Abstract
Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, environmental, and social challenges. To meet such challenges, we regard Industry 5.0 as a socio-technical revolution based on the socio-cyber-physical system (SCPS), and propose a socio-technically enhanced wisdom manufacturing architecture and framework beyond CPS-based Industry 4.0/smart manufacturing with especially concerning transition enabling technologies such as artificial intelligence, social Internet of Things (SIoT), big data, machine learning, edge computing, social computing, 3D printing, blockchains, digital twins, and cobots. Finally we address the roadmap to blockchainized value-added SCPS-based Industrial Metaverse for Industry/Society 5.0, which will achieve high utilization of resources and provide products and services to satisfy experience-driven individual needs via metamanufacturing cloud services towards smart, resilient, sustainable, and human-centric solutions.
Similar content being viewed by others
Abbreviations
- AI:
-
artificial intelligence
- AM:
-
additive manufacturing
- CAD:
-
computer aided design
- CAE:
-
computer aided engineering
- CAM:
-
computer aided manufacturing
- CIM:
-
computer integrated manufacturing
- CPPS:
-
cyber-physical production system
- CPS:
-
cyber-physical system
- DT:
-
digital twin
- ERP:
-
enterprise resource planning
- GPU:
-
graphics processing unit
- HiL:
-
human-in-the loop
- HoL:
-
human-on-the-Loop
- HofL:
-
human-out-of-the-Loop
- ICT:
-
information and communication technology
- IbfP:
-
Internet by and for people
- IIoT:
-
industrial IoT
- IoCK:
-
Internet of contents and knowledge
- IoP:
-
Internet of people
- IoS:
-
Internet of services
- IoT:
-
Internet of things
- ML:
-
machine learning
- PDM:
-
product data management
- PLM:
-
product lifecycle management
- SCPPS:
-
social-cyber-physical production system
- SCPS:
-
socio/social-cyber-physical system
- SF:
-
smart factory
- SIoT:
-
social Internet of things
- SWSN:
-
social wireless sensor networks
- WM:
-
wisdom (wise) manufacturing
References
Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Selangor, Malaysia, December 09–12 2014.https://doi.org/10.1109/ieem.2014.7058728
Wikipedia (2021). Industry 4.0. Available: https://en.wikipedia.org/wiki/Industry_4.0
Hermann, M., Pentek, T., & Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios. In 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, January 05–08 2016.https://doi.org/10.1109/HICSS.2016.488
Kang, H. S., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128. https://doi.org/10.1007/s40684-016-0015-5
Lee, E. A. (2015). The Past, Present and Future of Cyber-Physical Systems: A Focus on Models. Sensors (Basel, Switzerland), 15(3), 4837–4869. https://doi.org/10.3390/s150304837
Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11–25. https://doi.org/10.1016/j.compind.2015.08.004
Cohen, Y., Faccio, M., Pilati, F., & Yao, X. (2019). Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. The International Journal of Advanced Manufacturing Technology, 105(9), 3565–3577. https://doi.org/10.1007/s00170-019-04595-0
Fantini, P., et al. (2016). Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: methodology and results. In IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, October 23–26 2016.https://doi.org/10.1109/IECON.2016.7793579
Romero, D., Bernus, P., Noran, O., & Stahre, J., & Å. Fast-Berglund. (2016). The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation towards Human-Automation Symbiosis Work Systems. In APMS (Advances in Production Management Systems). https://doi.org/10.1007/978-3-319-51133-7_80
Breque, M., Nul, L. D., & Petridis, A. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Luxembourg: Publications Office of the European Union. https://ec.europa.eu/info/publications/industry-50_en
K. A. Demir &H. Ciciba. Industry 5.0 and a critique of Industry 4.0. In 4th International Management Information Systems Conference “Industry 4.0”, İstanbul, Turkey, October 17–20 2017.http://www.innovation4.cn/library/r52700
V. Ozdemir, & N. Hekim. (2018). The Internet of Things” and Next-Generation Technology Policy. Omics-a Journal of Integrative Biology, 22(1), 65–76. https://doi.org/10.1089/omi.2017.0194. Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence
