Exploring interpretable features of hardness for intermetallic compounds prepared by spark plasma sintering

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@Article{LI:2023:ijrmhm,

• author = "Xiangyue Li and Dexin Zhu and Kunming Pan and Hong-Hui Wu and Yongpeng Ren and Can Hu and Shuaikai Zhao",
• title = "Exploring interpretable features of hardness for intermetallic compounds prepared by spark plasma sintering",
• journal = "International Journal of Refractory Metals and Hard Materials",
• volume = "117",
• pages = "106386",
• year = "2023",
• ISSN = "0263-4368",
• DOI = "doi:10.1016/j.ijrmhm.2023.106386",
• URL = "https://www.sciencedirect.com/science/article/pii/S026343682300286X",
• keywords = "genetic algorithms, genetic programming, Intermetallic compounds, Vickers hardness, Machine learning, Symbolic regression, XAI",
• abstract = "Intermetallic compounds, known for their excellent hardness, conductivity, and strength, have significant applications in aerospace and automotive industries. Hardness is a crucial mechanical property in the development and optimization of intermetallic compounds (IMCs), and meanwhile, spark plasma sintering (SPS) serves as a prevalent technique for preparing IMCs. In this study, a dataset of Vickers hardness of binary intermetallic compounds prepared by SPS and potential feature sets influencing the target performance (HV) were collected. Three machine-learning strategies were developed and comprehensively evaluated. The first strategy focuses on processing parameters and compositions, the second incorporates physical properties in addition to the features considered in the first strategy, and the third one employs a combined feature engineering based on the second strategy. The third strategy, which includes three screened features through a rigorous feature engineering process, achieves the highest predictive accuracy. Subsequently, a symbolic regression (SR) model based on genetic programming (GP) was employed to develop a physically interpretable formula linking the target performance with the selected features. The findings of this study are of significance for developing high-performance intermetallic compounds",

}

Genetic Programming entries for Xiangyue Li Dexin Zhu Kunming Pan Hong-Hui Wu Yongpeng Ren Can Hu Shuaikai Zhao

Citations