Conference Tools for 2023 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2023 TMS Annual Meeting & Exhibition
Symposium	Algorithm Development in Materials Science and Engineering
Presentation Title	Thermographic Process Classification in Electron Beam Additive Manufacturing via Stacked Long Short-Term Memory Networks
Author(s)	Benjamin Stump, Alex Plotkowski, Vincent Paquit
On-Site Speaker (Planned)	Benjamin Stump
Abstract Scope	Additive manufacturing (AM) provides opportunities to produce complex geometries and high-performance materials with an unprecedented amount of control. AM simulations must either choose accuracy or performance; therefore, collecting and analyzing in situ process data offers a tractable way to correlate the process to the results. Previous work successfully correlated noisy, low framerate data to the process classification for a single layer; however, this approach broke down when applying it to other layers potentially due to overfitting the solutions. This work utilized a machine learning approach known as long-short term memory networks (LSTMs) to the same problem. LSTMs, which are known for their ability to deal with time series data, achieved superior results with no parameter turning with the results transferring well to layers it was not trained on. Finally, stacked LSTMs, a technique used in natural language processing, achieve the best results with a lowed bound classification accuracy of 96%.
Proceedings Inclusion?	Planned:
Keywords	Additive Manufacturing, Machine Learning, Solidification

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