Shiroishi, Y., Uchiyama, K., & Suzuki, N. (2018). Society 5.0: For human security and well-being. Computer, 51(7), 91–95. https://doi.org/10.1109/MC.2018.3011041
Fukuda, K. (2020). Science, technology and innovation ecosystem transformation toward society 5.0. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.07.033. 220
Shiroishi, Y., Uchiyama, K., & Suzuki, N. (2019). Better Actions for Society 5.0: Using AI for Evidence-Based Policy Making That Keeps Humans in the Loop. Computer, 52(11), 73–78. https://doi.org/10.1109/MC.2019.2934592
Melnyk, L. H., Kubatko, O. V., Dehtyarova, I. B., Dehtiarova, I. B., Matsenko, O. M., & Rozhko, O. D. (2019). The effect of industrial revolutions on the transformation of social and economic systems. https://essuir.sumdu.edu.ua/handle/123456789/77259
Salimova, T., Guskova, N., & Krakovskaya, I., & E. Sirota. (2019). From industry 4.0 to Society 5.0: Challenges for sustainable competitiveness of Russian industry. In IOP Conference Series: Materials Science and Engineering(p. 012090). IOP Publishing
Potocan, V., Mulej, M., & Nedelko, Z. (2021). Society 5.0: balancing of Industry 4.0, economic advancement and social problems. Kybernetes, 50(3), 794–811. https://doi.org/10.1108/k-12-2019-0858
Zengin, Y., Naktiyok, S., Kaygin, E., Kavak, O., & Topcuoglu, E. (2021). An Investigation upon Industry 4.0 and Society 5.0 within the Context of Sustainable Development Goals. Sustainability, 13(5), 2682. https://doi.org/10.3390/su13052682
Carayannis, E. G., Dezi, L., Gregori, G., & Calo, E. (2021). Smart Environments and Techno-centric and Human-Centric Innovations for Industry and Society 5.0: A Quintuple Helix Innovation System View Towards Smart, Sustainable, and Inclusive Solutions. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-021-00763-4
Oborski, P. (2003). Social-technical aspects in modern manufacturing. The International Journal of Advanced Manufacturing Technology, 22(11–12), 848–854. https://doi.org/10.1007/s00170-003-1573-6
Yao, X. F., Lian, Z. T., Yang, Y., Zhang, Y., & Jin, H. (2014). Wisdom manufacturing: new humans-computers-things collaborative manufacturing model. Computer Integrated Manufacturing Systems, 20(6), 1490–1498. https://doi.org/10.13196/j.cims.2014.06.yaoxifan.1490.9.20140627
Yao, X., Jin, H., & Zhang, J. (2015). Towards a wisdom manufacturing vision. International Journal of Computer Integrated Manufacturing, 28(12), 1291–1312. https://doi.org/10.1080/0951192x.2014.972462
Yao, X., Zhang, J., & Lin, Y. (2016). The basic theory and technical framework for wisdom manufacturing systems. Systems Engineering - Theory & Practice, 36(10), 2699–2711. https://doi.org/10.12011/1000-6788(2016)10-2699-13
Papadimitriou, D. (2009). Future Internet: The cross-ETP vision document,. Available: http://www.future-internet.eu/fileadmin/documents/reports/Cross-ETPs_FI_Vision_Document_v1_0.pdf
Wu, J., Dong, M., Ota, K., Liang, L., & Zhou, Z. (2014). Securing distributed storage for Social Internet of Things using regenerating code and Blom key agreement. Peer-to-Peer Networking and Applications, 8(6), 1133–1142. https://doi.org/10.1007/s12083-014-0286-y
Tuptuk, N. &S., & Hailes (2018). Security of smart manufacturing systems. Journal of Manufacturing Systems, 47, 93–106. https://doi.org/10.1016/j.jmsy.2018.04.007
Yao, X., Jing, X., Zhou, J., & Lin, Y. (2019). Towards next generation sustainable manufacturing-inclusive manufacturing. Computer Integrated Manufacturing Systems, 25(10), 2419–2432. https://doi.org/10.13196/j.cims.2019.10.002
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006
E. G. Carayannis &J. J. J. o. t. K. E. Morawska-Jancelewicz. The futures of Europe: Society 5.0 and Industry 5.0 as driving forces of future universities. 1–27. https://doi.org/10.1007/s13132-021-00854-2
Maddikunta, P. K. R., et al. (2022). Industry 5.0: A survey on enabling technologies and potential applications. 26,100257. https://doi.org/10.1016/j.jii.2021.100257
Fox, W. M. (1995). Sociotechnical system principles and guidelines: past and present. The Journal of Applied Behavioral Science, 31(1), 91–105. https://doi.org/10.1177/0021886395311009
Yao, X., Zhang, J., Tao, T., Jiang, J., & Chen, X. (2018). From leagile manufacturing to long-tail production in Industry 4.0 for upgrading manufacturing. Computer Integrated Manufacturing Systems, 24(10), 2377–2387. https://doi.org/10.13196/j.cims.2018.10.001
