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Meeting MS&T26: Materials Science & Technology
Symposium Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title Designing Sensor Systems for Anomaly and Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing
Author(s) Luke Scime, Trevor McDonald, Frank Brinkley, Zackary Snow, Christopher Ledford, Vincent Paquit
On-Site Speaker (Planned) Luke Scime
Abstract Scope In this presentation we discuss the design process for an in-situ sensor system on a laser powder bed fusion printer. This system is capable of observing a wide range of anomalies and supporting the detection of certain types of flaws, such as spatter-induced lack-of-fusion pores. Topics will include camera optics, data acquisition and analysis hardware, network infrastructure, and the data acquisition software pipeline. To achieve high quality imaging, the pipeline begins with a flexible camera driver layer. Next, multiple cameras can be stitched into a single group to improve resolution for long-wave infrared imaging. Individual camera frames are stored in circular buffers to facilitate camera and light triggering and enable advanced pre-processing such as temporal integration of thermal emissions data. Finally, calibration procedures remove perspective distortion, non-linear distortions, and variations in diffuse reflected lighting conditions. This work is part of the Peregrine program. Developed at the Manufacturing Demonstration Facility, Peregrine is deployed at-the-edge for laser, electron beam, and binder jet printers across the DOE and DOD lab complex.

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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