| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Presentation Title |
Variational Autoencoders for Comprehensive Feature Identification in Fatigue Analysis
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| Author(s) |
William J. Frieden Templeton, Tharun Kondareddy, Justin Miner, Sneha Prabha Narra |
| On-Site Speaker (Planned) |
William J. Frieden Templeton |
| Abstract Scope |
Fatigue life is a key performance metric for metal AM parts, making it an active research area generating vast datasets of micrographs from fatigue test specimens. Using these image-based datasets, this study pursues data analytics to find correlations between fatigue life and porosity defects. To do so, the information in the images must be quantified for statistical analysis, thereby introducing a source of information loss. This work aims to preserve information by applying variational autoencoders (VAEs) to the image dataset. By encoding the micrographs to a low-dimensional latent space, valuable information in the dataset is captured and similar features are clustered. Encodings that correlate with poor fatigue life are identified using random forest models, and examples of the features they link to are generated. The results will demonstrate the potential application of VAEs to quantify micrographs and discern useful correlations between defects and fatigue life. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |