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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis
Author(s) Tiberiu Stan, Peter Voorhees
On-Site Speaker (Planned) Tiberiu Stan
Abstract Scope One of the advantages of modern materials processing techniques (such as additive manufacturing) is the ability to correct for defects during part fabrication. To be successful, the images obtained through in-situ monitoring must be rapidly collected and analyzed. We showcase the use of convolutional neural networks (CNNs) as an efficient way to accurately evaluate large materials imaging datasets. Novel approaches to CNN training using experimental and synthetic datasets will be presented, as well as techniques for comparing and determining the success of different machine learning methods. Future steps to incorporating artificial intelligence into process optimization and anomaly detection will also be discussed.


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