About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Symposium
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2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
|
Presentation Title |
Convolution Long-short Term Memory Autoencoders Applied to Process Monitoring Across Multiple Camera Types for Laser Hot Wire Additive Manufacturing |
Author(s) |
Brandon Abranovic, Elizabeth Chang-Davidson, Sulagna Sarkar, Jack Beuth |
On-Site Speaker (Planned) |
Brandon Abranovic |
Abstract Scope |
In order to reliably print quality parts using additive manufacturing, process monitoring is a crucial step in the build process. During the deposition period, large quantities of data are collected and must be analyzed in order to detect and eliminate process anomalies. Unsupervised deep learning techniques are valuable in executing this analysis due to their ability to recognize flaws without the need for vast quantities of labeled data. A convolutional long-short term memory autoencoder model was trained on process data from a laser hot wire additive manufacturing process. This model used, as input, data from both a visible-light camera and an infrared camera, to encompass melt pool disturbances as well as near-melt pool part temperatures. This model is shown to be feasible as a realtime monitoring technique capable of detecting known characteristic process flaws, as well as a post-deposition data analysis tool for directing part testing towards suspected flaw areas. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |