Energy Efficient Material Processing Through Automated Process Monitoring and Controls
Project Lead: Virginia Tech
Partners: University of Virginia, Pennsylvania State University, Arconic, Commonwealth Center for Advanced Manufacturing
Member % Cost Share: 30%
CESMII % Cost Share: 70%
Duration: 21 Months
- Wireless sensor nodes impacted by electromagnetic, vibration, and thermal noise
- High volume of data raises challenges related to data transfer and processing
- Complex, nonlinear, dynamic mfg systems – challenges in real time decision making
- Existing sensor based decision making models are computationally complex
Demonstrate energy efficient metal material processing at Arconic facility through advanced sensing, automated process monitoring and model based controls.
Self-powered wireless sensor nodes and deployment of energy harvesting approaches; Efficient computational framework to acquire, post-process, and synthesize large quantities of sensory data in real time; In-process monitoring capability with offline big-data analysis techniques; Closed-loop system to enable real-time, model-based control for energy consumption optimization.
Key Tasks & Milestones
- Development of data acquisition system and appropriate wireless sensor devices
- Design of efficient computational framework with statistical and forecasting models
- Development of In-Process Monitoring Capability with online decision making tools
- Process Improvement Through Optimization and Control for operations and maintenance
- Integration and Validation for Process Improvement Tools at Arconic facility
- Intelligently monitoring and controlling manufacturing processes for a relevant testbed, the usage of 800,000 to 1,000,000 kW of power could be affected
- A reduction of only 15% could save $100,000 annually for a single process
- Widespread adoption throughout the world increases the cascade of possible resource and cost saving processes
- Contribution to the SM Platform™ core technologies in process monitoring and control
- Sensor instrumentation, computational models, and analytics package will be available to other CESMII members with similar processes.
- Contribution to the SM Platform™ core technologies in process monitoring and control.