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Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop

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Abstract

With increasing diverse product demands, the manufacturing paradigm has been transformed into a mass-individualized one, among which one bottleneck is to achieve the interoperability between physical world and the digital world of manufacturing system for the intelligent organizing of resources. This paper presents a digital twin-driven manufacturing cyber-physical system (MCPS) for parallel controlling of smart workshop under mass individualization paradigm. By establishing cyber-physical connection via decentralized digital twin models, various manufacturing resources can be formed as dynamic autonomous system to co-create personalized products. Clarification on the MCPS concept, characteristics, architecture, configuration, operating mechanism and key enabling technologies are elaborated, respectively. A demonstrative implementation of the digital twin-driven parallel controlling of board-type product smart manufacturing workshop is also presented. It addresses a bi-level online intelligence in proactive decision making for the organization and operation of manufacturing resources.

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References

  • Alam KM, El Saddik A (2017) C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5:2050–2062

    Article  Google Scholar 

  • Battle R, Benson E (2008) Bridging the semantic Web and Web 2.0 with representational state transfer (REST). Web Semant Sci Serv Agents World Wide Web 6:61–69

    Article  Google Scholar 

  • Boschert S, Rosen R (2016) Digital twin the simulation aspect. Springer International Publishing, Basel

    Book  Google Scholar 

  • Brenner B, Hummel V (2017) Digital twin as enabler for an innovative digital shopfloor. Proc Manuf 9:198–205

    Google Scholar 

  • Cavalieri S, Pezzotta G (2012) Product-service systems engineering: state of the art and research challenges. Comput Ind 63:278–288

    Article  Google Scholar 

  • Cerrone A, Hochhalter J, Heber G, Ingraffea A (2014) On the effects of modeling as-manufactured geometry: toward digital twin. Int J Aerosp Eng 439278:1–10

    Article  Google Scholar 

  • Cochran DS, Hendricks S, Barnes J, Bi Z (2016) Extension of manufacturing system design decomposition to implement manufacturing systems that are sustainable. J Manuf Sci Eng Trans ASME 138:1–10

    Article  Google Scholar 

  • Derberg RS, Rmefjord KW, Carlson JS, Lindkvist L (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann-Manuf Technol 66:137–140

    Article  Google Scholar 

  • Erenay B, Suer GA, Huang J, Maddisetty S (2015) Comparison of layered cellular manufacturing system design approaches. Comput Ind Eng 85:346–358

    Article  Google Scholar 

  • Ferguson S, Bennett E, Ivashchenko A (2017) Digital twin tackles design challenges. World Pumps 2017:26–28

  • Gang X, Fenghua Z, Xiwei L, Xisong D, Wuling H, Songhang C, Kai Z (2015) Cyber-physical–social system in intelligent transportation. IEEE/CAA J Autom Sin 2:320–333

    Article  MathSciNet  Google Scholar 

  • Gao J, Yao Y, Zhu VCY, Sun L, Lin L (2011) Service-oriented manufacturing: a new product pattern and manufacturing paradigm. J Intell Manuf 22:435–446

    Article  Google Scholar 

  • Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. Web Pages, https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication. Accessed 30 Jan 2018

  • Grieves M, Vickers J (2017) Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary perspectives on complex systems. Springer, Cham, pp 85–113

    Chapter  Google Scholar 

  • Heinrichs H (2013) Sharing economy: a potential new pathway to sustainability. GAIA 22:228

    Article  Google Scholar 

  • Hussein D, Park S, Han SN, Crespi N (2015) Dynamic social structure of things: a contextual approach in CPSS. IEEE Intell Syst 19:12–20

    Google Scholar 

  • Jiang P, Ding K, Leng J (2016) Towards a cyber-physical–social-connected and service-oriented manufacturing paradigm: social manufacturing. Manuf Lett 7:15–21

    Article  Google Scholar 

  • Leng J, Jiang P (2016) A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowl Based Syst 100:188–199

    Article  Google Scholar 

  • Leng J, Jiang P (2017a) Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. J Intell Manuf. https://doi.org/10.1007/s10845-017-1301-y

    Google Scholar 

  • Leng J, Jiang P (2017b) Mining and matching relationships from interaction contexts in a social manufacturing paradigm. IEEE Trans Syst Man Cybern Syst 47:276–288

    Google Scholar 

  • Leng J, Jiang P (2018) Evaluation across and within collaborative manufacturing networks: a comparison of manufacturers’ interactions and attributes. Int J Prod Res. https://doi.org/10.1080/00207543.2018.1430903

    Google Scholar 

  • Leng J, Jiang P, Ding K (2014) Implementing of a three-phase integrated decision support model for parts machining outsourcing. Int J Prod Res 52:3614–3636

    Article  Google Scholar 

  • Leng J, Jiang P, Zheng M (2017) Outsourcer-supplier coordination for parts machining outsourcing under social manufacturing. Proc Inst Mech Eng Part B J Eng Manuf 231:1078–1090

