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Intelligent assembly system for mechanical products and key technology based on internet of things

Author

Listed:
  • Mingzhou Liu

    (Hefei University of Technology)

  • Jing Ma

    (Hefei University of Technology)

  • Ling Lin

    (Hefei University of Technology)

  • Maogen Ge

    (Hefei University of Technology)

  • Qiang Wang

    (Hefei University of Technology)

  • Conghu Liu

    (Hefei University of Technology)

Abstract

The Internet of Things (IoT) has a significant effect on the development of manufacturing technology. Therefore, according to the analysis of the challenges and opportunities faced by manufacturing industry, this study uses the assembly process of mechanical products as the research object and analyzes the characteristics of IoT-based manufacturing systems. To improve the interconnection, perception, efficiency, and intelligence of the assembly system, this study proposes the concept of IoT-enabled intelligent assembly system for mechanical products (IIASMP). The IIASMP framework, which is based on advanced techniques such as information and communication technology, sensor network, and radio-frequency identification, is then presented. Key technologies under this framework, including assembly resources identification, information interaction technology, multi-source data perception and fusion, intelligent assembly agent, and value-added data and dynamic self-adaptive optimization, are described. Finally, the current results of IIASMP are described in the case study. The proposed framework and methods aims to have an important reference value for applying the key technologies and be used widely in the intelligent manufacturing field.

Suggested Citation

  • Mingzhou Liu & Jing Ma & Ling Lin & Maogen Ge & Qiang Wang & Conghu Liu, 2017. "Intelligent assembly system for mechanical products and key technology based on internet of things," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 271-299, February.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0976-6
    DOI: 10.1007/s10845-014-0976-6
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    References listed on IDEAS

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    1. Ahlem Zayati & Frédérique Biennier & Mohamed Moalla & Youakim Badr, 2012. "Towards lean service bus architecture for industrial integration infrastructure and pull manufacturing strategies," Post-Print hal-02312543, HAL.
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    Cited by:

    1. Fernando E. Garcia-Muiña & Rocío González-Sánchez & Anna Maria Ferrari & Davide Settembre-Blundo, 2018. "The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company," Social Sciences, MDPI, vol. 7(12), pages 1-31, December.
    2. Kung-Jeng Wang & Diwanda Ageng Rizqi & Hong-Phuc Nguyen, 2021. "Skill transfer support model based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1129-1146, April.
    3. Bilal S. A. Alhayani & Haci llhan, 2021. "RETRACTED ARTICLE: Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 597-610, February.
    4. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    5. Yafeng Han & Tetiana Shevchenko & Bernard Yannou & Meisam Ranjbari & Zahra Shams Esfandabadi & Michael Saidani & Ghada Bouillass & Kseniia Bliumska-Danko & Guohou Li, 2023. "Exploring How Digital Technologies Enable a Circular Economy of Products," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    6. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.
    7. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    8. Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    9. Maja Turk & Marko Šimic & Miha Pipan & Niko Herakovič, 2022. "Multi-Criterial Algorithm for the Efficient and Ergonomic Manual Assembly Process," IJERPH, MDPI, vol. 19(6), pages 1-17, March.
    10. Wei Fang & Lianyu Zheng, 2020. "Shop floor data-driven spatial–temporal verification for manual assembly planning," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1003-1018, April.
    11. Xiang T. R. Kong & Hao Luo & George Q. Huang & Xuan Yang, 2019. "Industrial wearable system: the human-centric empowering technology in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2853-2869, December.

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