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Meeting MS&T26: Materials Science & Technology
Symposium Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title Smoke, Mirrors, and Melt Pools: An Assessment of Commercial PBF-LB In-Situ Process Monitoring Solutions
Author(s) Abdalla R. Nassar, Liam Adler-pollock, Christopher Apple
On-Site Speaker (Planned) Abdalla R. Nassar
Abstract Scope In the context of Laser Powder Bed Fusion (PBF-LB), the ability to detect and correct process drifts and anomalies has long been the "Holy Grail" of additive manufacturing. In-situ inspection promises near-real-time identification of flaws and non-conformances during production. Indeed, Lab-scale and commercial systems, many of which incorporate machine learning, have been developed and demonstrated to identify some process and part flaws. This presentation elucidates the underpinnings of current, commercial in-situ monitoring technologies and presents a real-world comparison of solutions from five companies— Addiguru, Additive Assurance, Applied Optimization, JENTEK Sensors, and Phase3D—carried out on as part of an ASTRO-ASTM-InSPIRE sponsored challenge. Furthermore, we demonstrate that combining high-resolution melt pool imaging with near-infrared long-exposure imaging enables the detection of flaws on the order of tens of microns in thin-walled lattice structures. Applications include quality assurance for heat exchangers and other thin-walled structural components.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AMDiffusion: Domain-Adaptive Diffusion Modeling for Causal Data Fusion in Additive Manufacturing
Beyond Deep Learning: A Bayesian-Optimized Computer Vision Framework for Rapid Spatter Detection and Tracking in Laser Powder Bed Fusion
Designing Sensor Systems for Anomaly and Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing
Hybrid Feedforward-Feedback Process Control of Laser Powder Bed Fusion
K2: An Open Architecture Wire-Laser Directed Energy Deposition Testbed for Advanced Control Strategy Development
Large Language Models for In-Situ Interpretation of Defect Signatures in Powder Bed Fusion
Rapid Modeling and Prediction of Thermal Strain in Laser Powder Bed Fusion
Self-Sensing of 3D-Printed Materials by Measuring the Inductance, Resistance and Capacitance
Smoke, Mirrors, and Melt Pools: An Assessment of Commercial PBF-LB In-Situ Process Monitoring Solutions

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