Efficient Retraining of ML Models for Process Monitoring

The project investigates adapting machine-learning models to changing manufacturing process conditions while minimizing new data and computational effort. Students review adaptive-training approaches, define a possible validation strategy, compare training strategies, and analyze model quality, training time, computational effort, and possible catastrophic forgetting. Potential applications include tool-wear monitoring and quality estimation in VAD.