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Cyber-Physical System and Big Data-Enabled Scheduling Optimization for Sustainable Machining

  • Y. C. Liang
  • X. Lu
  • S. Wang
  • W. D. LiEmail author
Chapter

Abstract

Modern manufacturing is challenged by customized, high-variety and low-volume job orders, dynamic ambient working conditions in shop floors, and stricter requirements on sustainability. Data-driven approach for manufacturing planning, control, and management will be a new research trend to tackle the challenge. In this research, based on in-process monitoring on energy consumption of machining processes and data analytics technologies, an innovative Cyber-physical system (CPS) and Big Data-enabled scheduling optimization system for sustainable computer numerical control (CNC) machining has been developed. This system is augmented with intelligent mechanisms for enhancing adaptability to condition dynamics in machining shop floors. The system consists of scheduling and re-scheduling functions. For scheduling, an artificial neural networks (ANNs)-based algorithm has been designed to establish energy models of components machined in a shop floor according to current working conditions of CNC machines. Based on the energy models, a fruit fly optimization (FFO) algorithm has been applied to generate a multi-objective optimized schedule. For re-scheduling, another ANNs-based algorithm has been developed to monitor the energy consumption of components during machining in real time. Scheduling optimization will be triggered to generate an updated schedule if there are significantly varying working conditions and re-scheduling adjustments are necessary. The system has been validated through deployment into a European machining company and industrial case studies to demonstrate technical innovations and the great potential of applicability in practice.

Keywords

Cyber-physical system Big Data Energy efficiency Scheduling optimization 

Notes

Acknowledgements

This research was carried out as a part of the Smarter and Cloudflow projects which are supported by the European Commission 7th Framework Programme under the grant agreement PEOPLE-2013-IAPP-610675 and FP7-2013-NMP-ICT-FOF- 609100.

References

  1. 1.
    O’Driscoll, E., & O’Donnell, G. (2013). Industrial power and energy metering—a state-of-the-art review. Journal of Cleaner Production, 41, 53–64.CrossRefGoogle Scholar
  2. 2.
    Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. (2011). Integrating energy efficiency performance in production management—gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19(6–7), 667–679.CrossRefGoogle Scholar
  3. 3.
    Li, W. D., & McMahon, C. A. (2007). A simulated annealing—based optimization approach for integrated process planning and scheduling. International Journal of Computer Integrated Manufacturing, 20(1), 80–95.CrossRefGoogle Scholar
  4. 4.
    Wang, S., Lu, X., Li, X., & Li, W. D. (2015). A systematic approach of process planning and scheduling optimization for sustainable machining. Journal of Cleaner Production, 87, 914–929.CrossRefGoogle Scholar
  5. 5.
    Gahm, C., Denz, F., Dirr, M., & Tuma, A. (2016). Energy-efficient scheduling in manufacturing companies: A review and research framework. European Journal of Operational Research, 248(3), 744–757.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Babiceanu, R., & Seker, R. (2016). Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81, 128–137.CrossRefGoogle Scholar
  7. 7.
    Fang, K., Uhan, N., Zhao, F., & Sutherland, J. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234–240.CrossRefGoogle Scholar
  8. 8.
    He, Y., Liu, F., Wu, T., Zhong, F., & Peng, B. (2011). Analysis and estimation of energy consumption for numerical control machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(2), 255–266.CrossRefGoogle Scholar
  9. 9.
    Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52, 462–471.CrossRefGoogle Scholar
  10. 10.
    Winter, M., Li, W., Kara, S., & Herrmann, C. (2014). Determining optimal process parameters to increase the eco-efficiency of grinding processes. Journal of Cleaner Production, 66, 644–654.CrossRefGoogle Scholar
  11. 11.
    Yan, J., Li, L., Zhao, F., Zhang, F., & Zhao, Q. (2016). A multi-level optimization approach for energy-efficient flexible flow shop scheduling. Journal of Cleaner Production, 137, 1543–1552.CrossRefGoogle Scholar
  12. 12.
    Tang, D., Dai, M., Salido, M., & Giret, A. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 81, 82–95.CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, 259–272.CrossRefGoogle Scholar
  14. 14.
    Xu, W., Shao, L., Yao, B., Zhou, Z., & Pham, D. (2016). Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing. Journal of Manufacturing Systems, 41, 86–101.CrossRefGoogle Scholar
  15. 15.
    Salido, M., Escamilla, J., Barber, F., & Giret, A. (2016). Rescheduling in job-shop problems for sustainable manufacturing systems. Journal of Cleaner Production (online).Google Scholar
  16. 16.
    Dai, J., Huang, J., Huang, S., Huang, B., & Liu, Y. (2011). HiTune: Dataflow-based performance analysis for big data cloud. In Proceedings of the 2011 USENIX ATC (pp. 87–100). Berkeley.Google Scholar
  17. 17.
    Nagorny, K., Colombo, A., & Schmidtmann, U. (2012). A service- and multi-agent-oriented manufacturing automation architecture. Computers in Industry, 63(8), 813–823.CrossRefGoogle Scholar
  18. 18.
    Loshin, D. (2014). Big data analytics. Burlington, NJ: Morgan Kaufmann Pub.Google Scholar
  19. 19.
    Noor, A. (2013). Putting big data to work. ASME Mechanical Engineering, 32–37.Google Scholar
  20. 20.
    Prabhu, N. (2013). Design and construction of an RFID-enabled infrastructure. Florida: CRC Press.Google Scholar
  21. 21.
    Chaplin, J., Bakker, O., de Silva, L., Sanderson, D., Kelly, E., Logan, B., et al. (2015). Evolvable assembly systems: A distributed architecture for intelligent manufacturing. IFAC-PapersOnLine, 48(3), 2065–2070.CrossRefGoogle Scholar
  22. 22.
    Liu, C., & Jiang, P. Y. (2016). A cyber-physical system architecture in shop floor for intelligent manufacturing. Procedia CIRP, 56, 372–377.CrossRefGoogle Scholar
  23. 23.
    Zhou, R., Nee, A. Y. C., & Lee, H. (2009). Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems. International Journal of Production Research, 47(11), 2903–2920.CrossRefGoogle Scholar
  24. 24.
    Adibi, M., Zandieh, M., & Amiri, M. (2010). Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Systems with Applications, 37(1), 282–287.CrossRefGoogle Scholar
  25. 25.
    Li, W. D., Ong, S. K., & Nee, A. Y. C. (2006). Integrated and collaborative product development environment—Technologies and implementation. Singapore: World Scientific Publisher.Google Scholar
  26. 26.
    Mausser, H. (2006). Normalization and other topics in multi-objective optimization. In Proceedings of the Fields–MITACS Industrial Problems Workshop (pp. 59–101).Google Scholar
  27. 27.
    Pan, W. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.CrossRefGoogle Scholar
  28. 28.
    Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A Revolution that will transform how we live. Boston: Houghton Miffin Harcourt Publishing Company.Google Scholar
  29. 29.
    Zheng, X., Wang, L., & Wang, S. (2014). A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowledge-Based Systems, 57, 95–103.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering, Environment and ComputingCoventry UniversityCoventryUK

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