Skip to main content

Advertisement

Log in

Hardware in the loop simulation for product driven control of a cyber-physical manufacturing system

  • Production Process
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Cyber-physical system (CPS) is considered as a building block of industry 4.0. They are formulated as a network of interacting cyberspace and physical elements. Dealing with this new industrial context, distributed control systems (DCS) are increasingly involved because they permit meeting flexibility and adaptability requirements, which can give scope to CPS. The product driven control system (PDS) is considered as DCS in which the product plays a major role in decision-making. However, the PDS paradigm has not yet received sufficient attention within the CPS. Relying on multi-agents system as implementation framework, radio frequency identity as auto-identity technologies, and hardware in the loop simulation as a practical methodology, the paper proposes a validation and practical framework of PDS applied to the highly automated flexible robotized assembly system. An efficient CPS is developed for a discrete flexible manufacturing system.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Liao Y, Deschamps F, Freitas Rocha Loures DE, Ramos LFP (2017) Past, present and future of Industry 4.0—a systematic literature review and research agenda proposal. Int J Prod Res 55(12):3609–3629

    Article  Google Scholar 

  2. Verl A, Lechler A, Schlechtendahl J (2012) Glocalized cyber physical production systems. Prod Eng Res Dev 6(6):643–649

    Article  Google Scholar 

  3. Bangemann T, Riedl M, Thron M, Diedrich C (2016) Integration of classical components into industrial cyber physical systems. Proc IEEE 104(5):947–959

    Article  Google Scholar 

  4. Dalenogare LS, Benitez GB, Ayala NF, Frank AG (2018) The expected contribution of Industry 4.0 technologies for industrial performance. Int J Prod Econ 204:383–394

    Article  Google Scholar 

  5. Leitao P, Karnouskos S, Ribeiro L, Lee J, Strasser T, Colombo AW (2016) Smart agents in industrial cyber–physical systems. Proc IEEE 104(5):1086–1101

    Article  Google Scholar 

  6. Cardin O (2019) Classification of cyber-physical production systems applications: proposition of an analysis framework. Comput Ind 104:11–21

    Article  Google Scholar 

  7. Wang Y, Ma HS, Yang JH, Wang KS (2017) Industry 4.0: a way from mass customization to mass personalization production. Adv Manuf 5(4):311–320

    Article  Google Scholar 

  8. Monostori L, Kádár B, Bauernhansl T et al (2016) Cyber-physical systems in manufacturing. CIRP Ann 65(2):621–641

    Article  Google Scholar 

  9. Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23

    Article  Google Scholar 

  10. Zhang J, Ding G, Zou Y, Qin S, Fu J (2019) Review of job shop scheduling research and its new perspectives under Industry 4.0. J Intell Manuf 30(4):1809–1830

    Article  Google Scholar 

  11. Shen W, Norrie DH (1999) Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowl Inf Syst 1(2):129–156

    Article  Google Scholar 

  12. Rey GZ, Bonte T, Prabhu V, Trentesaux D (2014) Reducing myopic behavior in FMS control: a semi-heterarchical simulation–optimization approach. Simul Model Pract Theory 46:53–75

    Article  Google Scholar 

  13. Gaham M, Bouzouia B, Achour N (2014) An evolutionary simulation-optimization approach to product-driven manufacturing control. Springer, Berlin, pp 283–294

    Google Scholar 

  14. Upasani K, Bakshi M, Pandhare V, Lad BK (2017) Distributed maintenance planning in manufacturing industries. Comput Ind Eng 108:1–14

    Article  Google Scholar 

  15. Jennings NR (2000) On agent-based software engineering. Artif Intell 117:277–296

    Article  MATH  Google Scholar 

  16. Xiang W, Lee HP (2008) Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng Appl Artif Intell 21(1):73–85

    Article  Google Scholar 

  17. Pannequin R, Morel G, Thomas A (2009) The performance of product-driven manufacturing control: an emulation-based benchmarking study. Comput Ind 60(3):195–203

    Article  Google Scholar 

  18. El Haouzi H, Pétin JF, Thomas A (2009) Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation. Prod Plan Control 20(6):510–524

