| About this Abstract |
| Meeting |
3rd World Congress on High Entropy Alloys (HEA 2023)
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| Symposium
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HEA 2023
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| Presentation Title |
Inferrable Bayesian Models for Process-structure-property Linkages in Complex Concentrated Alloys |
| Author(s) |
George Thoppil, Alankar Alankar, Jian-Feng Nie |
| On-Site Speaker (Planned) |
George Thoppil |
| Abstract Scope |
In this work we attempt to extract the effect of thermo--mechanical processing on the microstructure--mechanical property linkages of complex concentrated alloys (CCAs) by training machine learning (ML) models using processing parameters as features. The effect of processing on the phase morphology and the mechanical properties is studied. The stacking fault energy (SFE) predicted based on CCA composition is used as a benchmark to identify deformation mechanisms. This work presents a novel method that attempts to establish a framework using an assortment of Bayesian--learning models to generate process--structure--property (PSP) linkages that captures the evolution of phases, their volume fractions and grain sizes and the corresponding change in mechanical properties of a diverse set of CCA compositions as they undergo various processing conditions. The evolution of the mechanical property with grain size is captured as Hall--Petch relations as an example of possible PSP linkage representations.
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| Proceedings Inclusion? |
Planned: Metallurgical and Materials Transactions |