Atzori, L., Iera, A., & Morabito, G.,M. Nitti (2012). The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization. Computer Networks, 56(16), 3594–3608. https://doi.org/10.1016/j.comnet.2012.07.010
Yao, X., & Lin (2016). Emerging manufacturing paradigm shifts for the incoming industrial revolution. International Journal of Advanced Manufacturing Technology, 85(5), 1665–1676. https://doi.org/10.1007/s00170-015-8076-0
Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2019). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), 2805–2817. https://doi.org/10.1007/s10845-017-1384-5
P. J. Mosterman &J. Zander. (2016). Industry 4.0 as a Cyber-Physical System study.Software & Systems Modeling, 15(1),17–29. https://doi.org/10.1007/s10270-015-0493-x
Lee, J., Bagheri, B., & Hung-An, K. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letter, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lu, Y. (2017). Cyber Physical System (CPS)-Based Industry 4.0: A Survey. Journal of Industrial Integration and Management, 02(03), 1750014. https://doi.org/10.1142/S2424862217500142
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527. https://doi.org/10.1016/j.jmsy.2015.04.008
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks, 12(1), 3159805. https://doi.org/10.1155/2016/3159805
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2017). Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. Ieee Access : Practical Innovations, Open Solutions, 6, 6505–6519. https://doi.org/10.1109/ACCESS.2017.2783682
Kagermann, H., Wahlster, W., & J. Helbig. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Available: http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf
Moghaddam, M., & Nof, S. Y. (2017). The collaborative factory of the future. International Journal of Computer Integrated Manufacturing, 30(1), 23–43. https://doi.org/10.1080/0951192X.2015.1066034
Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536–541. https://doi.org/10.1016/j.procir.2016.01.129
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of Manufacturing Systems, 39, 79–100. https://doi.org/10.1016/j.jmsy.2016.03.001
Anderson, C. (2012). Makers: The New Industrial Revolution. New York: Crown Business
Jing, X., & Yao, X. F. (2019). Towards Social Cyber-physical Production Systems. Acta Automatica Sinica, 45(4), 637–656. https://doi.org/10.16383/j.aas.2018.c180274
Tortorella, G. L., Vergara, L. G. L., & Ferreira, E. P. (2017). Lean manufacturing implementation: an assessment method with regards to socio-technical and ergonomics practices adoption. The International Journal of Advanced Manufacturing Technology, 89(9), 3407–3418. https://doi.org/10.1007/s00170-016-9227-7
Ma, N., Yao, X., & Wang, K. (2022). Current status and prospect of future Internet oriented wisdom manufacturing. SCIENTIA SINICA Technologica, 52(1), 55–75. https://doi.org/10.1360/SST-2021-0232
Yao, X., Zhou, J., Zhang, C., & Liu, M. (2017). Proactive manufacturing - a big-data driven emerging manufacturing paradigm. Computer Integrated Manufacturing Systems, 23(1), 172–185. https://doi.org/10.13196/j.cims.2017.01.019
Yao, X., Huang, Y., Huang, Y., Mai, H., & Yang, E.,H. Yu (2022). Autonomous smart manufacturing: social-cyber-physical interaction, reference architecture and operation mechanism. Computer Integrated Manufacturing Systems, 28(2), 325–338. https://doi.org/10.13196/j.cims.2022.02.001
Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D. S. (2016). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA, November 18–20 2016.https://doi.org/10.1109/SmartCloud.2016.18
Kompatsiaris, I., Gatica-Perez, D., Xie, X., & Luo, J. (2013). Special section on social media as sensors. IEEE Transactions on Multimedia, 15(6), 1229–1230. https://doi.org/10.1109/TMM.2013.2264232
Calì, J., et al. (2012). 3D-printing of non-assembly, articulated models. ACM Transactions on Graphics (TOG), 31(6), 130. https://doi.org/10.1145/2366145.2366149
Zhang, J., Yao, X., & Li, Y. (2020). Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing. International Journal of Production Research, 58(8), 2263–2282. https://doi.org/10.1080/00207543.2019.1617447
Jara, A. J., Bocchi, Y., & Genoud, D. (2014). Social Internet of Things: The Potential of the Internet of Things for Defining Human Behaviours. 581–585. https://doi.org/10.1109/INCoS.2014.113