    Article  Google Scholar 

  19. Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harvard Bus Rev 92(11):64–88

    Google Scholar 

  20. Mühlhäuser M (2007) Smart products: an introduction. Springer, Berlin, pp 158–164

    Google Scholar 

  21. McFarlane D, Sarma S, Chirn JL, Wong C, Ashton K (2002) The intelligent product in manufacturing control and management. IFAC Proc Vol 35(1):49–54

    Article  Google Scholar 

  22. Meyer GG, Främling K, Holmström J (2009) Intelligent products: a survey. Comput Ind 60(3):137–148

    Article  Google Scholar 

  23. McFarlane D, Giannikas V, Wong AC, Harrison M (2013) Product intelligence in industrial control: theory and practice. Annu Rev Control 37(1):69–88

    Article  Google Scholar 

  24. Leitão P, Rodrigues N, Barbosa J, Turrin C, Pagani A (2015) Intelligent products: the grace experience. Control Eng Pract 42:95–105

    Article  Google Scholar 

  25. Zhang Y, Qian C, Lv J, Liu Y (2016) Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Trans Ind Inf 13(2):737–747

    Article  Google Scholar 

  26. Basile F, Chiacchio P, Coppola J (2016) A cyber-physical view of automated warehouse systems. In: IEEE, pp 407–412

  27. McFarlane D, Sarma S, Chirn JL, Wong C, Ashton K (2003) Auto ID systems and intelligent manufacturing control. Eng Appl Artif Intell 16(4):365–376

    Article  Google Scholar 

  28. Lu B, Bateman R, Cheng K (2006) RFID enabled manufacturing: fundamentals, methodology and applications. Int J Agile Syst Manag 1(1):73–92

    Article  Google Scholar 

  29. Wang KS (2014) Intelligent and integrated RFID (II-RFID) system for improving traceability in manufacturing. Adv Manuf 2(2):106–120

    Article  Google Scholar 

  30. Guo Z, Ngai E, Yang C, Liang X (2015) An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. Int J Prod Econ 159:16–28

    Article  Google Scholar 

  31. Barbosa J, Leitão P, Teixeira J (2018) Empowering a cyber-physical system for a modular conveyor system with selforganization. Springer, Berlin, pp 157–170

    Google Scholar 

  32. Gonzalez SR, Zambrano GM, Mondragon IF (2019) Semi-heterarchical architecture to AGV adjustable autonomy within FMSs. IFAC Pap OnLine 52(10):7–12

    Article  Google Scholar 

  33. Feliciano Filho M, Liao Y, Loures ER, Junior OC (2017) Self-aware smart products: systematic literature review, conceptual design and prototype implementation. Proc Manuf 11:1471–1480

    Google Scholar 

  34. Molesini A, Casadei M, Omicini A, Viroli M (2013) Simulation in agent-oriented software engineering: the SODA case study. Sci Comput Program 78(6):705–714

    Article  Google Scholar 

  35. Madni AM, Sievers M (2018) Model-based systems engineering: motivation, current status, and research opportunities. Syst Eng 21(3):172–190

    Article  Google Scholar 

  36. Bersani MM, García-Valls M (2018) Online verification in cyber-physical systems: practical bounds for meaningful temporal costs. J Softw Evolut Process. https://doi.org/10.1002/smr.1880

    Article  Google Scholar 

  37. Soylemezoglu A, Zawodniok MJ, Cha K et al (2006) A testbed architecture for auto-ID technologies. Assembly Autom 26(2):127–136

    Article  Google Scholar 

  38. Millitzer J, Mayer D, Henke C et al (2019) Recent developments in hardware-in-the-loop testing. Springer, Berlin, pp 65–73

    Google Scholar 

  39. Harrison WS, Tilbury DM, Yuan C (2011) From hardware-in-the-loop to hybrid process simulation: an ontology for the implementation phase of a manufacturing system. IEEE Trans Autom Sci Eng 9(1):96–109

    Article  Google Scholar 

  40. Sarhadi P, Yousefpour S (2015) State of the art: hardware in the loop modeling and simulation with its applications in design, development and implementation of system and control software. Int J Dyn Control 3(4):470–479

