Classified FDM printer faults (blockages, material run-out) from acoustic emission signals with a tuned Random Forest, handling heavy class imbalance.

A classification system for real-time health monitoring of fused deposition modeling (FDM) printers. Acoustic emission (AE) signals recorded during printing were used to detect four conditions: nozzle semi-blockage, full obstruction, material run-out, and normal operation.

What I did

• Model: designed and trained a Random Forest classifier on AE features, chosen for strong performance with minimal preprocessing.
• Data: standardized a high-dimensional AE feature set and addressed severe class imbalance, since normal operation far outnumbers faults.
• Tuning: used grid-search cross-validation to tune hyperparameters and limit overfitting.
• Dimensionality: compared the full feature set against PCA-reduced data, showing the full set is needed to capture subtle run-out and blockage signatures.

Result

The tuned model reached 52% average test accuracy across the four classes, with particularly strong detection of material run-out. It showed AE signals are viable for printer health monitoring, while highlighting how hard subtle blockages are to classify.