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- W4313201740 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." @default.
- W4313201740 created "2023-01-06" @default.
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- W4313201740 date "2023-02-01" @default.
- W4313201740 modified "2023-10-12" @default.
- W4313201740 title "Establishing a data-driven strength model for <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si1.svg display=inline id=d1e613><mml:mi>β</mml:mi></mml:math>-tin by performing symbolic regression using genetic programming" @default.
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- W4313201740 doi "https://doi.org/10.1016/j.commatsci.2022.111967" @default.
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