| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Process-Structure-Property Relationships from Variational Autoencoders |
| Author(s) |
Michael White, N.H. Gowtham, Christopher Race, Philip Withers, Bikramjit Basu |
| On-Site Speaker (Planned) |
Michael White |
| Abstract Scope |
Microstructure provides the link between processing and properties. A key aim of metallurgy is to understand how microstructural features influence properties and how processing determines microstructure. Although microstructure is well understood in this context, we often use qualitative descriptions to make comparisons between materials. A variety of metrics can be applied to form a quantitative description, but the metrics that are suitable depend on the material and any processing it has undergone – there is no catch-all method for describing microstructure quantitively. Since the rise of machine learning in materials informatics, many new approaches to constructing quantitative descriptions of image data have become available.
Here, we explore the use of variational autoencoders (VAEs) for constructing encoded representations of microstructure from image data for titanium alloys. We then use these encoded representations as inputs to a variety of tasks, including prediction of processing parameters and mechanical properties, highlighting process-structure-property relationships. |