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

1. Prognostics and Health Management (PHM)

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

• Remaining useful life prediction

• Uncertainty-aware prognosis

• Physics-guided/informed learning

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

• Multimodal manufacturing process monitoring

• Fault detection and classification (FDC)

• Quality inspection/prediction

• Uncertainty-aware prediction

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 under limited/imperfect data

5. Engineering Design

• AI-assisted engineering design

• Design optimization and decision support

• Design for manufacturing and manufacturability