SpringerLink
Log in

Simulation based energy-resource efficient manufacturing integrated with in-process virtual management

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the availability of advanced technological innovation has created more complex manufacturing systems that involve a large variety of processes and machines serving different functions. To extend the limited knowledge on energy-efficient scheduling, the research presented in this paper attempts to model the production schedule at an operation process by considering the balance of energy consumption reduction in production, production work flow (productivity) and quality. An innovative systematic approach to manufacturing energy-resource efficiency is proposed with the virtual simulation as a predictive modelling enabler, which provides real-time manufacturing monitoring, virtual displays and decision-makings and consequentially an analytical and multidimensional correlation analysis on interdependent relationships among energy consumption, work flow and quality errors. The regression analysis results demonstrate positive relationships between the work flow and quality errors and the work flow and energy consumption. When production scheduling is controlled through optimization of work flow, quality errors and overall energy consumption, the energy-resource efficiency can be achieved in the production. Together, this proposed multidimensional modelling and analysis approach provides optimal conditions for the production scheduling at the manufacturing system by taking account of production quality, energy consumption and resource efficiency, which can lead to the key competitive advantages and sustainability of the system operations in the industry.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. DUFLOU J R, SUTHERLAND J W, DORNFIELD D, et al. Towards energy and resource efficient manufacturing: A processes and systems approach[J]. CIRP Annal—Manufacturing Technology, 2012, 61: 587–609.

    Article  Google Scholar 

  2. ALLWOOD J M, CULLEN J M. Sustainable materials: With both eyes open[M]. UIT Cambridge, Cambridge, 2012.

    Google Scholar 

  3. BEY N, HAUSCHILD M Z, MCALOONE T C. Drivers and barriers for implementation of environmental strategies in manufacturing companies[J]. CIRP Annals-Manufacturing Technology, 2013, 62: 43–46.

    Article  Google Scholar 

  4. GERETTI M, TAISCH M. Sustainable manufacturing: Trends and research challenges[J]. Production Planning & Control: The Management of Operations, 2012, 23: 83–104.

    Article  Google Scholar 

  5. RENTSCH R, HEINZEL C, BRINKSMEIER E. Artificial intelligence for an energy and resource efficient manufacturing chain design and operation[J]. Procedia CIRP, 2015, 33: 139–144.

    Article  Google Scholar 

  6. GIRET A, TRENTESAUX D, PRABHU V. Sustainability in manufacturing operations scheduling: A state of the art review[J]. Journal of Manufacturing Systems, 2015, 37: 126–140.

    Article  Google Scholar 

  7. TRENTESAUX D, PRABHU V. Sustainability in manufacturing operations scheduling: stakes, approaches and trends[M]//GRABOT B, VALLESPIR B, GOMES S, et al eds. Adv Prod Manage SystInnovKnowl Based Prod Manage Glob—Local World. Springer, Heidelberg, 2014: 106–113.

  8. FANG K, UHAN N, ZHAO F, et al. A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction[J]. Journal of Manufacturing Systems, 2011, 30: 234–240.

    Article  Google Scholar 

  9. PRABHU V V, JEON H W, TAISCH M. Simulation modelling of energy dynamics in discrete manufacturing systems[M]//Service orientation in holonic and multi agent manufacturing and robotics, Springer, Heidelberg, 2013.

    Book  Google Scholar 

  10. HERRMANN C, THIEDE S. Process chain simulation to foster energy efficiency in manufacturing[J]. CIRP-Journal of ManufacturingScience and Technology, 2009, 1: 221–229.

    Article  Google Scholar 

  11. THIEDE S. Energy efficiency in manufacturing systems[M]. Springer, Heidelberg, 2012.

    Book  Google Scholar 

  12. GUTOWSKI T, DAHMUS J, THIRIEZ A. Electrical Energy Requirements for Manufacturing Processes[C]//Proceedings of 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium, May 31–June 2, 2006, 31: 623–638.

    Google Scholar 

  13. DEVOLDERE T, DEWULF W, DEPREZ W, et al. Improvement potential for energy consumption in discretepart production machines[C]//Proceedings of the 14th CIRP Conference on Life Cycle Engineering,Tokyo, Japan, 2007: 311–316.

    Google Scholar 

  14. PACH C, BERGER T, SALLEZ Y, et al. Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields[J]. Computers in Industry, 2014, 65: 434–448.

    Article  Google Scholar 

  15. BABU CA, ASHOK S. Peak load management in electrolytic process industries[J]. IEEE Transactions on Power Systems, 2008, 23: 399–405.

    Article  Google Scholar 

  16. NGHIEM T, BEHL M, PAPPAS G J, et al. Green scheduling: Scheduling of control systems for peak power reduction[C]//Green Computing Conference and Workshops (IGCC) International, 2011: 1–8.

    Google Scholar 

  17. BRUZZONE A A G, ANGHINOLFI D, PAOLUCCI M. Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops[J]. CIRP Annals-Manufacturing Technology, 2012, 61: 459–462.

