Main Research Areas

Process Monitoring, Diagnostics, Prognostics and Health Management

  • Integration of physics-based models and data analytics for enhanced degradation modeling, performance analysis, and remaining useful life prediction of engineering systems

Stochastic Modeling and Optimization for Complex Systems

  • Modeling and analysis of system dynamics under multi-source uncertainties — integration with machine learning, simulation, and optimization
  • Applications in battery manufacturing/remanufacturing, assembly systems, roll-to-roll manufacturing systems, etc.

Decision Support Tools for Intelligent Manufacturing Systems

  • Design of optimal predictive and preventive strategies for manufacturing equipment
    operations and maintenance in service environment

Funded Projects:

  1. (PI) NSF-CAREER: A Unified Machine Perception and Iterative Learning Control Framework for High-Precision Micro-Manufacturing Processes
  2. (PI) NSF-CMMI: Manufacturing USA: Precision Alignment of Roll-to-Roll Printing Electronics Through Spatial Variation Modeling and Virtual Sensing Based Control
  3. (Co-PI) NSF: Integrative Manufacturing and Production Engineering Education Leveraging Data Science Program (IMPEL)
  4. (Co-PI) ARL: Development of Non-destructive On-Field Quality Control Systems (NOQCS) for a Field Deployable Cold-Spray System
  5. (PI) Adaptive AI-based Automated Fault Notification System, Industry sponsor
  6. (PI) Data-Driven Inference Modeling for Multi-objective Decision Making, Industry sponsor
  7. (PI) Achieving Smart Factory through Predictive Dynamic Scheduling, Manufacturing USA Institute – Manufacturing Times Digital (MxD)
  8. TIER1 FY2021: Industrial AI-Assisted Synthesis of 2D Quantum Materials, Role: PI, Sponsor: Northeastern University
  9. TIER1 FY2019: Multi-Agent Reinforcement Learning Framework for Learning Coordination and Decision-Making, Role: PI, Sponsor: Northeastern University