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Hassel and Marianne Ledbetter MatSE Colloquium - “Accelerated design and understanding of high temperature structural alloys”

Event Type
Seminar/Symposium
Sponsor
Materials Science and Engineering Department
Location
100 Materials Science and Engineering Building, 1304 W. Green Street, Urbana
Date
Dec 5, 2022   4:00 pm  
Speaker
Michael Titus, Materials Engineering, Purdue University
Views
19
Originating Calendar
MatSE Colloquium Calendar

“Accelerated design and understanding of high temperature structural alloys”

For decades, high temperature structural alloys have been dominated by γ’-Ni3Al or γ’’-Ni3Nb-strengthened Ni-based superalloys such as Ni alloy 718, Mar-M-247, and advanced single crystal alloys. This is despite the fact that there exists both thousands of unique intermetallic compounds and a vast composition space that has yet to be explored. New computational and experimental tools that aid in advancing the fundamental understanding and prediction of materials properties combined with new alloy design paradigms afforded by the “high entropy alloy” concept now enable dramatic advances in properties that have not been achieved for many decades.

This talk will first focus on the unique deformation mechanisms of a γ’’’-Ni2(Cr,Mo,W)-strengthened Ni-based superalloy Haynes® 244®, which exhibits microtwinning over temperatures and strain rates spanning 25 ºC to 760 ºC and 10-10 s-1 to 10-4 s-1. We hypothesize that the unique symmetry, generalized stacking fault energy surface, and 6 variants of the γ’’’ phase together facilitate microtwin formation over all testing conditions. This mechanism will be elucidated and strategies for designing new alloys with unique deformation characteristics will be presented. The second half of this talk will focus on the recent integration between thermodynamic calculations, first-principles modeling, machine learning, and experimental validation of mechanical properties and oxidation resistance in refractory complex, concentrated alloys (RCCAs). We will present a new machine learning for accelerated materials discovery (ML-AMD) framework that utilizes multi-fidelity and multi-cost experiments with physics-based modeling. New semi-high-throughput methods for characterizing hardness and oxidation resistance will be presented, and methods for implementing high-throughput simulations into the ML-AMD framework will be expounded. Promising alloys will be identified, and strategies for improving the oxidation resistance of RCCAs will be discussed.

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