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Smart Manufacturing Systems

The manufacturing industry is currently amid the fourth industrial revolution. Industry 4.0 is a holistic vision of future cyber-physical systems that are built on both advanced manufacturing, focused on the physical advances such as additive manufacturing or manufacturing of novel and optimal designs for process intensification, as well as smart manufacturing, focused on the cyber or digital side including data analytics and AI. This digital transformation and embracing of smart technologies, such as sensors, Industrial Internet of Things, smart wearables, artificial intelligence and predictive modeling, paves the way for more efficient, sustainable, adaptive, resilient and effective processes and operations.

Affiliated Faculty

Recent Publications

  1. Cordonier, J.F., Bhattacharyya, D. Torres Arango, M.A., Sierros, K.A., & Gupta, R.K. (2017). Modelling and validation of a direct-write 3D printed track, Proc. Society of Plastics Engineers ANTEC Anaheim, CA.
  2. Kapp, V., May, M. C., Lanza, G., & Wuest, T. (2020). Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems. Journal of Manufacturing and Materials Processing, 4(3), 88.
  3. Lima, F. V., Daoutidis, P., & Tsapatsis, M. (2016). Modeling, optimization, and cost analysis of an IGCC plant with a membrane reactor for carbon capture. AIChE Journal, 62(5), 1568-1580.
  4. Liu, Z., Li, T., Ning, F., Cong, W., Kim, H., Jiang, Q., & Zhang, H. (2019). Effects of deposition variables on molten pool temperature during laser engineered net shaping of Inconel 718 superalloy. The International Journal of Advanced Manufacturing Technology, 102(1-4), 969-976.
  5. He, X., Wang, Y., Bhattacharyya, D., Lima, F. V., & Turton, R. (2018). Dynamic modeling and advanced control of post-combustion CO2 capture plants. Chemical Engineering Research and Design, 131, 430-439.
  6. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1342-1361.
  7. Bankole T S, Bhattacharyya D, Pezzini P, Gebreslassie B, Harun, N F, Tucker D, Bryden, K M. (2020). Multi-Objective Optimal Controlled Variable Selection for a Gas Turbine-Solid Oxide Fuel Cell System using a Multi-Agent Optimization Platform. Industrial & Engineering Chemistry Research, 59, 20058-20070.
  8. Thoben, K. D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International journal of automation technology, 11(1), 4-16.
  9. Zhang, Z., Liu, Z., & Wu, D. (2020). Prediction of Melt Pool Temperature in Directed Energy Deposition Using Machine Learning. Additive Manufacturing, 101692.