BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221020T223000
DTEND;TZID=America/New_York:20221020T233000
DTSTAMP:20260405T141215
CREATED:20221006T171538Z
LAST-MODIFIED:20221006T171538Z
UID:7639-1666305000-1666308600@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: “What Governs Grain Boundary Migration?"
DESCRIPTION:Curvature is the common driving force for grain boundary motion in all polycrystals. However\, models and simulations derived from curvature-based motion cannot predict irregular\, albeit commonly observed\, grain growth behavior. To build better predictive models\, we need to employ new tools to understand what governs grain growth. First\, I will demonstrate how high energy x-ray diffraction microscopy (HEDM) can be used to observe grain growth in real 3D polycrystalline systems. In a grain growth study employing HEDM of strontium titanate\, we find that curvature is a poor predictor of grain boundary migration. Instead\, anisotropic grain boundary properties are hypothesized to override the contribution of curvature. Second\, I will describe how reinforcement learning\, a machine learning tool\, can capture the underlying behavior of an evolving Markov decision process and “teach” it to maximize the “rewards” regarding the agreement between prediction and simulation. To demonstrate the feasibility of this approach\, we built a deep reinforcement model that emulates grain growth by training on Monte Carlo Potts grain growth simulations. The developed reinforcement model was validated on different microstructural architectures to ensure that it captures the underlying physics. The accuracy of our short and long-term predictions will be evaluated. Then\, I will discuss how HEDM and our machine learning model can be combined to understand how anisotropic grain boundaries migrate in 3D polycrystals.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-what-governs-grain-boundary-migration/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
END:VCALENDAR