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Digital twin-based cyber physical production system architectural framework for personalized production

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Abstract

Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin-based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object-orientation and the “type and instance” concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system-of-systems perspective. The application of the digital twin-based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of ~ 26.87% was achieved using the implemented CPPS services in the MSF.

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References

  1. Yao X, Lin Y (2016) Emerging manufacturing paradigm shifts for the incoming industrial revolution. Int J Adv Manuf Technol 85:1665–1676

    Google Scholar 

  2. Wiktorsson M et al (2018) Smart factories: South Korean and Swedish examples on manufacturing settings. Procedia Manuf. 25:471–478

    Google Scholar 

  3. Doukas M, Psarommatis F, Mourtzis D (2014) Planning of manufacturing networks using an intelligent probabilistic approach for mass customised products. Int J Adv Manuf Technol 74:1747–1758

    Google Scholar 

  4. Yao S et al (2007) Computer-aided manufacturing planning for personalized production: Part 1, framework. Int J Adv Manuf Technol 32:194–204

    Google Scholar 

  5. Mai J et al (2016) Customized production based on distributed 3D printing services in cloud manufacturing. Int J Adv Manuf Technol 84:71–83

    Google Scholar 

  6. Kumar A (2007) From personalized production to mass personalization: a strategic transformation. Int J Flex Manuf Syst 19:533

    Google Scholar 

  7. Zhao WB, Jung YJ, Park KT, Noh SD, Kim H, Son JY (2016) Design and implementation of a factory-level CPS for a micro smart factory. ACDDE, Jeju

    Google Scholar 

  8. Du X, Jiao J, Tseng MM (2006) Understanding customer satisfaction in product customization. Int J Adv Manuf Technol 31:396–406

    Google Scholar 

  9. Son J-Y et al (2015) IoT-based open manufacturing service platform for mass personalization, J. KICS 33:42–47

    Google Scholar 

  10. Engel G, Greiner T, Seifert S (2018) Ontology-assisted engineering of cyber–physical production systems in the field of process technology. IEEE Trans Ind. Informat. 14:2792–2802

    Google Scholar 

  11. Kim H-J (2018) Bounds for parallel machine scheduling with predefined parts of jobs and setup time. Ann Oper Res 261:401–412

    MathSciNet  MATH  Google Scholar 

  12. Hakan K, Farahani S, Pilla S (2019) Feasibility study for manufacturing CF/epoxy–thermoplastic hybrid structures in a single operation. Procedia Manuf 33:232–239

    Google Scholar 

  13. Qi Q, Fei T (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6:3585–3593

    Google Scholar 

  14. Brecher C et al (2012) Integrative production technology for high-wage countries. In: Integrative production technology for high-wage countries. Springer, Berlin, pp 17–76

    Google Scholar 

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

    Google Scholar 

  16. Shah D, Patel P (2018) Productivity improvement by implementing lean manufacturing tools in manufacturing industry. Int Res J Eng Technol 5(3):3–7

    Google Scholar 

  17. Lee J-H, Kim H-J (2016) Makespan analysis of loT switching period in cluster tools. IEEE Trans Semicond Manuf 29:127–136

    Google Scholar 

  18. Liu Q et al (2018) Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int J Prod Res:1–17

  19. Cheng Y et al (2018) Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey. Int J Adv Manuf Technol:1–13

  20. Monostori L et al (2016) Cyber-physical systems in manufacturing, CIRP Ann. Manuf Technol 65:621–641

    Google Scholar 

  21. Ribeiro L, Björkman M (2017) Transitioning from standard automation solutions to cyber-physical production systems: an assessment of critical conceptual and technical challenges. IEEE Syst J 1-13

  22. Ribeiro L (2017) Cyber-physical production systems’ design challenges. IEEE ISIE:1189–1194

  23. Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96(1-4):1149–1163

    Google Scholar 

  24. Otto J, Vogel-Heuser B, Niggemann O (2018) Automatic parameter estimation for reusable software components of modular and reconfigurable cyber-physical production systems in the domain of discrete manufacturing. IEEE Trans Ind Informat 14:275–282

    Google Scholar 

  25. Ding K et al (2019) Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors. Int J Prod Res 1-20

  26. Tao F et al (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576

    Google Scholar 

  27. Vachálek J et al (2017) The digital twin of an industrial production line within the industry 4.0 concept. IEEE PC:258–262

  28. Zhang H et al (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5:26901–26911

    Google Scholar 

  29. Farahani S, et al. (2019) Numerical simulation for the hybrid process of sheet metal forming and injection molding using smoothed particle hydrodynamics method. No. 2019-01-0713. SAE Technical Paper, 2019

  30. Sztipanovits J, Ying S (2013) Foundations for Innovation: strategic R&D opportunities for the 21th century cyber-physical systems, National Institute of Standards and Technology (NIST), 32 pages

  31. Park KT, Kang YT, Yang SG, Zhao WB, Kang YS, Im SJ, Kim DH, Choi SY, Noh S (2019) Cyber physical energy system for saving energy of the dyeing process with industrial Internet of Things and manufacturing big data. Int J Precis Eng Manuf-Green Technol:1–20. https://doi.org/10.1007/s40684-019-00084-7

