Researchers: Miguel Saez
The objective is to develop a framework for modeling manufacturing machines as Cyber-Physical systems to support anomaly detection and multi-objective optimization using “Big Data” and “High-Performance Computing”
We study of manufacturing systems as cyber-physical systems to support modeling, simulation, and control of different variables and for anomaly detection and multi-objective optimization. The proposed solution aims to help improve manufacturing systems in the following ways:
- Provide a framework to model cyber-physical systems in manufacturing for anomaly detection and reliability analysis.
- Develop modular hybrid model at machine and system level to support real-time simulation.
- Formulate multi-objective optimization of manufacturing systems operations.
- Study different strategies for extraction, transmission, and load of plant floor data.
Related Publications:
- M. Saez, F. Maturana, K. Barton, and D. Tilbury, “Real-time manufacturing machine and system performance monitoring using internet of things,” Transactions in Automation Science and Engineering (Accepted for publication), 2017.
- M. Saez, F. Maturana, K. Barton, and D. Tilbury, “Real-time hybrid simulation of manufacturing systems for performance analysis and control,” Automation Science and Engineering, IEEE International Conference on, pp. 526-531, 2015.
- M. Saez, F. Maturana, K. Barton, and D. Tilbury, “Anomaly detection and productivity analysis for cyber-physical systems in manufacturing,” Automation Science and Engineering, IEEE International Conference on, 2017.