IIoT M2M Industrial informatics Industry 4.0 AM 

Notes

Acknowledgment

The authors would like to thank the European Commission for funding the PAM2 project under H2020-MSCA-ITN-2016 Program, Grant Agreement No 721383. The authors gratefully acknowledge the technical help provided by Eng. Saeed Mohsen, PhD student at ASU, Egypt.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahmoud Salama
    • 1
  • Ahmed Elkaseer
    • 2
    • 3
    Email author
  • Mohamed Saied
    • 1
  • Hazem Ali
    • 4
  • Steffen Scholz
    • 2
  1. 1.Faculty of EngineeringAin Shams UniversityCairoEgypt
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Faculty of EngineeringPort Said UniversityPort SaidEgypt
  4. 4.European X-Ray Free-Electron Laser Facility GmbHSchenefeldGermany

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