    Google Scholar 

  32. Lee J et al (2018) Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf Lett 18:20–23

    Google Scholar 

  33. Lee J et al (2015) Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38:3–7

    Google Scholar 

  34. Crawley E et al (2004) The influence of architecture in engineering system. Monograph 3

  35. Chiriac N et al (2011) Level of modularity and different levels of system granularity. J Mech Des 133:101007

    Google Scholar 

  36. Liao Y, de Freitas Rocha Loures E, Deschamps F (2018) Industrial Internet of Things: a systematic literature review and insights. IEEE IoT J 5:4515–4525

    Google Scholar 

  37. Ngai EWT et al (2008) RFID research: an academic literature review (1995–2005) and future research directions. Int J Prod Econ 112:510–520

    Google Scholar 

  38. Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White Paper, Florida Institute of Technology, pp 1–7

    Google Scholar 

  39. 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

    Google Scholar 

  40. Rosen R et al (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567–572

    Google Scholar 

  41. Rojas RA, Rauch E (2019) From a literature review to a conceptual framework of enablers for smart manufacturing control. Int J Adv Manuf Technol 1-17

  42. Gabor T et al (2016) A simulation-based architecture for smart cyber-physical systems. IEEE ICAC:374–379

  43. Uhlemann TH-J et al (2017) The digital twin: demonstrating the potential of real-time data acquisition in production systems. Procedia Manuf 9:113–120

    Google Scholar 

  44. Park KT et al (2019) Service-oriented platform for smart operation of dyeing and finishing industry. Int J Comput Integr Manuf 32(3):307–326

    Google Scholar 

  45. Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427

    Google Scholar 

  46. Tao F, Qi Q (2017) New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans Syst Man Cybern 99:1–11

    Google Scholar 

  47. van Kranenburg R (2008) The Internet of Things: a critique of ambient technology and the all-seeing network of RFID. Institute of Network Cultures

  48. Hossain MS, Muhammad G (2016) Cloud-assisted industrial Internet of Things (IIoT)—enabled framework for health monitoring. Comput Netw 101:192–202

    Google Scholar 

  49. Tao F, Cheng J, Qi Q (2018) IIHub: An industrial Internet-of-Things hub toward smart manufacturing based on cyber-physical system. IEEE Trans Ind Informat 14:2271–2280

    Google Scholar 

  50. Da Xu L, He W, Li S (2014) Internet of things in industries: a survey. IEEE Trans Ind. Informat 10:2233–2243

    Google Scholar 

  51. Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54:2787–2805

    MATH  Google Scholar 

  52. Sklyar V (2017) Vedic mathematics as fast algorithms in green computing for Internet of Things. In: Green IT Engineering: Components Networks and Systems Implementation. Springer, Cham, pp 3–21

    Google Scholar 

  53. Zezulka F et al (2016) Industry 4.0—an introduction in the phenomenon. IFAC-PapersOnLine 49:8–12

    Google Scholar 

  54. Komoda N (2006) Service oriented architecture (SOA) in industrial systems. IEEE INDIN:1–5

  55. MacKenzie CM et al. (2006) Reference model for service oriented architecture 1.0, OASIS 12:18

  56. Papazoglou MP et al (2007) Service-oriented computing: state of the art and research challenges. Computer:40

  57. Ramollari E, Dranidis D, Simons AJH (2007) A survey of service oriented development methodologies, 2nd European YR-SOC 75

  58. Yaqoob I et al (2017) Internet of Things architecture: recent advances, taxonomy, requirements, and open challenges. IEEE Wirel Commun 24:10–16

    Google Scholar 

  59. Zhu T et al (2017) An architecture for aggregating information from distributed data nodes for industrial Internet of Things. Comput Electr Eng 58:337–349

    Google Scholar 

  60. Zhang Y et al (2015) Real-time information capturing and integration framework of the internet of manufacturing things. Int J Comput Integr Manuf 28:811–822

    Google Scholar 

  61. Kang HS et al (2018) The FaaS system using additive manufacturing for personalized production. Rapid Prototyp J 24:1486–1499

    Google Scholar 

  62. Park KT et al (2019) Design and implementation of a digital twin application for connected micro smart factory. Int J Comput Integr Manuf 32(6):596–614

    Google Scholar 

Download references

Funding

This work was supported by the IT R&D Program of MOTIE/KEIT [10052972, Development of the Reconfigurable Manufacturing Core Technology based on the Flexible Assembly and ICT Converged Smart Systems] and the WC300 Project [S2482274, Development of Multi-vehicle Flexible Manufacturing Platform Technology for Future Smart Automotive Body Production] funded by the Ministry of SMEs and Startups

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Authors and Affiliations

  1. Department of Industrial Engineering, Sungkyunkwan University, Suwon-si, Republic of Korea

    Kyu Tae Park, Jehun Lee & Sang Do Noh

  2. Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon-si, Republic of Korea

    Hyun-Jung Kim

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  1. Kyu Tae Park
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  2. Jehun Lee
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  3. Hyun-Jung Kim
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  4. Sang Do Noh
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Correspondence to Sang Do Noh.

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Park, K.T., Lee, J., Kim, HJ. et al. Digital twin-based cyber physical production system architectural framework for personalized production. Int J Adv Manuf Technol 106, 1787–1810 (2020). https://doi.org/10.1007/s00170-019-04653-7

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