Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Aguilar-Ponce, R., Kumar, A., Tecpanecatl-Xihuitl, J. L., & Bayoumi, M. (2007). A network of sensor-based framework for automated visual surveillance. Journal of Network and Computer Applications, 30(3), 1244–1271.

    Article  Google Scholar 

  • Al-Habaibeh, A., & Parkin, R. (2003). An autonomous low-cost infrared system for the online monitoring of manufacturing processes using novelty detection. International Journal of Advanced Manufacturing Technology, 22(3–4), 249–258.

    Article  Google Scholar 

  • Atlas, L. E., Bernard, G. D., & Narayanan, S. B. (1996). Applications of time-frequency analysis to signals from manufacturing and machine monitoring sensors. Proceedings of the IEEE, 84(9), 1319–1329.

    Article  Google Scholar 

  • Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.

    Article  Google Scholar 

  • Bargelis, A., Hoehne, G., & Cesnulevicius, A. (2004). Intelligent functional model for costs minimization in hybrid manufacturing systems. Informatica, 15(1), 3–22.

    Google Scholar 

  • Beach, R., Muhlemann, A. P., Price, D. H. R., Paterson, A., & Sharp, J. A. (2000). A review of manufacturing flexibility. European Journal of Operational Research, 122(1), 41–57.

    Article  Google Scholar 

  • Changfeng, Y., Dinghua, Z., Wenli, P., & Kun, B. (2006). Research on resources optimisation deployment model and algorithm for collaborative manufacturing process. International Journal of Production Research, 44(16), 3279–3301.

    Article  Google Scholar 

  • Chaves, L. W., & Nochta, Z. (2011). Breakthrough towards the internet of things. In D. C. Ranasinghe, Q. Z. Sheng & S. Zeadally (Eds.), Unique Radio Innovation For The 21st Century (pp. 25–38). Berlin: Springer. http://link.springer.com/chapter/10.1007/978-3-642-03462-6_2 .

  • Chen, R. S., & Tu, M. A. (2009). Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology. Expert Systems with Applications, 36(4), 7581–7593.

    Article  Google Scholar 

  • Chen, K. Y. (2012). Cell controller design for RFID based flexible manufacturing systems. International Journal of Computer Integrated Manufacturing, 25(1), 35–50.

    Article  Google Scholar 

  • Chiu, S. W. (2008). An optimization problem of manufacturing systems with stochastic machine breakdown and rework process. Applied Stochastic Models in Business and Industry, 24(3), 203–219.

    Article  Google Scholar 

  • Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.

    Article  Google Scholar 

  • Chryssolouris, G., Mavrikios, D., Papakostas, N., Mourtzis, D., Michalos, G., & Georgoulias, K. (2009). Digital manufacturing: History, perspectives, and outlook. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(5), 451–462.

    Article  Google Scholar 

  • Cutting-Decelle, A. F., Young, R. I., Michel, J. J., Grangel, R., Le Cardinal, J., & Bourey, J. P. (2007). ISO 15531 MANDATE: A product-process-resource based approach for managing modularity in production management. Concurrent Engineering, 15(2), 217–235.

    Article  Google Scholar 

  • Dai, Q., Zhong, R., Huang, G. Q., Qu, T., Zhang, T., & Luo, T. (2012). Radio frequency identification-enabled real-time manufacturing execution system: A case study in an automotive part manufacturer. International Journal of Computer Integrated Manufacturing, 25(1), 51–65.

    Article  Google Scholar 

  • Deshpande, S., & Cagan, J. (2004). An agent based optimization approach to manufacturing process planning. Journal of Mechanical Design, 126(1), 46–55.

    Article  Google Scholar 

  • Drath, R., Luder, A., Peschke, J., & Hundt, L. (2008). AutomationML-the glue for seamless automation engineering. In IEEE international conference on emerging technologies and factory automation, 2008. ETFA 2008. (pp. 616–623). doi:10.1109/ETFA.2008.4638461.

  • Edinbarough, I., Balderas, R., & Bose, S. (2005). A vision and robot based online inspection monitoring system for electronic manufacturing. Computers in Industry, 56(8–9), 986–996.

    Article  Google Scholar 

  • Giusto, D., Lera, A., Morabito, G., & Atzori, L. (2010). The Internet of Things. Berlin: Springer.

    Book  Google Scholar 

  • Golnabi, H., & Asadpour, A. (2007). Design and application of industrial machine vision systems. Robotics and Computer-Integrated Manufacturing, 23(6), 630–637.

    Article  Google Scholar 

  • Guo, Q., & Zhang, M. (2008). Research on intelligent manufacturing system based on multi-agent. In C. Xiong, H. Liu, Y. Huang & Y. Xiong (Eds.), Intelligent Robotics and Applications (pp. 829–838). Berlin: Springer. http://link.springer.com/chapter/10.1007/978-3-540-88518-4_89.

