Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning. (1st January 2023)

• Record Type: Journal Article
• Authors: Chen, Yimian; Wang, Shuize; Xiong, Jie; Wu, Guilin; Gao, Junheng; Wu, Yuan; Ma, Guoqiang; Wu, Hong-Hui; Mao, Xinping
• Is Part Of: Journal of materials science & technology. Volume 132(2023)
• Page Start: 213
• Page End: 222
• Publication Date: 2023-01-01
• Subjects: Machine learning -- Symbolic regression -- Low-alloy steel -- Charpy impact toughness; Metals -- Periodicals; Materials science -- Periodicals; Materials science; Metals; Periodicals; 620.1105
• DOI: 10.1016/j.jmst.2022.05.051
• Languages: English
• ISSNs: 1005-0302

Abstract

Highlights: Facile descriptors of CIT in low-alloy steels were identified via a ML method. The predicted values of models have a high consistency with the experimental values. The mathematical expression of CIT is optimized by symbolic regression. The ML model can guide the optimal design of low-alloy steel with desired properties. 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. Graphical abstract: Image, graphical abstract