About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
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Symposium
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Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
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Presentation Title |
Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images |
Author(s) |
Richard Andrew Couperthwaite, Levi McClenny, Jaylen James, Vahid Attari, Raymundo Arróyave, Ulisses Braga Neto |
On-Site Speaker (Planned) |
Richard Andrew Couperthwaite |
Abstract Scope |
Microstructural images contain a wealth of information that can be used to determine both heat treatment conditions and mechanical properties. While standard methods for characterizing microstructures, such as grain size, and volume fraction of phases are useful, convolutional neural networks are capable of featurizing images in more complex ways. This ability can be utilized to generate feature information that can be used to fit regression models. A further step is that it is possible to terminate convolutional neural networks in a single node that is capable of providing a regression result directly from the neural network. Utilizing an image set of 10000 phase field microstructures, the current work considers the effect of different structures and secondary regression methods on the effectiveness of predicting various parameters used in the production and analysis of the microstructures. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |