Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, the ability to model the evolution of microstructures from changes in processing condition or even predict the microstructures from given processing condition would greatly reduce the time frame and the cost of the optimization process. In 1, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. We also demonstrate the capability of predicting new microstructures from the representation with deep learning and we can explore the physical insights of the implicitly expressed microstructure representations. We validate our framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures. In 2, we further improve and generalize the generating framework, a set of metrics is used to quantitatively analyze the effectiveness of the generation by comparing the microstructure characteristics between the generated samples and the originals. We also take advantage of image processing techniques to aid the calculation of metrics that require grain segmentation. / Master of Science / Different from the traditional manufacturing technique which removes material to form the desired shape, additive manufacturing (AM) adds material together to form the shapes usually layer by layer. AM which is sometimes also referred to as 3-D printing enables the optimization of material property through changing the processing conditions. The microstructure is structures formed by materials on a microscopic scale. Crystals like metal usually form a crystalline structure composed of grains where atoms have the same orientation. Especially for metal AM, changes in the processing condition will usually result in changes in microstructures and material properties. To better optimize for the desired material properties, in 1 we present a microstructure representation method that allows projection of microstructure onto the representation space and prediction from an arbitrary point from the representation space. This representation method allows us to better analyze the changes in microstructure in relation to the changes in processing conditions. In 2, we validate the representation and prediction using EBSD data collected from copper samples manufactured with AM under different processing conditions.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/103392 |
| Date | 19 May 2021 |
| Creators | Han, Yi |
| Contributors | Electrical and Computer Engineering, Zhu, Yunhui, Leonessa, Alexander, Yu, Hang |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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