摘要
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-13 Nb-13 Zr 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-13 Nb-13 Zr alloy. The stress-strain data expanded by this model are input into finite element solver, and the computation accuracy is improved.
韧塑性合金的复杂非线性流变行为是成形数值模拟的关键因素。结合遗传算法(GA)和支持向量回归(SVR),即GA-SVR,准确表征Ti-13Nb-13Zr锻态合金的高度非线性流变行为。GA-SVR模型对训练数据组进行学习,并由检验数据组进行验证。对GA-SVR模型的泛化能力进行评价,无论合金处于β相还是(α+β)相,相关系数R值均>0.9999,平均绝对相对误差(AARE)则始终<0.18%。求解的GA-SVR模型可以精确描述该合金的高度非线性行为。该模型进而用来扩展合金的应力-应变数据,这些扩展后的数据被输入到有限元模型中以提升数值模拟的精度。
基金
Project(cstc2018jcyjAX0459) supported by Chongqing Basic Research and Frontier Exploration Program,China
Projects(2019CDQYTM027,2019CDJGFCL003,2018CDPTCG0001-6,2019CDXYCL0031) supported by the Fundamental Research Funds for the Central Universities,China
