As part of our commitment to Materials 4.0 we are running a series of webinars to showcase and demystify research towards Materials 4.0.
The seminar is also hosted in-person at the Royce Hub Building
The event will feature a 30-minute talk by one of our Royce Researchers followed by a 20-minute Q&A portion.
Guests are welcome to submit questions in advance of the event.
Finding efficient means of quantitatively describing material microstructure is a critical step towards harnessing data-centric machine learning approaches to understanding and predicting processing-microstructure-property relationships. Common quantitative descriptors of microstructure tend to consider only specific, narrow features such as grain size or phase fractions, but these metrics discard vast amounts of information. Since the gain in traction of machine learning and computer vision, more abstract methods for describing image data in a concise and quantitative manner have become available but have yet to be fully exploited within materials science. Here, we explore some of these methods as tools for constructing compressed representations of microstructural image data, referred to as “microstructural fingerprints”, and investigate their potential for mining correlations between fingerprints.
Mike White is a PhD student at the University of Manchester, working on machine learning-based tools for materials applications, with Chris Race and Phil Withers, in the Department of Materials.
This event will be recorded and posted online. By attending you consent to some of your actions being recorded. Video for attendees will be turned off.