Machine learning-assisted design of high-entropy alloys for optimal strength and ductility

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@Article{Singh:2024:jallcom,

• author = "Shailesh Kumar Singh and Bashista Kumar Mahanta and Pankaj Rawat and Sanjeev Kumar",
• title = "Machine learning-assisted design of high-entropy alloys for optimal strength and ductility",
• journal = "Journal of Alloys and Compounds",
• year = "2024",
• volume = "1007",
• pages = "176282",
• keywords = "genetic algorithms, genetic programming, High entropy alloys, Deep neural network, ANN, Microstructural characterization",
• ISSN = "0925-8388",
• URL = "https://www.sciencedirect.com/science/article/pii/S092583882402869X",
• DOI = "doi:10.1016/j.jallcom.2024.176282",
• abstract = "High Entropy Alloys (HEAs) are a novel class of multi-component alloys with compositional flexibility, presenting a promising alternative to traditional alloys. This research aims to enhance the strength of HEAs while achieving adequate ductility, a challenging objective due to their conflicting nature. We applied evolutionary data-driven models and Bi-Objective Genetic Programming (BioGP) with genetic algorithms (GA) to accurately predict and optimise yield strength and ductility. The predictions were validated through experimental methods, including casting by vacuum arc melting and comprehensive mechanical and microstructural characterisation. Our integrated approach successfully developed an HEA exhibiting a strength of 1795 plus-minus 21MPa and a ductility of 31.45percent. This study highlights the effectiveness of combining data-driven models with experimental validation to advance the development of high-performance materials",

}

Genetic Programming entries for Shailesh Kumar Singh Bashista Kumar Mahanta Pankaj Rawat Sanjeev Kumar

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