    Article  Google Scholar 

  • Leng J, Chen Q, Mao N, Jiang P (2018) Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl Based Syst 143:295–306

    Article  Google Scholar 

  • Liu MR, Zhang QL, Ni LM, Tseng MM (2004) An RFID-based distributed control system for mass customization manufacturing. In: Cao J, Yang LT, Guo M, Lau F (eds) Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 1039–1049

    Google Scholar 

  • Park JH, Yen NY (2018) Advanced algorithms and applications based on IoT for the smart devices. J Amb Intel Hum Comp. https://doi.org/10.1007/s12652-018-0715-5

    Google Scholar 

  • Schleich B, Anwer N, Mathieu L, Wartzack S (2017) Shaping the digital twin for design and production engineering. CIRP Ann Manuf Technol 66:141–144

    Article  Google Scholar 

  • Shardaa B, Banerjee A (2013) Robust manufacturing system design using multi objective genetic algorithms, Petri nets and Bayesian uncertainty representation. J Manuf Syst 32:315–324

    Article  Google Scholar 

  • Sheth A, Anantharam P, Henson C (2013) Physical-cyber-social computing: an early 21st century approach. IEEE Intell Syst 28:78–82

    Article  Google Scholar 

  • Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inform 10:1547–1557

    Article  Google Scholar 

  • Tao F, Cheng J, Qi Q (2017) IIHub: an industrial internet-of-things hub towards smart manufacturing based on cyber-physical system. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2017.2759178

    Google Scholar 

  • Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018a) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576

    Article  Google Scholar 

  • Tao F, Qi Q, Liu A, Kusiak A (2018b) Data-driven smart manufacturing. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2018.01.006

    Google Scholar 

  • Tu M, Lin J, Chen R, Chen K, Jwo J (2009) Agent-based control framework for mass customization manufacturing with UHF RFID technology. IEEE Syst J 3:343–359

    Article  Google Scholar 

  • Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng 154798:1–14

    Article  Google Scholar 

  • Uhlemann THJ, Schock C, Lehmann C, Freiberger S, Steinhilper R (2017) The digital twin: demonstrating the potential of real time data acquisition in production systems. Proc Manuf 2017:113–120

    Google Scholar 

  • Vargoa SL, Luschb RF (2008) From goods to service(s): divergences and convergences of logics. Ind Mark Manag 37:254–259

    Article  Google Scholar 

  • Wang F (2010) The emergence of intelligent enterprises: from CPS to CPSS. IEEE Intell Syst 25:85–88

    Article  Google Scholar 

  • Wang XV, Wang L (2017) A cloud based production system for information and service integration an internet of things case study on waste electronics. Enterp Inf Syst UK 11:952–968

    Article  Google Scholar 

  • Wang C, Jiang P, Ding K (2017) A hybrid-data-on-tag-enabled decentralized control system for flexible smart workpiece manufacturing shop floors. Proc Inst Mech Eng Part C J Eng Mech Eng Sci 231:764–782

    Article  Google Scholar 

  • Xu B, Xu LD, Fei X, Jiang L, Cai H, Wang S (2017) A method of demand-driven and data-centric web service configuration for flexible business process implementation. Enterp Inf Syst UK 11:988–1004

    Article  Google Scholar 

  • Yang Y, Hu T, Ye Y, Gao W, Zhang C (2018) A knowledge generation mechanism of machining process planning using cloud technology. J Amb Intel Hum Comp. https://doi.org/10.1007/s12652-018-0779-2

    Google Scholar 

  • Zhang F, Jiang P, Li J, Hui J, Zhu B (2017a) A distributed configuration scheme for warehouse product service system. Adv Mech Eng 9:1–13

    Google Scholar 

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

    Article  Google Scholar 

  • Zhang C, Wang J, Zhang C (2018) Two-agent scheduling on a single parallel-batching machine to minimize the weighted sum of the agents’ makespans. J Amb Intel Hum Comp. https://doi.org/10.1007/s12652-018-0741-3

    Google Scholar 

  • Zou J, Chang Q, Arinez J, Xiao G, Lei Y (2017) Dynamic production system diagnosis and prognosis using model-based data-driven method. Expert Syst Appl 80:200–209

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 51705091 and 51675108; the Science and Technology Plan Project of Guangzhou under Grant No. 201804020092; the Science and Technology Planning Project of Guangdong Province of China under Grant Nos. 2015B010128007 and 2016A010106006; and the Fundamental Research Funds for the Central Universities under Grant No. 2015ZZ079.

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Correspondence to Qiang Liu.

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Leng, J., Zhang, H., Yan, D. et al. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. J Ambient Intell Human Comput 10, 1155–1166 (2019). https://doi.org/10.1007/s12652-018-0881-5

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