  • Guo, Q., & Zhang, M. (2009). A novel approach for multi-agent-based intelligent manufacturing system. Information Sciences, 179(18), 3079–3090.

    Article  Google Scholar 

  • Huang, C. Y. (2002). Distributed manufacturing execution systems: A workflow perspective. Journal of Intelligent Manufacturing, 13(6), 485–497.

    Article  Google Scholar 

  • Jain, A., Jain, P. K., Chan, F. T., & Singh, S. (2013). A review on manufacturing flexibility. International Journal of Production Research, 51(19), 5946–5970.

    Article  Google Scholar 

  • Jardim-Goncalves, R., Sarraipa, J., Agostinho, C., & Panetto, H. (2011). Knowledge framework for intelligent manufacturing systems. Journal of Intelligent Manufacturing, 22(5), 725–735.

    Article  Google Scholar 

  • Jiang, C. Y., Wang, J. B., & Jiang, J. J. (2006). Research on analyzing and coding technology of manufacturing information resource based on granularity-structure. In C. Jiang, G. Liu, D. Zhang & X. Xu (Eds.), Materials science forum (Vol. 532, pp. 1080–1083). Pune: Trans Tech Publications. http://www.scientific.net/MSF.532-533.1080.

  • Kletti, J. (2007). Manufacturing execution systems-MES. Berlin: Springer.

    Book  Google Scholar 

  • Kumar, P., Barua, P., & Gaindhar, J. (2000). Quality optimization (multi-characteristics) through Taguchi’s technique and utility concept. Quality and Reliability Engineering International, 16(6), 475–485.

    Article  Google Scholar 

  • Lee, C. S., Jiang, C. C., & Hsieh, T. C. (2006). A genetic fuzzy agent using ontology model for meeting scheduling system. Information Sciences, 176(9), 1131–1155.

    Article  Google Scholar 

  • Liu, W., Zheng, L., Sun, D., Liao, X., Zhao, M., Su, J., et al. (2012). RFID-enabled real-time production management system for Loncin motorcycle assembly line. International Journal of Computer Integrated Manufacturing, 25(1), 86–99.

    Article  Google Scholar 

  • Luck, M., & d’Inverno, M. (2001). A conceptual framework for agent definition and development. The Computer Journal, 44(1), 1–20.

    Article  Google Scholar 

  • Madejski, J. (2007). Survey of the agent-based approach to intelligent manufacturing. Journal of Achievements in Materials and Manufacturing Engineering, 21(1), 67–70.

    Google Scholar 

  • Makris, S., Michalos, G., & Chryssolouris, G. (2012). RFID driven robotic assembly for random mix manufacturing. Robotics and Computer-Integrated Manufacturing, 28(3), 359–365.

    Article  Google Scholar 

  • McFarlane, D., Sarma, S., Chirn, J. L., Wong, C. Y., & Ashton, K. (2003). Auto-ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365–376.

    Article  Google Scholar 

  • Michalos, G., Makris, S., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2010). Automotive assembly technologies review: Challenges and outlook for a flexible and adaptive approach. CIRP Journal of Manufacturing Science and Technology, 2(2), 81–91.

    Article  Google Scholar 

  • Mitchell, H. B. (2007). An introduction. In MultiSensor data fusion (pp. 3–13). Berlin: Springer.

  • Monostori, L., & Prohaszka, J. (1993). A step towards intelligent manufacturing: Modelling and monitoring of manufacturing processes through artificial neural networks. CIRP Annals-Manufacturing Technology, 42(1), 485–488.

    Article  Google Scholar 

  • Nasr, E. S. A., & Kamrani, A. K. (2008). Intelligent design and manufacturing. In A. K. Kamrani & E. S. Abouel Nasr (Eds.), Collaborative engineering (pp. 103–125). New York: Springer. http://link.springer.com/chapter/10.1007/978-0-387-47321-5_6.

  • Nearchou, A. C., & Omirou, S. L. (2006). Differential evolution for sequencing and scheduling optimization. Journal of Heuristics, 12(6), 395–411.

    Article  Google Scholar 

  • Oztemel, E. (2010). Intelligent manufacturing systems. In L. Benyoucef & B. Grabot (Eds.), Artificial intelligence techniques for networked manufacturing enterprises management (pp. 1–41). London: Springer. http://link.springer.com/chapter/10.1007/978-0-387-47321-5_6.

  • Oztemel, E., & Tekez, E. K. (2009). A general framework of a reference model for intelligent integrated manufacturing systems (REMIMS). Engineering Applications of Artificial Intelligence, 22(6), 855–864.

