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Meeting MS&T21: Materials Science & Technology
Symposium Additive Manufacturing of Metals: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title High-speed Observations and Quantification of Spatter in Laser Powder Bed Fusion
Author(s) Christian Gobert, Jack Beuth, Evan Diewald
On-Site Speaker (Planned) Christian Gobert
Abstract Scope Mitigation of spatter is desired to improve material performance in laser powder bed fusion processes. Quantifying spatter particles ejected from the melt pool can shed light on the phenomenon. Using a machine learning identification and tracking tool, spatter particles were quantified in high-speed videos observations of the laser powder bed fusion process. Spatter generation related to key process variables and materials is studied in the aim of developing process map approach to mitigating spatter.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing Measurement Science of Laser Powder Bed Fusion (LPBF) Process Monitoring Applying Thermal Imaging
Combined In-situ Monitoring of Meltpool, Powder Layer, and Part Topography for Laser Powder Bed Fusion (LPBF) Based Metal Additive Manufacturing
Defect Recognition and Improvement in Ti-6Al-4V Fabrication by In-situ Monitoring and Feedback System of Directed Energy Deposition LAMDA 200
Functionally Graded Material Development by Leveraging Ultrasonic Grain Refinement in Additive Manufactured Nickel 718
High-speed Observations and Quantification of Spatter in Laser Powder Bed Fusion
In-situ Sensing in Processing Parameter Development for Bismuth Telluride Bulk Part Fabrication Using Laser Powder Bed Fusion
Innovative and Practical Approaches to Laser Powder Bed Fusion Sensing and Process Enhancement
Laser Powder Bed Fusion of Tall Thin Walled Structures: Dimensional Inaccuracy Due to Local Buckling, and In Situ Infrared Imaging for Early Failure Detection
Materials Characterization of Anomalies Identified Through In-situ Process Monitoring Data Analytics
Melt Pool Level Flaw Detection in Laser Hot Wire Additive Manufacturing Using a Trained Convolutional Long Short Term Memory Autoencoder
Physics Guided Machine Learning DED Melt Pool Width Prediction
Plenoptic Imaging for In-situ PIV and Melt Pool Monitoring in Laser Directed Energy Deposition
Studying the Effect of Inert Gases on Thermal Behavior in Laser Powder Bed Fusion Using In Situ Monitoring and Similarity Analysis
Ultrasonics for Monitoring Melt Pool Dynamics and Solidification
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