Project Goals
Immediate
- Defect detection and identification
- Anomaly detection
- Process-physics integrated learning
Mid-term
- Robust model development
- Model explainability and interpretability
- Out-of-distribution (OOD) detection
Long-term
- Life-long learning (LML)
- Learning without forgetting (LwF)
- OOD integrated learning
Research team: Vignesh Selvaraj
Publications
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[1] Glaeser, A., Selvaraj, V., Lee, S., Hwang, Y., Lee, K., Lee, N., ... & Min, S. (2021). Applications of deep learning for fault detection in industrial cold forging. International Journal of Production Research, 59(16), 4826-4835.
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[2] Glaeser, A., Selvaraj, V., Lee, K., Lee, N., Hwang, Y., Lee, S., ... & Min, S. (2020). Remote machine mode detection in cold forging using vibration signal. Procedia Manufacturing, 48, 908-914.
Industrial Case Study
Through this work we aim to develop systematic approaches to build robust and trustworthy AI for industrial applications. Our work focuses not only building reliable physics informed models for industrial applications and machines, but also ensures their reliability in field. Through our work, we aim to facilitate industries’ transition to industry 4.0 by enabling widespread deployment of the developed models.
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