Our research focuses on developing robust and generalizable industrial artificial intelligence (AI) methods (e.g., deep learning) for smart manufacturing systems, with applications to monitoring, diagnosis, prognosis, optimization, design, and control.

1. Manufacturing Process Monitoring and Fault Detection & Classification (FDC)

• Multimodal manufacturing process monitoring

• Fault detection and classification (FDC)

• Quality inspection and prediction

• Uncertainty-aware prediction

2. Prognostics and Health Management (PHM)

• Health/degradation monitoring (e.g., tool wear prediction)

• Remaining useful life prediction

• Uncertainty-aware prognosis

• Physics-guided and physics-informed learning

3. AI-Driven Process Optimization and Autonomous Manufacturing

• Manufacturing process optimization

• Intelligent process control

4. Robust and Generalizable Industrial AI

• Domain adaptation and generalization

• Continual and adaptive learning

• Learning from limited and imperfect data

5. Engineering Design

• AI-assisted engineering design

• Design optimization and decision support

• Design for manufacturing and manufacturability