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

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.