Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning
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- @Article{CHEN:2023:jmst,
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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",
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title = "Identifying facile material descriptors for Charpy
impact toughness in low-alloy steel via machine
learning",
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journal = "Journal of Materials Science \& Technology",
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volume = "132",
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pages = "213--222",
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year = "2023",
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ISSN = "1005-0302",
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DOI = "doi:10.1016/j.jmst.2022.05.051",
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URL = "https://www.sciencedirect.com/science/article/pii/S100503022200545X",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Symbolic regression, Low-alloy steel, Charpy
impact toughness",
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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