    Article  Google Scholar 

  • Qu, T., Yang, H., Huang, G. Q., Zhang, Y., Luo, H., & Qin, W. (2012). A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. Journal of Intelligent Manufacturing, 23(6), 2343–2356.

    Article  Google Scholar 

  • Ruiz, N., Giret, A., Botti, V., & Feria, V. (2011). Agent-supported simulation environment for intelligent manufacturing and warehouse management systems. International Journal of Production Research, 49(5), 1469–1482.

    Article  Google Scholar 

  • Sabar, M., Montreuil, B., & Frayret, J.-M. (2012). An agent-based algorithm for personnel shift-scheduling and rescheduling in flexible assembly lines. Journal of Intelligent Manufacturing, 23(6), 2623–2634.

    Article  Google Scholar 

  • Saenz de Ugarte, B., Artiba, A., & Pellerin, R. (2009). Manufacturing execution system—a literature review. Production Planning and Control, 20(6), 525–539.

    Article  Google Scholar 

  • Silvestre, J., & Sempere, V. (2007). An architecture for flexible scheduling in Profibus networks. Computer Standards & Interfaces, 29(5), 546–560.

    Article  Google Scholar 

  • Sung, W. T., & Hsu, Y. C. (2011). Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBee. Expert Systems with Applications, 38(4), 4522–4529.

    Article  Google Scholar 

  • Thiesse, F., Floerkemeier, C., Harrison, M., Michahelles, F., & Roduner, C. (2009). Technology, standards, and real-world deployments of the EPC network. IEEE Internet Computing, 13(2), 36–43.

    Article  Google Scholar 

  • Trappey, A. J., Lu, T.-H., & Fu, L.-D. (2009). Development of an intelligent agent system for collaborative mold production with RFID technology. Robotics and Computer-Integrated Manufacturing, 25(1), 42–56.

    Article  Google Scholar 

  • Tseng, M. C., & Lin, W. Y. (2001). Mining generalized association rules with multiple minimum supports. In Y. Kambayashi, W. Winiwarter & M. Arikawa (Eds.), Data warehousing and knowledge discovery (pp. 11–20). Berlin, Springer. http://link.springer.com/chapter/10.1007/3-540-44801-2_2.

  • Tu, M., Lin, J. H., Chen, R. S., Chen, K. Y., & Jwo, J. S. (2009). Agent-based control framework for mass customization manufacturing with UHF RFID technology. Ieee Systems Journal, 3(3), 343–359.

  • Wang, X. Z., & McGreavy, C. (1998). Automatic classification for mining process operational data. Industrial & Engineering Chemistry Research, 37(6), 2215–2222. doi:10.1021/ie970620h.

    Article  Google Scholar 

  • Youssef, S. M., & Salem, R. M. (2007). Automated barcode recognition for smart identification and inspection automation. Expert Systems with Applications, 33(4), 968–977.

    Article  Google Scholar 

  • Zayati, A., Biennier, F., Moalla, M., & Badr, Y. (2012). Towards lean service bus architecture for industrial integration infrastructure and pull manufacturing strategies. Journal of Intelligent Manufacturing, 23(1), 125–139.

    Article  Google Scholar 

  • Zhang, H. P., Gen, M. S., & Seo, Y. H. (2006). An effective coding approach for multiobjective integrated resource selection and operation sequences problem. Journal of Intelligent Manufacturing, 17(4), 385–397.

  • Zhang, Y., Huang, G. Q., Qu, T., & Ho, O. (2010). Agent-based workflow management for RFID-enabled real-time reconfigurable manufacturing. International Journal of Computer Integrated Manufacturing, 23(2), 101–112.

    Article  Google Scholar 

  • Zhang, Y., Jiang, P., Huang, G., Qu, T., Zhou, G., & Hong, J. (2012). RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises. Journal of Intelligent Manufacturing, 23(6), 2357–2366.

    Article  Google Scholar 

  • Zijm, W. (2000). Towards intelligent manufacturing planning and control systems. OR-Spektrum, 22(3), 313–345.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the financial support of the National Science Foundation of China (51375134) and National Basic Research Program of China (973 Program #2011CB013406).

Author information

Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Hefei University of Technology, 193 Tunxi Road, Hefei, 23009, Anhui, China

    Mingzhou Liu, Jing Ma, Ling Lin, Maogen Ge, Qiang Wang & Conghu Liu

Authors
  1. Mingzhou Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maogen Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Conghu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Ma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, M., Ma, J., Lin, L. et al. Intelligent assembly system for mechanical products and key technology based on internet of things. J Intell Manuf 28, 271–299 (2017). https://doi.org/10.1007/s10845-014-0976-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0976-6

Keywords

Search

Navigation