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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Presentation Title Deep Neural Network Regressor for Phase Fraction Estimation on the High Entropy Alloy System Al-Co-Cr-Fe-Mn-Nb-Ni
Author(s) Guillermo Vazquez Tovar, Sourav Chakravarty, Rebeca Gurrola, Raymundo Arroyave
On-Site Speaker (Planned) Guillermo Vazquez Tovar
Abstract Scope High Entropy Alloys (HEAs) are composed of more than one principal element and constitute a major paradigm in metals research. A thorough estimation of the phases that form in HEAs given different elemental input is of paramount importance in designing HEAs. Machine Learning presents a feasible and non-expensive method of predicting HEA phase fractions. A Deep Neural Network (DNN) model is developed for the system Al-Co-Cr-Fe-Mn-Nb-Ni, using a dataset of nearly a million points generated via Thermo-Calc. A list of features was then compiled, heavily influenced by previous works and freely available databases. A feature significance analysis expands the current knowledge on phase-elemental properties relations. The final regressor model shows high performance with coefficient of determination values above 0.98 when identifying the most recurrent phases: BCC, FCC, LAVES, and SIGMA. The DNN is then used as a faster and easier to implement surrogate model for optimization problems.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, Machine Learning, Other


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