| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
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| Presentation Title |
Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys |
| Author(s) |
Franck Tancret, Edern Menou, Gérard Ramstein |
| On-Site Speaker (Planned) |
Franck Tancret |
| Abstract Scope |
Our use of artificial intelligence (AI) to design high-temperature alloys (HTA) started more than twenty years ago with the modelling of mechanical properties of nickel-based superalloys as a function of composition, using data mining tools like artificial neural networks and Gaussian processes (GP). Such machine learning (ML) models were then associated to the calculation of phase diagrams (Calphad / Thermo-Calc) to design an affordable wrought superalloy for power plant applications, and later a set of single-crystal superalloys for aeroengines. Other AI tools, like genetic algorithms (GA), including their multi-objective optimisation (MOO) version, were then coupled to both ML and Calphad to propose the most advanced integrated computational alloy design scheme at its time, along with the successful redesign of a proprietary superalloy for turbine disks. Current works are ongoing, both on algorithm development and on the design of HTAs like so-called high entropy alloys (HEA) or complex concentrated alloys (CCA). |
| Proceedings Inclusion? |
Planned: |