Optimized Additive Manufacturing Technology Using Digital Twins and Cyber Physical Systems
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.
KeywordsCyber physical systems Additive manufacturing Digital twins Industry 4.0
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.
- 1.Gibson, I., Rosen, D., Strucker, B.: Additive Manufacturing Technologies 3D Printing, rapid Prototyping and Direct Digital Manufacturing 2nd Edition (2015)Google Scholar
- 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
- 6.Baumann, F., Roller, D.: Additive manufacturing, cloud-based 3D printing and associated services—overview. J. Manuf. Mater. Process (2017)Google Scholar
- 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
- 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
- 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.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