- By:
- Pike, John A; O'brien, Chris B; Bailey, Benjamin; Bisson, Wesley; Stevens, Jason; Tomlinson, Scott; Studer, Gregory; Villez, Kris Roger Elie
- Page Number:
- 35-44
- Book Title:
- SAMPE 2025 Conference and Exhibition
- Publication Date:
- March 12, 2026
- Publisher Location:
- Curran Associates, New York, United States of America
- Conference Name:
- SAMPE 2025 Conference and Exhibition
- Conference Location:
- Indianapolis, Indiana, United States of America
- Conference Sponsor:
- Society for the Advancement of Material and Process Engineering
- View DOI Listing:
- https://doi.org/10.33599/nasampe/s.25.0260
Abstract
Today, large-scale additive manufacturing with plastics and composite materials requires continuous monitoring by experienced staff to prevent, detect and correct anomalous events affecting the performance of the printed part. We address the complexity of this demanding task by designing a camera-based anomaly detection system utilizing probabilistic principal component analysis (PPCA). This is a machine learning technique is trained with thermal images collected during normal operation of the large-scale printer (Cincinnati BAAM). This technique is advantageous for practical applications as there is no need to artificially introduce anomalous conditions into model training. During deployment, we challenge this model by introducing deliberate variations of the extruder speed. We reduce extrusion speed to a lower level, between 70 and 95% of the nominal value to collected test images. Our results show that images are easily identified as anomalous for extruder speeds at or below 85% of the nominal speed, meaning that an anomalous reduction of the material deposition rate can be detected within seconds of its onset. We show that our results are robust to (a) camera-to-camera variability and (b) print-to-print variability.
