Establishing a data-driven strength model for beta-tin by performing symbolic regression using genetic programming

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@Article{MONTESDEOCAZAPIAIN:2023:commatsci,

• author = "David {Montes de Oca Zapiain} and J. Matthew D. Lane and Jay D. Carroll and Zachary Casias and Corbett C. Battaile and Saryu Fensin and Hojun Lim",
• title = "Establishing a data-driven strength model for beta-tin by performing symbolic regression using genetic programming",
• journal = "Computational Materials Science",
• volume = "218",
• pages = "111967",
• year = "2023",
• ISSN = "0927-0256",
• DOI = "doi:10.1016/j.commatsci.2022.111967",
• URL = "https://www.sciencedirect.com/science/article/pii/S0927025622006784",
• keywords = "genetic algorithms, genetic programming, Tin, Strength, Symbolic regression",
• abstract = "Tin (Sn) exhibits complex deformation behavior characterized by significant dependence of strength on temperature and strain rate. This work develops a strength model for tin by using genetic programming to perform symbolic regression on a set of compression tests at various strain rates and temperatures. The strength model developed in this work showed increased accuracy compared to traditional strength models. Furthermore, the developed strength model adequately predicted independent experimental data (i.e., data that was not used to train the model). Results demonstrate that genetic programming successfully established a valid analytical function that adequately characterizes the temperature and strain rate dependent strength behavior of tin. Therefore, demonstrating that the developed framework provides robust and accurate formulations of strength models",

}

Genetic Programming entries for David Montes de Oca Zapiain J Matthew D Lane Jay D Carroll Zachary Casias Corbett C Battaile Saryu Fensin Hojun Lim

Citations