Birdshot Center

Batch-wise Improvement in Reduced Materials Design Space using a Holistic Optimization Technique

Conference (TMS 2024)

Rapidly Assessing Strain Rate Sensitivity of Titanium Alloys via Nanoindentation

2024

T. Hastings, J. Paramore, B. Butler, R. Arroyave, D. Khatamsaz, and D. Allaire, “Interoperable Batch Bayesian Optimization Techniques for Efficient Property Discovery of Metals,” in TMS Annual Meeting and Exhibition, Orlando, FL, USA, 2024.

1 Bush Combat Development Complex, Texas A&M University System
2 Department of Materials Science and Engineering, Texas A&M University
3 DEVCOM Army Research Laboratory

Abstract:
Many next-generation applications of titanium alloys in the defense, transportation, and energy sectors require reliable mechanical performance at elevated strain rates. However, reliably assessing mechanical properties over a wide range of strain rates can be cumbersome using bulk methods (e.g., Kolsky/split-Hopkinson pressure bar testing), thereby slowing innovation in both alloy and processing design. In this study, several nanoindentation techniques for assessing strain rate sensitivity (SRS) were investigated and compared with respect to reliability, rapidity, and quality of data produced. The material of interest was Ti-6Al-4V produced via advanced powder metallurgy methods, which has been previously shown to exhibit promising high strain rate properties during bulk testing. Correlations between the strain rate sensitivity measured via the various nanoindentation methods and bulk testing will be presented. Furthermore, the potential for microscale mechanical characterization methods to accelerate discovery of new titanium alloys and processing routes will be discussed.


Rapidly Assessing Strain Rate Sensitivity of Titanium Alloys via Nanoindentation

Accelerating Materials Discovery of HEA’s through Constraint Based High Throughput Design, Synthesis and Batch Bayesian Optimization Framework

Mrinalini Mulukutla1; Raymundo Arroyave1; Danial Khatamsaz1; James Paramore1; Brady Butler1; Trevor Hastings1; Daniel Lewis1; Daniel Salas1; Nicole Person1; Wenle Xu1; Douglas Allaire1; George Pharr1; Ibrahim Karaman1; 1Texas A&M University

2024

Abstract:
High entropy alloys have been of great interest to materials research community for the development of advanced materials with exceptional properties. Efficient and accelerated exploration of these vast compositional spaces has been an ongoing challenge with conventional high throughput experimentation/computational methods. We address this challenge by implementation of framework that employs a composition agnostic, multi-objective, multi-constraint co-design for performance, and manufacturability. Using 6 element phase space (Co, Cr, Fe, Ni, V and Al), we defined the space through intelligent constraint-based filtering, produced candidate alloys by vacuum arc melting followed by characterization for objectives relevant to structural materials for extreme conditions. They are iterated in a closed loop by Batch Bayesian Optimization to identify pareto set for the subsequent iterations. Optimal exploration involving five successful iterations showcases the superiority of framework’s powerful machine learning algorithms suggesting scope for higher fidelity systems in future works.