Skip to main content

Advertisement

SpringerLink
Log in

A novel intelligent manufacturing mode with human-cyber-physical collaboration and fusion in the non-ferrous metal industry

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The non-ferrous metal industry is one of the most important parts of China’s process industry and has an extremely important strategic position in the national economy. However, there are many problems in the process of non-ferrous metal smelting: (1) The utilization rate of resource and energy is low in the production process. (2) Large amount of waste emissions have caused prominent environmental problems. (3) The added value of non-ferrous metals is low; besides, the product homogenization is serious. To solve these problems, a novel intelligent manufacturing mode with human-cyber-physical(HCP)collaboration and fusion is constructed from three aspects: (1) an intelligent manufacturing system with HCP collaboration and fusion, (2) a service-based ecosystem platform, and (3) a sustainable business model. The proposed manufacturing mode can make the process of non-ferrous smelting safe, efficient, intelligent, and green. A case study on the largest copper smelting enterprise in the world elaborates the digital twin-based manufacturing system collaborative platform architecture, the framework of the service-based ecosystem platform, and the sustainable business model canvas, which are built by the proposed intelligent manufacturing mode. The results of this paper contribute to the research and practice of non-ferrous metals enterprise’s manufacturing mode.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article.

Code availability

Code availability is applicable by the website on http://61.149.254.174:3889/login (uid:ysy/psw:111,111).

References

  1. Yuan X, Gui W, Chen X, et al (2018) Transforming and upgrading nonferrous metal industry with artificial intelligence. Chinese J Eng Sci 20:59. https://doi.org/10.15302/j-sscae-2018.04.010

  2. Ahmad T, Zhang D, Huang C et al (2021) Artificial intelligence in sustainable energy industry: status quo, challenges and opportunities. J Clean Prod 289:125834. https://doi.org/10.1016/j.jclepro.2021.125834

    Article  Google Scholar 

  3. Haenlein M, Kaplan A (2019) A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev 61:5–14. https://doi.org/10.1177/0008125619864925

    Article  Google Scholar 

  4. Wu F, Lu C, Zhu M et al (2020) Towards a new generation of artificial intelligence in China. Nat Mach Intell 2:312–316. https://doi.org/10.1038/s42256-020-0183-4

    Article  Google Scholar 

  5. Mosenia A, Jha NK (2017) A comprehensive study of security of internet-of-things. IEEE Trans Emerg Top Comput 5:586–602. https://doi.org/10.1109/TETC.2016.2606384

    Article  Google Scholar 

  6. Sisinni E, Saifullah A, Han S et al (2018) Industrial internet of things: challenges, opportunities, and directions. IEEE Trans Ind Informatics 14:4724–4734. https://doi.org/10.1109/TII.2018.2852491

    Article  Google Scholar 

  7. Varghese B, Buyya R (2018) Next generation cloud computing: new trends and research directions. Futur Gener Comput Syst 79:849–861. https://doi.org/10.1016/j.future.2017.09.020

    Article  Google Scholar 

  8. Ren J, Yu G, He Y, Li GY (2019) Collaborative cloud and edge computing for latency minimization. IEEE Trans Veh Technol 68:5031–5044. https://doi.org/10.1109/TVT.2019.2904244

    Article  Google Scholar 

  9. Iqbal R, Doctor F, More B et al (2020) Big data analytics: computational intelligence techniques and application areas. Technol Forecast Soc Change 153:1. https://doi.org/10.1016/j.techfore.2018.03.024

    Article  Google Scholar 

  10. Cui Y, Kara S, Chan KC (2020) Manufacturing big data ecosystem: a systematic literature review. Robot Comput Integr Manuf 62:101861. https://doi.org/10.1016/j.rcim.2019.101861

    Article  Google Scholar 

  11. Tao F, Zhang H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art. IEEE Trans Ind Informatics 15:2405–2415. https://doi.org/10.1109/TII.2018.2873186

    Article  Google Scholar 

  12. Koren Y (2010) The global manufacturing revolution: product-process-business integration and reconfigurable systems. John Wiley & Sons

    Book  Google Scholar 

  13. Zhou J, Li P, Zhou Y et al (2018) Toward new-generation intelligent manufacturing. Engineering 4:11–20. https://doi.org/10.1016/j.eng.2018.01.002

    Article  Google Scholar 

  14. Aazam M, Zeadally S, Harras KA (2018) Deploying fog computing in industrial internet of things and industry 4.0. IEEE Trans Ind Informatics 14:4674–4682. https://doi.org/10.1109/TII.2018.2855198

    Article  Google Scholar 

  15. Boyes H, Hallaq B, Cunningham J, Watson T (2018) The industrial internet of things (IIoT): an analysis framework. Comput Ind 101:1–12. https://doi.org/10.1016/j.compind.2018.04.015

    Article  Google Scholar 

  16. Beier G, Ullrich A, Niehoff S, et al (2020) Industry 4.0: how it is defined from a sociotechnical perspective and how much sustainability it includes – a literature review. J Clean Prod 259:120856. https://doi.org/10.1016/j.jclepro.2020.120856

