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Meeting MS&T21: Materials Science & Technology
Symposium Additive Manufacturing of Metals: Equipment, Instrumentation and In-Situ Process Monitoring
Presentation Title Melt Pool Level Flaw Detection in Laser Hot Wire Additive Manufacturing Using a Trained Convolutional Long Short Term Memory Autoencoder
Author(s) Brandon Abranovic, Sulagna Sarkar, Jack Lee Beuth
On-Site Speaker (Planned) Brandon Abranovic
Abstract Scope This work focuses on deep learning enabled process monitoring for large-scale laser hot wire additive manufacturing using video data, which was collected using camera mounted on the robot arm pointed at the melt pool. Initial work consists of the unsupervised training of a convolutional long short term memory autoencoder to reconstruct footage from anomaly free single beads. The trained architecture was used to reconstruct footage where anomalies including arcing and wire stubbing occurred. Wire stubbing is a condition where un-melted wire impacts the solid bottom of the melt pool, leading to jittering of the wire. The model’s ability to faithfully reconstruct the video was quantified by computing a regularity score between the raw frames and model outputs, with low regularity scores being indicative of an anomaly. Preliminary results have demonstrated the model’s robustness in detecting the anomaly classes of interest to this study.
Proceedings Inclusion? Undecided


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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