    Article  Google Scholar 

  18. KARNOUSKOS S, COLUMBO A W, LASTRA J L M, et al. Towards the energy efficient future energy[C]//7th IEEE International Conference on Industrial Informatics INDIN, 2009: 367–371.

    Google Scholar 

  19. MORI M, FUJISHIMA M, INAMASU Y, et al. A study on energy efficiency improvement for machine tools[J]. CIRP Annals- Manufacturing Technology, 2011, 60: 145–148.

    Article  Google Scholar 

  20. BI ZM, WANG, L. Optimization of machining processes from the perspective of energy consumption: A case study[J]. Journal of Manufacturing Systems, 2012, 31: 420–428.

    Article  Google Scholar 

  21. WANG Q, LIU F, WANG X. Multi-objective optimization of machining parameters considering energy consumption[J]. International Journal of Advance Manufacturing Technology, 2014, 71: 1133–1142.

    Article  Google Scholar 

  22. DEVOLDERE T, DEWULF W, DEPREZ W, et al. Improvement potential for energy consumption in discrete part production machines[M]//Advances in life cycle engineering for sustainable manufacturing businesses. Springer, London, 2007.

    Book  Google Scholar 

  23. MATI Y, XIE X. A polynomial algorithm for a two-job shop scheduling problem with routing flexibility[C]//Robotics and automation Proceedings ICRA03 IEEE International Conference, 2003, 1: 157–162.

    Google Scholar 

  24. VALLADA E, RUIZ R, MINELLA G. Minimising total tardiness in the m-machine flowshop problem: A review and evaluation of heuristics and metaheuristics[J]. Computers & Operations Research, 2008, 35: 1350–1373.

    Article  MATH  Google Scholar 

  25. MARIK V, MCFARLANE D. Industrial adoption of agent-based technologies[J]. IEEE Intelligent Systems, 2005, 1: 27–35.

    Article  Google Scholar 

  26. LEE J H, KIM C O. Multi-agent systems applications in manufacturing systems and supply chain management: A review paper[J]. International Journal of Production Research, 2008, 46: 233–265.

    Article  MATH  Google Scholar 

  27. SHEN W, HAO Q, YOON H J, et al. Applications of agent-based systems in intelligent manufacturing: An updated review[J]. Advanced Engineering Informatics, 2006, 20: 415–431.

    Article  Google Scholar 

  28. KUSTER T, LUTZENBERGER M, FREUND D, et al. Distributed optimization of energy costs in manufacturing using multi-agent system technology[C]//Proceedings of the 2nd International Conference on Smart Grids Green Communications and IT Energy-aware Technologies, Maho Beach, St. Maarten, 2012.

    Google Scholar 

  29. KATCHASUWANMANEE, K, BATEMAN R, CHENG K. Development of theenergy-smartproductionmanagementsystem (e-ProMan): A big data drivenapproachanalysis and optimization[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2015, 230(5): 972–978.

    Article  Google Scholar 

  30. NOUREDDINE A, ROUVOY R, SEINTURIER L. A review of energy measurement approaches[J]. ACM SIGOPS Operating Systems Review, 2013, 47: 42–49.

    Article  Google Scholar 

  31. LEACH R. Fundamental principles of engineering nanometrology[M]. Elsevier, Philadelphia, PA, 2014.

    Google Scholar 

  32. CHENG K, BATEMANR J. e-Manufacturing: characteristics, applications and potentials[J]. Progress in Natural Science, 2008, 18: 1323–1328.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Engineering, Design and Physical Sciences, Brunel University, London, UB8 3PH, UK

    Kanet Katchasuwanmanee & Kai Cheng

  2. Faculty of Engineering, Environment and Computing, Coventry University, Coventry, CV1 5FB, UK

    Richard Bateman

Authors
  1. Kanet Katchasuwanmanee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Bateman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Cheng.

Additional information

Supported by the EU 7th Framework ICT Programme under EuroEnergest Project (Contract No. 288102)

KATCHASUWANMANEE Kanet is currently a PhD candidate in manufacturing engineering at Brunel University, London, UK. His research interests include energy efficiency and energy management in manufacturing system.

CHENG Kai is currently a professor of manufacturing engineering at Brunel University, London, UK. His main research interests include design of high precision machines, ultraprecision and micro machining, multiscale multi-physics based design and analysis, smart tooling and smart machining and sustainable manufacturing systems.

BATEMAN Richard is currently a senior lecturer at Coventry University, Coventry, UK. His main research interests include energy efficient & energy smart manufacturing and digital manufacturing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Katchasuwanmanee, K., Cheng, K. & Bateman, R. Simulation based energy-resource efficient manufacturing integrated with in-process virtual management. Chin. J. Mech. Eng. 29, 1083–1089 (2016). https://doi.org/10.3901/CJME.2016.0714.080

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2016.0714.080

Keywords

Search

Navigation