  17. Ghobakhloo M (2020) Industry 4.0, digitization, and opportunities for sustainability. J Clean Prod 252:119869. https://doi.org/10.1016/j.jclepro.2019.119869

  18. Keiningham T, Aksoy L, Bruce HL et al (2020) Customer experience driven business model innovation. J Bus Res 116:431–440. https://doi.org/10.1016/j.jbusres.2019.08.003

    Article  Google Scholar 

  19. Laukkanen M, Tura N (2020) The potential of sharing economy business models for sustainable value creation. J Clean Prod 253:120004. https://doi.org/10.1016/j.jclepro.2020.120004

    Article  Google Scholar 

  20. Savolainen J, Collan M (2020) How additive manufacturing technology changes business models? – review of literature. Addit Manuf 32:101070. https://doi.org/10.1016/j.addma.2020.101070

    Article  Google Scholar 

  21. Saebi T, Lien L, Foss NJ (2017) What drives business model adaptation? The impact of opportunities, threats and strategic orientation. Long Range Plann 50:567–581. https://doi.org/10.1016/j.lrp.2016.06.006

    Article  Google Scholar 

  22. Teece DJ (2010) Business models, business strategy and innovation. Long Range Plann 43:172–194. https://doi.org/10.1016/j.lrp.2009.07.003

    Article  Google Scholar 

  23. Parry Z (2014) Book review: business model generation: a handbook for visionaries, game changers, and challengers. Int J Entrep Innov 15:137–138. https://doi.org/10.5367/ijei.2014.0149

    Article  Google Scholar 

  24. Joyce A, Paquin RL (2016) The triple layered business model canvas: a tool to design more sustainable business models. J Clean Prod 135:1474–1486. https://doi.org/10.1016/j.jclepro.2016.06.067

    Article  Google Scholar 

  25. Wirtz BW, Pistoia A, Ullrich S, Göttel V (2016) Business models: origin, development and future research perspectives. Long Range Plann 49:36–54. https://doi.org/10.1016/j.lrp.2015.04.001

    Article  Google Scholar 

  26. Foss NJ, Saebi T (2017) Fifteen years of research on business model innovation. J Manage 43:200–227. https://doi.org/10.1177/0149206316675927

    Article  Google Scholar 

  27. Li F (2020) The digital transformation of business models in the creative industries: a holistic framework and emerging trends. Technovation 92–93:102012. https://doi.org/10.1016/j.technovation.2017.12.004

    Article  Google Scholar 

  28. Chesbrough H (2010) Business model innovation: opportunities and barriers. Long Range Plann 43:354–363. https://doi.org/10.1016/j.lrp.2009.07.010

    Article  Google Scholar 

  29. Frank AG, Dalenogare LS, Ayala NF (2019) Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ 210:15–26. https://doi.org/10.1016/j.ijpe.2019.01.004

    Article  Google Scholar 

  30. Rojko A (2017) Industry 4.0 concept: background and overview. Int J Interact Mob Technol 11:77–90. https://doi.org/10.3991/ijim.v11i5.7072

    Article  Google Scholar 

  31. Ślusarczyk B, Haseeb M, Hussain HI (2019) Fourth industrial revolution: a way forward to attain better performance in the textile industry. Eng Manag Prod Serv 11:52–69. https://doi.org/10.2478/emj-2019-0011

    Article  Google Scholar 

  32. Ibarra D, Ganzarain J, Igartua JI (2018) Business model innovation through industry 4.0: a review. Procedia Manuf 22:4–10. https://doi.org/10.1016/j.promfg.2018.03.002

    Article  Google Scholar 

  33. Beverungen D, Müller O, Matzner M et al (2019) Conceptualizing smart service systems Electron Mark 29:7–18. https://doi.org/10.1007/s12525-017-0270-5

    Article  Google Scholar 

Download references

Funding

This work was supported partly by the National Key R&D Program of China under 2019YFB1704700 and partly by the Natural Science Foundation of China under 71690234.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Qing Liu, Min Liu & Zichun Wang

  2. CINF Engineering Corporation Limited, Changsha, 410000, China

    Feng Yan

  3. Guixi Smelter, Yingtan, 335000, China

    Yingyi Ma

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Weiming Shen

Authors
  1. Qing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zichun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingyi Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Weiming Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qing Liu: Writing—original draft, writing—review and editing, investigation, methodology, experiments conduction.

Min Liu: Writing—review and editing, project administration, methodology.

Zichun Wang: Investigation, software development, visualization, data analysis, validation of the software tools.

Feng Yan: Experiments conduction.

Yingyi Ma: Experiments conduction.

Weiming Shen: Advice and supervision.

Corresponding authors

Correspondence to Min Liu or Feng Yan.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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

Liu, Q., Liu, M., Wang, Z. et al. A novel intelligent manufacturing mode with human-cyber-physical collaboration and fusion in the non-ferrous metal industry. Int J Adv Manuf Technol 119, 549–569 (2022). https://doi.org/10.1007/s00170-021-08250-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08250-5

Keywords

Search

Navigation