Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning

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@Article{CHEN:2023:jmst,

• author = "Yimian Chen and Shuize Wang and Jie Xiong and Guilin Wu and Junheng Gao and Yuan Wu and Guoqiang Ma and Hong-Hui Wu and Xinping Mao",
• title = "Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning",
• journal = "Journal of Materials Science \& Technology",
• volume = "132",
• pages = "213--222",
• year = "2023",
• ISSN = "1005-0302",
• DOI = "doi:10.1016/j.jmst.2022.05.051",
• URL = "https://www.sciencedirect.com/science/article/pii/S100503022200545X",
• keywords = "genetic algorithms, genetic programming, Machine learning, Symbolic regression, Low-alloy steel, Charpy impact toughness",
• abstract = "High toughness is highly desired for low-alloy steel in engineering structure applications, wherein Charpy impact toughness (CIT) is a critical factor determining the toughness performance. In the current work, CIT data of low-alloy steel were collected, and then CIT prediction models based on machine learning (ML) algorithms were established. Three feature construction strategies were proposed. One is solely based on alloy composition, another is based on alloy composition and heat treatment parameters, and the last one is based on alloy composition, heat treatment parameters, and physical features. A series of ML methods were used to effectively select models and material descriptors from a large number of alternatives. Compared with the strategy solely based on the alloy composition, the strategy based on alloy composition, heat treatment parameters together with physical features perform much better. Finally, a genetic programming (GP) based symbolic regression (SR) approach was developed to establish a physical meaningful formula between the selected features and targeted CIT data",

}

Genetic Programming entries for Yimian Chen Shuize Wang Jie Xiong Guilin Wu Junheng Gao Yuan Wu Guoqiang Ma Hong-Hui Wu Xinping Mao

Citations