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

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Problem Statement:

  • 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

Project Goal:

Demonstrate energy efficient metal material processing at Arconic facility through advanced sensing, automated process monitoring and model based controls.

 
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Technical Approach:

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 (M6)

  • Design of efficient computational framework with statistical and forecasting models (M12)

  • Development of In-Process Monitoring Capability with online decision making tools (M16)

  • Process Improvement Through Optimization and Control for operations and maintenance (M16)

  • Integration and Validation for Process Improvement Tools at Arconic facility (M21)

Potential Impact:

  • 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

Benefit to CESMII:

  • 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

Project Cost Share and Duration:

Project Duration: 21 months, CESMII Cost Share: 70%, Member Cost Share: 30%

 
 
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