Kiel, D., Arnold, C., & Voigt, K. I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies A business level perspective. Technovation, 68, 4–19. https://doi.org/10.1016/j.technovation.2017.09.003
Kim, S., Yim, Y., Oh, S., & Kim, S. H. (2016). Social wireless sensor network toward device-to-device interactive Internet of Things services. International Journal of Distributed Sensor Networks, 12(9), https://doi.org/10.1177/1550147716664251
Ding, K., & Jiang, P. (2016). Incorporating Social Sensors and CPS Nodes for Personalized Production under Social Manufacturing Environment. Procedia CIRP, 56, 366–371. https://doi.org/10.1016/j.procir.2016.10.057
Hey, T., Tansley, S., & Tolle, K. M. (2009). The fourth paradigm: data-intensive scientific discovery. WA: Microsoft research Redmond
Magoutas, B., Stojanovic, N., Bousdekis, A., Apostolou, D., Mentzas, G., & Stojanovic, L. (2014). Anticipation-driven Architecture for Proactive Enterprise Decision Making. In CAiSE (Forum/Doctoral Consortium)(pp. 121–128)
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544, 23–25. https://doi.org/10.1038/544023a
Li, J., Tao, F., Cheng, Y., & Zhao, L. (2015). Big Data in product lifecycle management. International Journal of Advanced Manufacturing Technology, 81(1–4), 667–684. https://doi.org/10.1007/s00170-015-7151-x
Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation, 84–85, 21–36. https://doi.org/10.1016/j.technovation.2018.07.004
Alpaydin, E. (2010). Introduction to Machine Learning (2nd ed.). Adaptive Computation and Machine Learning. The MIT Press
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media
Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45. https://doi.org/10.1080/21693277.2016.1192517
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing — The business perspective. Decision Support Systems, 51(1), 176–189. https://doi.org/10.1016/j.dss.2010.12.006
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
A. Ahmed &E. Ahmed. A survey on mobile edge computing. In Intelligent Systems and Control (ISCO), 10th International Conference on(pp. 1–8). IEEE. https://doi.org/10.1109/ISCO.2016.7727082
Wang, F. Y., Carley, K. M., Zeng, D., & Mao, W. (2007). Social computing: From social informatics to social intelligence. IEEE Intelligent Systems, 22(2), 79–83. https://doi.org/10.1109/MIS.2007.41
Parameswaran, M., & Whinston, A. B. (2007). Social computing: An overview. Communications of the Association for Information Systems, 19(1), 37. https://doi.org/10.17705/1CAIS.01937
ASTM. (2015). “ISO/ASTM52900-15 Standard Terminology for Additive Manufacturing-General Principles-Terminology,“. West Conshohocken: ASTM
Thomas, D. S., & Gilbert, S. W. (2014). Costs and cost effectiveness of additive manufacturing. NIST Special Publication 1176. https://doi.org/10.6028/nist.sp.1176
Hague, R., & Ruffo, M. (2007). Cost estimation for rapid manufacturing — simultaneous production of mixed components using laser sintering. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(11), 1585–1591. https://doi.org/10.1243/09544054jem894
Ruffo, M., Tuck, C., & Hague, R. (2006). Empirical laser sintering time estimator for Duraform PA. International Journal of Production Research, 44(23), 5131–5146. https://doi.org/10.1080/00207540600622522
Huang, S. H., Liu, P., Mokasdar, A., & Hou, L. (2013). Additive manufacturing and its societal impact: a literature review. The International Journal of Advanced Manufacturing Technology, 67(5), 1191–1203. https://doi.org/10.1007/s00170-012-4558-5
Kreiger, M., & Pearce, J. M. (2013). Environmental Life Cycle Analysis of Distributed Three-Dimensional Printing and Conventional Manufacturing of Polymer Products. Acs Sustainable Chemistry & Engineering, 1(12), 1511–1519. https://doi.org/10.1021/sc400093k
Wang, F. (2012). From social computing to social manufacturing: the coming industrial revolution and new frontier in cyber-physical-social space. Bulletin of chinese Academy of Sciences, 6(1), 658–669. https://doi.org/10.3969/j.issn.1000-3045.2012.06.002
Swan, M. (2015). Blockchain: Blueprint for a new economy. " O’Reilly Media, Inc.“
Yu, B., Wright, J., Nepal, S., Zhu, L., Liu, J., & Ranjan, R. (2018). IoTChain: Establishing Trust in the Internet of Things Ecosystem Using Blockchain. IEEE Cloud Computing, 5(4), 12–23. https://doi.org/10.1109/MCC.2018.043221010
