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DIGITAL LIBRARY: SAMPE 2025 | INDIANAPOLIS, IN | MAY 19-22

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Accurate and Fast Anomaly Detection in Additive Composite-Based Manufacturing using Thermal Cameras

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Title: Accurate and Fast Anomaly Detection in Additive Composite-Based Manufacturing using Thermal Cameras

Authors: Jay Pike, Chris O'Brien, Benjamin Bailey, Wesley Bisson, Jason Stevens, Scott Tomlinson, Gregory Studer, Kris Villez

DOI: 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. To alleviate the burden of this tedious and cumbersome task, we develop a camera-based anomaly detection tool based on principal component analysis. This is a machine learning technique that is trained with thermal images collected during normal operation of the large-scale printer (Cincinatti BAAM). The main advantage of the technique is that there is no need to artificially introduce anomalous conditions to train this type of model. During deployment, we challenge this model by introducing deliberate variations of the extruder speed. We reduce this speed to a lower level, between 70 and 80% of the nominal value and collect test images under these conditions. We show that more than 99% of these images are identified as anomalous, meaning that an anomalous reduction of the material deposition rate can be detected within seconds of its onset. At the time of writing, we are also evaluating whether the detection tool is robust to (a) camera replacement, (b) camera position adjustment, and (c) print-to-print variability.

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Conference: SAMPE 2025

Publication Date: 2025/05/19

SKU: TP25-0000000260

Pages: 12

Price: $24.00

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