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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Rapid Microstructural Determination from Nano-indentation of High Entropy Alloys Using Machine Learning and Genetic Algorithms
Author(s)	Amit Arora, Abhijeet Dhal, Rajiv S. Mishra
On-Site Speaker (Planned)	Amit Arora
Abstract Scope	Determining microstructural features such as phase, orientation, grain size, and orientation spread in industrial settings requires advanced microscopy and can take days. For high entropy alloys (HEAs) that exhibit hierarchical and heterogenous microstructures with deformation-induced transformation, the local hardness values depend on the microstructure. A combined machine learning (ML) and genetic algorithm (GA) based model can be useful for identifying the microstructural features from high-throughput nano-indentation mapping of such alloys. In this work, we present a ML based model to correlate the microstructural features with the nano-indentation hardness maps. A GA model was developed to reverse-engineer the microstructural features from the nano-indentation maps of the alloys. The GA model was trained on extensive datasets, enabling it to accurately predict microstructural characteristics based on hardness distributions. This approach facilitates rapid determination of microstructure in industrial alloy development using high-throughput nano-indentation mapping, offering a significant advantage in optimizing material properties efficiently.
Proceedings Inclusion?	Planned:
Keywords	High-Entropy Alloys, Machine Learning, ICME

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