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Optimized Additive Manufacturing Technology Using Digital Twins and Cyber Physical Systems

  • Sreekanth Vasudev Nagar
  • Arjun C. Chandrashekar
  • Manish SuvarnaEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 80)

Abstract

The latest industrial revolution, Industry 4.0, has called upon for the needs of integrating the processes involved in the production systems with the advanced information technologies powered by artificial intelligence and data driven analytical solutions. In the past decade, we have witnessed how the simulation models developed by analyzing the Big Data generated by the manufacturing units have aided in boosting the productivity of the industry and give rise to the concept of smart manufacturing; especially in the manufacturing sectors lead by additive manufacturing technology. A 3D printer is a classic example of an additive manufacturing machine and hence has been considered as the framework of study in this research. Deployment & development of digital twin technology will engage the manufacturing systems in a heuristic cyber domain that will help the manufacturing industries to achieve larger productivity with reduced downtime. The proposed digital twin model of the 3D printer shows a framework for developing a machine learning module to reduce and replace the standard defects and reducing the data transmission and data overload in wireless networks.

Keywords

Cyber physical systems Additive manufacturing Digital twins Industry 4.0 

Notes

Acknowledgements

The authors would like to acknowledge BMS College of Engineering 3D printing Lab and Product Innovation lab funded by Dassault Systemes and 3D PLM Bangalore for providing the infrastructure and software for conducting the research.

References

  1. 1.
    Gibson, I., Rosen, D., Strucker, B.: Additive Manufacturing Technologies 3D Printing, rapid Prototyping and Direct Digital Manufacturing 2nd Edition (2015)Google Scholar
  2. 2.
    Qi, Q., Tao, F., Zuo, Y., Zhao, D.: Digital twin service towards smart manufacturing. Procedia CIRP 72, 237–242 (2018)CrossRefGoogle Scholar
  3. 3.
    Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  4. 4.
    Lee, J., Ardakani, H.D., Yang, S., Bagheri, B.: Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38, 3–7 (2015)Google Scholar
  5. 5.
    Tao, F., Zhang, M.: Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017)CrossRefGoogle Scholar
  6. 6.
    Baumann, F., Roller, D.: Additive manufacturing, cloud-based 3D printing and associated services—overview. J. Manuf. Mater. Process (2017)Google Scholar
  7. 7.
    Baumann, F., Schön, M., Eichhoff, J., Roller, D.: Concept Development of a Sensor Array for 3D Printer. 3rd International Conference on Ramp-up Management (ICRM). Procedia CIRP 51, 24–31 (2016)CrossRefGoogle Scholar
  8. 8.
    Ayani, M., Ganeback, M., Ng, A.H.: Digital twin: applying emulation for machine reconditioning. Procedia CIRP 72, 243–248 (2018)CrossRefGoogle Scholar
  9. 9.
    Zhang, H., Liu, Q., Chen, X., Zhang, D., Leng, J.: A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. Special section on key technologies for smart factory of industry 4.0. 5, 26901–26911 (2017)CrossRefGoogle Scholar
  10. 10.
    Krolczyk, G., Raos, P., Legutko, S.: Experimental analysis of surface roughness and surface texture of machined and fused deposition modelled parts. Tehnički vjesnik 21(1), 217–221 (2014)Google Scholar
  11. 11.
    Nikolakisa, N., Sipsasa, K., Makris, S.: A cyber-physical context-aware system for coordinating human-robot collaboration. Procedia CIRP 72, 27–33 (2018)CrossRefGoogle Scholar
  12. 12.
    Lee, E.A.: Cyber physical systems: design challenges. In: Technical Report No. UCB/EECS-2008-8, 2008, Electrical Engineering and Computer Sciences, University of California at BerkeleyGoogle Scholar
  13. 13.
    Cho, S., May, G., Tourkogiorgis, I., Perez, R., Lazaro, O., de la Maza, B., Kiritsis, D.: A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future. APMS 2018, IFIP AICT 536, pp. 311–317 (2018)CrossRefGoogle Scholar
  14. 14.
    Susto, G.A., Wan, J., Pampuri, S., Zanon, M., Johnston, A.B., O’Hara, P.G., McLoone, S.: An adaptive machine learning decision system for flexible predictive maintenance. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE)Google Scholar
  15. 15.
    Wang, L, Wang, G.: Big data in cyber-physical systems, digital manufacturing and industry 4.0. I.J. Eng. Manuf. 4, 1–8 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sreekanth Vasudev Nagar
    • 1
  • Arjun C. Chandrashekar
    • 1
  • Manish Suvarna
    • 1
    Email author
  1. 1.Department of Mechanical EngineeringB.M.S. College of EngineeringBengaluruIndia

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