| Organizer(s) |
Ian Zuazo, ArcelorMittal Global R&D - Industeel
Mohsen Asle Zaeem, Colorado School of Mines
Janelle P. Wharry, Purdue University
Eric Payton, University of Cincinnati
Goro Miyamoto, Tohoku University
Eric A. Lass, University of Tennessee-Knoxville
Amy J. Clarke, Los Alamos National Laboratory
MingXin Huang, University of Hong Kong
Kester D. Clarke, Los Alamos National Laboratory |
| Scope |
Martensite is a key phase in steels for diverse industrial applications, including automotive, cryogenics, pressure vessels, and fuel systems. Yet despite the large body of research on the subject, a gap persists in understanding the relationships between the competing transformation phenomena that occur during processing (auto-tempering, low temperature tempering, transition carbides, twinning, etc.), the hierarchical microstructures that are produced, and properties, including damage evolution in service.
The advent of advanced characterization techniques in recent years, in concert with modelling approaches, have provided fresh views on the austenite to martensite transformation, on martensite structure and chemical variations, and on its evolution during processing. The knowledge acquired has given new insights into the development of novel microstructure-property relationships.
It is thus timely to provide a status on these investigations in a dedicated symposium with the aim of narrowing the gap in our understanding of microstructural evolution and its impact on properties. This Symposium will focus mainly on recent developments in the study of martensite in steels.
Abstracts are of interest on (but not limited to) the following topics:
• Observations of new phenomena and microstructural evolution during quenching and tempering, service, and/or extreme environments
• Advanced characterization (development of new techniques and methods) of microstructure and/or properties, as applied to martensite in steels
• Recent advances in multi-scale modeling of microstructure development during processing, service, or exposure to external stimuli
• Microstructure-inclusive modeling of properties and performance
• High-throughput, combinatorial, or machine learning-guided alloy design and development |