About this Symposium |
| Meeting |
MS&T27: Materials Science & Technology
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| Symposium
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AIST and TMS Joint Session on Integrated Computational Materials Engineering of Advanced Ferrous Alloys and Processing
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| Sponsorship |
AIST Metallurgy—Processing, Products and Applications Technology Committee
TMS: Integrated Computational Materials Engineering Committee
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| Organizer(s) |
Thomas S. Avey, Naval Surface Warfare Center Carderock
Daniel S. Baker, LIFT
Krista R. Limmer, DEVCOM Army Research Laboratory
Patrick J. Cleaver, Cleveland-Cliffs Steel Corporation |
| Scope |
The ferrous alloy industry has achieved considerable success by applying Integrated Computational Materials Engineering (ICME) for structure-processing-property modeling, impurity control, and novel alloy design. Today, the scope of the industry is rapidly expanding from microstructurally complex structural steels to advanced functional materials, including shape memory and biomedical alloys. Concurrently, the market is demanding improved energy efficiency and greater flexibility in supply chain and processing routes. To meet these evolving needs, the ICME field must also advance, accelerating the development of novel materials while incorporating these complex industrial requirements. This joint AIST-TMS session will showcase the current state-of-the-art in ICME-based modeling for ferrous alloys and processing, share practical lessons from industrial success stories, and explore the cutting-edge research needed to build and implement the next generation of ICME tools.
Abstracts of interest to this joint AIST-TMS session are encouraged but not required to communicate the application of the following to the ferrous alloy industry:
• Applied ICME tools at production scale
• Complex frameworks that combine multiple tools or multiple length scales
• First-principal calculations: DFT, MD, etc.
• Thermodynamic and kinetic modeling
• Process-Structure-Property predictions
• New tool development and validation
• Recent advances in AI/ML tools
• In-situ monitoring and digital twins
• Larger-than-lab scale implementation |
| Abstracts Due |
05/01/2027 |