Skip to main content

Disruptive Innovation in Mining Industry 4.0

  • Conference paper
  • First Online:
Distributed Sensing and Intelligent Systems

Part of the book series: Studies in Distributed Intelligence ((SDI))

Abstract

The mining industry is facing global challenges with undergoing significant market-changing demand and competitiveness. It has become imperative for mining companies to keep up with the real-time visibility on production quality and control, cycle times, machine status, and other important operational variables. In order to reach smart manufacturing, mining companies must seize the opportunity of Industry 4.0 to leverage the advancement of information technology. This chapter reviews the current research studies about the smart manufacturing in Mining Industry 4.0 that stands on the intersection of the emerging information technologies (IT 4.0), mining industry, and innovation. The review discusses and analyzes a plethora of innovative technologies that assist miners in their roles such as the Internet of Things, cyber-physical systems, digital twins, and so forth. These disruptive technologies address the issue of cyber-physical integration (CPI). They are the pillars of smart manufacturing in the Mining Industry 4.0. The review provides insights about the next horizon of the use of disruptive technologies in the mining industry toward data-driven smart manufacturing.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 239.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 309.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ghodrati, B., Hoseinie, S., & Garmabaki, A. (2015). Reliability considerations in automated mining systems. International Journal of Mining, Reclamation and Environment, 29(15), 404–418.

    Google Scholar 

  2. Sishi, M. N., & Telukdarie, A. (2017). Implementation of Industry 4.0 technologies in the mining industry: A case study. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore (pp. 201–205). https://doi.org/10.1109/IEEM.2017.8289880.

  3. Cheng, Y., Zhang, Y., Ji, P., et al. (2018). The International Journal of Advanced Manufacturing Technology, 97, 1209. https://doi.org/10.1007/s00170-018-2001-2.

    Article  Google Scholar 

  4. Tao, F., & Qinglin, Q. (2017). New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2017.2723764.

  5. Wang, L., & Shih, A. (2016). Challenges in smart manufacturing. Journal of Manufacturing Systems, 40(SI), 1.

    Google Scholar 

  6. Preuveneers, D., & Ilie-Zudor, E. (2017). The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0. Journal of Ambient Intelligence and Smart Environments, 9, 287–298. https://doi.org/10.3233/AIS-170432.

    Article  Google Scholar 

  7. Schwab, K. (2019). The Fourth Industrial Revolution: What It Means and How to Respond. Retrieved from https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution (Last accessed October 5, 2019).

    Google Scholar 

  8. Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517.

    Article  Google Scholar 

  9. Peruzzini, M., Grandi, F., & Pellicciari, M. (2017). Benchmarking of tools for user experience analysis in industry 4.0. Procedia Manufacturing, 11, 806–813.

    Article  Google Scholar 

  10. Industry 4.0. (2015). How to navigate digitalization of the manufacturing sector. McKinsey Digital 2015.

    Google Scholar 

  11. Cai, H., Xu, L., Xu, B., Xie, C., Qin, S., & Jiang, L. (2014). IoT-based configurable information service platform for product lifecycle management. IEEE Transactions on Industrial Informatics, 10(2), 1558–1567.

    Article  Google Scholar 

  12. Sabar, M., Jayaweera, P., & Edirisuriya, E. (2016). SAIF-refactored efficiency interpolation in the HL7 specifications development paradigm. Journal of Industrial Information Integration, 4, 35–41.

    Article  Google Scholar 

  13. Lee, J., Bagheri, B. & Kao, H. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3(2015), 18–23.

    Article  Google Scholar 

  14. 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 (pp. 697–701).

    Google Scholar 

  15. Cheng, Y., Chen, K., Sun, H., Zhang, Y. & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1–13. https://doi.org/10.1016/j.jii.2017.08.001. Cheng, Y., Chen, K., Sun, H., Zhang, Y. & Tao, F. (2017). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration.

  16. Zhong, R. Y., Dai, Q. Y., Qu, T., Hu, G. J., & Huang, G. Q. (2013). RFID-enabled real-time manufacturing execution system for mass-customization production. Robotics and Computer-Integrated Manufacturing, 29(2), 283–292.

    Article  Google Scholar 

  17. Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of Things. International Journal of Communication Systems, 25(9), 1101–1102.

    Article  Google Scholar 

  18. Zhai, C., Zou, Z., Chen, Q., Xu, L., Zheng, L., & Tenhunen, H. (2016). Delay-aware and reliability-aware contention-free MF-TDMA protocol for automated RFID monitoring in industrial IoT. Journal of Industrial Information Integration, 3, 8–19.

    Google Scholar 

  19. Finogeev, A. G., & Finogeev. A. A. (2017). Information attacks and security in wireless sensor networks of industrial SCADA systems. Journal of Industrial Information Integration, 5, 6–16.

    Article  Google Scholar 

  20. Bag, G., Pang, Z., Johansson, M., Min, X., & Zhu. S. (2016). Engineering friendly tool to estimate battery life of a wireless sensor node. Journal of Industrial Information Integration, 4, 8–14.

    Article  Google Scholar 

  21. Li, S., & Xu, L. (2017). Securing the Internet of Things. Syngress: Elsevier.

    Google Scholar 

  22. Mitra, A., Kundu, A., Chattopadhyay, M. & Chattopadhyay, S. (2017). A cost-efficient one time password-based authentication in cloud environment using equal length cellular automata. Journal of Industrial Information Integration, 5, 17–25.

