About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Research Acceleration via Machine Learning for Characterization of Growing Dendritic Crystals from In Situ X-Ray Videos of Alloy Solidification |
Author(s) |
Jonathan Stewart Mullen, Mert Celikin, Pádraig Cunningham, David Browne |
On-Site Speaker (Planned) |
Jonathan Stewart Mullen |
Abstract Scope |
The observation and measurement of dendrites over time, through the use of in-situ X-Ray videos, can offer key insights into alloy solidification behaviour. However, depending on the constraints in place during data acquisition, the resulting videos can be difficult to assess due to imaging related issues, such as high noise or low contrast. Conventional image analysis and enhancement techniques alone can lead to a considerable reduction in measurement accuracy or to the need to manually assess video frames. Our approach demonstrates that, by using machine learning as a constituent part of an integrated analysis system, it is possible to obtain useful results from videos which are otherwise difficult or time-consuming to assess. This is shown through the automated assessment of individual dendrites within a thin Al-20wt%Cu alloy sample for two X-Ray videos, acquired as part of the MASER-13 microgravity sounding rocket project, and a comparison against solely conventionally obtained results. |
Proceedings Inclusion? |
Undecided |