The thesis explores the development, applications and future perspectives on the Genova High-Entropy Alloys (GHEA) thermodynamic database. In particular, the different activities which are typically carried out when building extensive thermodynamic databases are reported, ranging from critical assessment of phase diagram data to thermodynamic parameters optimization, database validation and application. The main focus regards the Al-C-Co-Cr-Fe-Ni-R (R= Mo, Ta, W) systems, described in the whole process, starting from their implementation in the database. Practical examples of GHEA application on these systems are given, ranging from alloy design to interpretation of experimental results. The implementation of Si in the database is also shown, as part of a preliminary work which will be continued in the future. In the final part, the future perspectives on the database, and more in general on the CALPHAD method, are explored, with particular attention to AI- and ML-based techniques and 3rd generation CALPHAD modelling.
The Genova High-Entropy Alloys (GHEA) Thermodynamic Database: Development, Application and Future perspectives
FENOCCHIO, LORENZO
2025
Abstract
The thesis explores the development, applications and future perspectives on the Genova High-Entropy Alloys (GHEA) thermodynamic database. In particular, the different activities which are typically carried out when building extensive thermodynamic databases are reported, ranging from critical assessment of phase diagram data to thermodynamic parameters optimization, database validation and application. The main focus regards the Al-C-Co-Cr-Fe-Ni-R (R= Mo, Ta, W) systems, described in the whole process, starting from their implementation in the database. Practical examples of GHEA application on these systems are given, ranging from alloy design to interpretation of experimental results. The implementation of Si in the database is also shown, as part of a preliminary work which will be continued in the future. In the final part, the future perspectives on the database, and more in general on the CALPHAD method, are explored, with particular attention to AI- and ML-based techniques and 3rd generation CALPHAD modelling.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/199678
URN:NBN:IT:UNIGE-199678