    Article  Google Scholar 

  41. Zhang H, Liu Q, Chen X, Zhang D, Leng J (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5:26901–26911

    Article  Google Scholar 

  42. Putman NM, Maturana F, Barton K, Tilbury DM (2017) Virtual fusion: a hybrid environment for improved commissioning in manufacturing systems. Int J Prod Res 55(21):6254–6265

    Article  Google Scholar 

  43. Garey MR, Johnson DS, Sethi R (1976) The complexity of flowshop and jobshop scheduling. Math Oper Res 1(2):117–129

    Article  MathSciNet  MATH  Google Scholar 

  44. Browne J, Dubois D, Rathmill K, Sethi SP, Stecke KE et al (1984) Classification of flexible manufacturing systems. FMS Mag 2(2):114–117

    Google Scholar 

  45. Bellifemine FL, Caire G, Greenwood D (2007) Developing multi-agent systems with JADE. 7. Wiley, New York

    Book  Google Scholar 

  46. Burbank JL, Kasch W, Ward J (2011) An introduction to network modeling and simulation for the practicing engineer. Wiley, New York

    Book  Google Scholar 

  47. Marijan D, Gotlieb A, Liaaen M (2019) A learning algorithm for optimizing continuous integration development and testing practice. Softw Pract Exp 49(2):192–213

    Article  Google Scholar 

  48. Li Q, Yang Y, Li M, Wang Q, Boehm BW, Hu C (2012) Improving software testing process: feature prioritization to make winners of success-critical stakeholders. J Softw Evol Process 24(7):783–801

    Article  Google Scholar 

  49. Trentesaux D, Pach C, Bekrar A et al (2013) Benchmarking flexible job-shop scheduling and control systems. Control Eng Pract 21(9):1204–1225

    Article  Google Scholar 

  50. AIP-PRIMECA (2013) FMS benchmarking. http://www.uphf.fr//bench4star/content/data-preparation

  51. Zambrano Rey G, Bekrar A, Prabhu V, Trentesaux D (2014) Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops. Int J Prod Res 52(12):3688–3709

    Article  Google Scholar 

  52. Ouelhadj D, Petrovic S (2009) A survey of dynamic scheduling in manufacturing systems. J Sched 12(4):417

    Article  MathSciNet  MATH  Google Scholar 

  53. Nasiri MM, Yazdanparast R, Jolai F (2017) A simulation optimisation approach for real-time scheduling in an open shop environment using a composite dispatching rule. Int J Comput Integr Manuf 30(12):1239–1252

    Article  Google Scholar 

  54. Azadeh A, Shoja BM, Moghaddam M, Asadzadeh S, Akbari A (2013) A neural network meta-model for identification of optimal combination of priority dispatching rules and makespan in a deterministic job shop scheduling problem. Int J Adv Manuf Technol 67(5–8):1549–1561

    Article  Google Scholar 

  55. Nordgren WB (2002) Flexsim simulation environment. In: Proceedings of the winter simulation conference, vol 1. IEEE, pp 250–252

  56. Röck S (2011) Hardware in the loop simulation of production systems dynamics. Prod Eng Res Dev 5(3):329–337

    Article  Google Scholar 

  57. Mihoubi B, Gaham M, Bouzouia B, Bekrar A (2015) A rule-based harmony search simulation-optimization approach for intelligent control of a robotic assembly cell. In: IEEE, pp 1–6

  58. Sallez Y, Berger T, Trentesaux D (2009) A stigmergic approach for dynamic routing of active products in FMS. Comput Ind 60(3):204–216

    Article  Google Scholar 

  59. OPC UA. https://opcfoundation.org/about/opc-technologies/opc-ua/

Download references

Acknowledgements

This work is supported by the Algerian Directorate General for Scientific Research and Technological Development through the project n°: 17/CDTA/DGRSDT.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Mihoubi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mihoubi, B., Bouzouia, B., Tebani, K. et al. Hardware in the loop simulation for product driven control of a cyber-physical manufacturing system. Prod. Eng. Res. Devel. 14, 329–343 (2020). https://doi.org/10.1007/s11740-020-00957-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-020-00957-w

Keywords

Navigation