Fraga-Lamas (2018). A Review on the Use of Blockchain for the Internet of Things. IEEE Access, 6, 32979–33001. https://doi.org/10.1109/ACCESS.2018.2842685
Holland, M., Nigischer, C., & Stjepandic, J. (2017). Copyright Protection in Additive Manufacturing with Blockchain Approach. In 24th ISPE Inc. International Conference on Transdisciplinary Engineering, Singapore.https://doi.org/10.3233/978-1-61499-779-5-914
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. J. I. P. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Haag, S., & Anderl, R. J. M. L. (2018). Digital twin–Proof of concept. Manufacturing Letters, 15, 64–66. https://doi.org/10.1016/j.mfglet.2018.02.006
Tao, F., Zhang, H., Liu, A., J., A. Y. I. T. o. I. I., & Nee (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Michaelis, J. E., Siebert-Evenstone, A., & Shaffer, D. W. (2020). & B. Mutlu. Collaborative or simply uncaged? understanding human-cobot interactions in automation. In Proceedings of the CHI Conference on Human Factors in Computing Systems(pp. 1–12). https://doi.org/10.1145/3313831.3376547
Nahavandi, S. (2019). Industry 5.0-A Human-Centric Solution. Sustainability, 11(16), 4371. https://doi.org/10.3390/su11164371
Demir, K. A., Doven, G., & Sezen, B. (2019). Industry 5.0 and Human-Robot Co-working. Procedia Computer Science, 158, 688–695. https://doi.org/10.1016/j.procs.2019.09.104
Pan, J., & J. McElhannon. (2018). Future Edge Cloud and Edge Computing for Internet of Things Applications. IEEE Internet of Things Journal, 5(1), 439–449. https://doi.org/10.1109/JIOT.2017.2767608
Jing, X., & Yao, X. (2019). Big Data Driven Cloud-Fog Manufacturing Architecture. Computer Integrated Manufacturing Systems, 25(9), 2119–2139. https://doi.org/10.13196/j.cims.2019.09.001
Hashem, I., Yaqoob, I., Anuar, N., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “Big Data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006
Georgakopoulos, D., Jayaraman, P. P., Fazia, M., Villari, M., & Ranjan, R. (2016). Internet of Things and Edge Cloud Computing Roadmap for Manufacturing. IEEE Cloud Computing, 3(4), 66–73. https://doi.org/10.1109/MCC.2016.91
Ali, M. S., Vecchio, M., Pincheira, M., Dolui, K., Antonelli, F., & Rehmani, M. H. (2018). Applications of Blockchains in the Internet of Things: A Comprehensive Survey. IEEE Communications Surveys and Tutorials. https://doi.org/10.1109/COMST.2018.2886932
Alphand, O., et al. (2018). IoTChain: A blockchain security architecture for the Internet of Things. In 2018 IEEE Wireless Communications and Networking Conference, WCNC April 15, 2018 - April 18, 2018(pp. 1–6). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/WCNC.2018.8377385
Wang, F. Y., Yuan, Y., Zhang, J., Qin, R., & Smith, M. H. (2018). Blockchainized Internet of Minds: A New Opportunity for Cyber-Physical-Social Systems. IEEE Transactions on Computational Social Systems, 5(4), 897–906. https://doi.org/10.1109/TCSS.2018.2881344
Yao, X., Lei, Y., Ge, D., & Ye, J. (2019). On big data that drives manufacturing from “Internet Plus” to “AI Plus”. China Mechanical Engineering, 30(2), 134–142. https://doi.org/10.3969/j.issn.1004-132X.2019.02.002
Romero, D., et al. Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, China(pp. 29–31)
Zhang, C. & X. Yao (2016). Innovation in wisdom manufacturing. In 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, November 19–19 2016.https://doi.org/10.1109/GET.2016.7916702
Świątek, L. (2018). From industry 4.0 to nature 4.0–sustainable infrastructure evolution by design. In International Conference on Applied Human Factors and Ergonomics(pp. 438–447). Springer, Cham. https://doi.org/10.1007/978-3-319-94199-8_42
Park, S. M., & Kim, Y. G. (2022). A Metaverse: Taxonomy, Components, Applications, and Open Challenges. Ieee Access : Practical Innovations, Open Solutions, 10, 4209–4251. https://doi.org/10.1109/ACCESS.2021.3140175
Acknowledgements
This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515010095, 2021A1515010506), the National Natural Science Foundation of China and the Royal Society of Edinburgh (51911530245), and National Natural Science Foundation of China (51675186).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yao, X., Ma, N., Zhang, J. et al. Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0. J Intell Manuf 35, 235–255 (2024). https://doi.org/10.1007/s10845-022-02027-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-022-02027-7