Intelligent Monitoring of Manufacturing Processes

In precision forming, batch anomalies are often invisible until final inspection — by which time entire lots have already been scrapped. This project writes forming mechanics into the algorithm: each production cycle signal is segmented according to the actual die motion phases, probabilistic priors tell the model which segments to watch, and a detector trained on normal parts only learns the boundary of "normal," classifying every part as pass, anomaly, or review-needed within the production takt time. Validated on two real production lines, the method achieves 99.32% and 94.92% accuracy under zero anomaly-sample training.

A large-model-driven adversarial semi-supervised training scheme that combines a handful of destructive-test samples with massive production-line sensor data to predict the mechanical properties of large die-cast parts, region by region, online

Anomaly detection, fault diagnosis, and virtual metrology on the shop floor are typically three separate models, each retrained from scratch whenever equipment or processes change. The team built an industrial time-series foundation model: self-supervised pre-training on unlabeled signals first teaches a shared backbone the general dynamics of time series; the backbone is then frozen and each downstream task attaches only a lightweight head, fine-tuned with minimal labeled data. Validated on real stamping and injection-molding lines, the single backbone delivers an anomaly-detection F1 of 97.0% with zero false alarms, a fault-diagnosis Macro-F1 of 94.6%, and a virtual-metrology RMSE of 0.0259 mm — all outperforming individually trained baselines.

Online virtual metrology with uncertainty evaluation and semi-automated fault-detection configuration for CMP and related processes, applied with Applied Materials