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Week 7 Summary: Time-series and process monitoring

Cross-Book Summary

1. Sequential Data & Memory

Temporal Dimension: Process logs are sequential; order matters.
RNNs: Possess hidden states that act as memory.
Vanishing Gradients: Basic RNNs struggle with long-term dependencies.
LSTMs/GRUs: Use gates to selectively remember/forget information.

2. Materials Process Monitoring

Preprocessing: Smoothing and "Triggering" to extract cycles.
Anomaly Detection: Large prediction deviations signal defects.
Surrogate Models: Fast RNN/LSTM replacements for slow simulations.

90-Minute Lecture Strategy

Part 1: Processing as Sequence

Time-dependency of microstructure.
Sensor types (1D vs. logs).
Event vs. Continuous.

Part 2: Preprocessing

Denoising filters.
Triggering cycles.
Temporal Feature Engineering.

Part 3: RNNs

Unrolled RNN structure.
Vanishing Gradient problem.
LSTM and GRU mechanics.

Part 4: Case Studies

Predictive Maintenance.
AM melt pool stability.
Dilatometry phase predictions.

Part 5: Challenges

Non-stationarity and machine drift.
Transformers for 1D data.
Closed-loop control outlook.

Quarto Website Update (Summary)

Summary for ML-PC Week 7:

Applies ML to Time-Series Data for process monitoring.
Introduces RNNs and LSTMs for sequential dependencies.
Details essential preprocessing like smoothing and triggering.
Covers anomaly detection and process outcome prediction.