    Article  Google Scholar 

  23. Zhou, K., Taigang, L., & Lifeng, Z. (2015). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie (pp. 2147–2152).

    Google Scholar 

  24. Xiang, F., Jiang, G. Z., Xu, L. L., & Wang, N. X. (2016). The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, 84(1–4), 59–70.

    Article  Google Scholar 

  25. Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The hadoop distributed file system. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (MSST ’10) (pp. 1–10). Washington, DC, USA: IEEE Computer Society.

    Google Scholar 

  26. Tao, F., Cheng, Y., Xu, L., Zhang, L., & Li, BH. (2014). CCIoT-CMfg: cloud computing and Internet of Things based cloud manufacturing service system. IEEE Transactions on Industrial Informatics 10(2), 1435–1442.

    Article  Google Scholar 

  27. Moghaddam, M., & Nof, S. Y. (2017). Collaborative service-component integration in cloud manufacturing. International Journal of Production Research, Published online 13 September 2017.

    Google Scholar 

  28. Mourtzis, D., & Vlachou, E. (2016). Cloud-based cyber-physical systems and quality of services. The TQM Journal, 28(5), 704–733.

    Article  Google Scholar 

  29. Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48(Part C), 157–169.

    Google Scholar 

  30. IBM Web Site. (2019). Watson, Available at: https://www.ibm.com/watson (Last accessed October 5, 2019).

  31. Edward, L. (2015). The past, present and the future of cyber-physical systems: a focus on models. Sensors, 15(3), 4837–4869.

    Article  Google Scholar 

  32. Bhowmik, S. (2019). Digital Twin of Subsea Pipelines: Conceptual Design Integrating IoT, Machine Learning and Data Analytics. https://doi.org/10.4043/29455-MS

    Google Scholar 

  33. Zhong, R. Y., Peng, Y., Xue, F., Fang, J., Zou, W., Luo, H., et al. (2017). Prefabricated construction enabled by the Internet-of-Things. Automation in Construction, 76, 59–70.

    Article  Google Scholar 

  34. WCPS. (2019). Wireless cyber-physical simulator, available at: http://wsn.cse.wustl.edu/index.php/WCPS:_Wireless_Cyber-Physical_Simulator (Last accessed October 5, 2019).

    Google Scholar 

  35. Mourtzis, D., & Vlachou, E. (2016). Cloud-based cyber physical systems and quality of services. TQM Journal, 28. https://doi.org/10.1108/TQM-10-2015-0133.

  36. Liu, Y. K., & Xu, X. (2017). Industry 4.0 and cloud manufacturing: A comparative analysis. Journal of Manufacturing Science and Engineering, 139(3), 034701-1–8.

    Google Scholar 

  37. Ubimax Web Site. (2019). Innovative Solutions. https://www.ubimax.com/en/solutions/ (Last accessed October 5, 2019).

  38. Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629.

    Article  Google Scholar 

  39. Goldman Sachs Global Investment Research Technical Report: Virtual and Augmented Reality—Understanding the Race for the Next Computing Platform, Dec. 2017, Available: http://www.goldmansachs.com/our-thinking/pages/technology-driving-innovation-folder/virtual-and-augmented-reality/report.pdf. (Last accessed October 5, 2019).

  40. Fraga-Lamas, P., Fernández-Caramés, T. M., Blanco-Novoa, Ó., & Vilar-Montesinos, M. A. (2018). A review on industrial augmented reality systems for the Industry 4.0 shipyard. IEEE Access, 6, 13358–13375.

    Article  Google Scholar 

  41. Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56, 1–22.

    Article  Google Scholar 

  42. Cyber-Physical Systems NIST. (2019). Available at: https://www.nist.gov/el/cyber-physical-systems (Last accessed October 5, 2019).

  43. Liu, Y., Peng, Y., Wang, B., Yao, S., & Liu, Z. (2017). Review on cyber–physical systems. IEEE/CAA Journal of Automatica Sinica, 4(1), 27–40.

    Article  Google Scholar 

  44. Giordano, A., Spezzano, G. & Vinci, A. (2014). Rainbow: an intelligent platform for large-scale networked cyber-physical systems. In Proceedings of the 5th International Workshop on Networks of Cooperating Objects of Smart Cities (UBICITEC), Berlin, April 14.

    Google Scholar 

  45. MathWorks. (2019). Model-based design of cyber-physical systems in MATLAB and Simulink, available at: www.mathworks.com/discovery/cyber-physical-systems.html (Last accessed October 5, 2019).

  46. Henriksson, D., & Elmqvist, H. (2011). Cyber-physical systems modeling and simulation with Modelica. In Proceedings 8th Modelica Conference, Dresden, June 20–22.

    Google Scholar 

  47. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. (2019). Digital twin in industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15, 2405–2415.

    Article  Google Scholar 

  48. Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), 141–144.

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Liu, Z., Meyendorf, N., & Mrad, N. (2018). The role of data fusion in predictive maintenance using digital twin. AIP Conference Proceedings, 1949, 020023.

    Article  Google Scholar 

  51. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access, 6, 3585–3593.

    Article  Google Scholar 

  52. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang C. (2016). Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168.

    Article  Google Scholar 

  53. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: a review. Engineering, 3(5), 616–630.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sara Qassimi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qassimi, S., Abdelwahed, E.H. (2022). Disruptive Innovation in Mining Industry 4.0. In: Elhoseny, M., Yuan, X., Krit, Sd. (eds) Distributed Sensing and Intelligent Systems. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-64258-7_28

Download citation

Publish with us

Policies and ethics