Artificial intelligence model of complicated flow behaviors for Ti−13Nb−13Zr alloy and relevant applications

Abstract The comprehensive nonlinear flow behaviors of a ductile alloy play a significant role in the numerical analysis of its forming process. The accurate characterization of as-forged Ti−13Nb−13Zr alloy was conducted by an improved intelligent algorithm, GA−SVR, the combination of genetic algorithm (GA) and support vector regression (SVR). The GA−SVR model learns from a training dataset and then is verified by a test dataset. As for the generalization ability of the solved GA−SVR model, no matter in β phase temperature range or (α+β) phase temperature range, the correlation coefficient R-values are always larger than 0.9999, and the AARE-values are always lower than 0.18%. The solved GA−SVR model accurately tracks the highly-nonlinear flow behaviors of Ti−13Nb−13Zr alloy. The stress−strain data expanded by this model are input into finite element solver, and the computation accuracy is improved.