Abstract
The nonlinear flow behaviors of BT22 alloy were investigated by thermal simulation experiments at different temperature and strain rates. Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy. Genetic algorithm (GA) was used to optimize the parameters of the SVR model and establish the GA-SVR model. At the same time, the physical model optimized by GA algorithm is compared with the machine learning model. Average absolute relative error (AARE), absolute relative error (ARE), and correlation coefficient (R) were used to evaluate the predictive ability of the four models. The results show that the order of model accuracy and generalization ability is GA-SVR > BPANN > SVR > physical model. The AARE value of the GA-SVR model is 1.5752%, and the R value is as high as 0.9984, which can accurately predict the flow behaviors of BT22 alloy. According to the GA-SVR model, the flow behaviors under other conditions could be predicted to expand the experimental stress-strain data and avoid a large number of artificial tests.
Highlights
BT22 alloy is a kind of near beta titanium alloy
Chen et al had investigated the high temperature flow behaviors of Ti-6Al-3Nb2Zr-1Mo titanium alloy by establishing the back propagation artificial neural network (BPANN) and Arrhenius model, which showed that the accuracy of regression model was lower than BPANN [13]
Rough the evaluation of the model, the following conclusions are drawn: (1) In the aspect of model fitting ability, the Average absolute relative error (AARE) value of the Genetic algorithm (GA)-support vector regression (SVR) model was lower than 1%, which was 0.8388%
Summary
BT22 alloy is a kind of near beta titanium alloy. It has many advantages, such as high strength, high plasticity, good hardenability, and weldability, which is widely used in aircraft and other large structural load-bearing parts [1,2,3,4,5]. Empirical/semiempirical model, analytical model, phenomenological model, and physical model were four typical constitutive models of metal deformation at high temperature [9,10,11,12], among which Arrhenius model was the most widely used These models are inferior to machine learning models in prediction accuracy and modeling [7, 11, 13,14,15,16,17]. Quan et al established back propagation artificial neural network (BPANN) model and improved Arrhenius model, respectively, to study flow stress behaviors of Ti-6Al-2Zr-1Mo-1V titanium alloy. Chen et al had investigated the high temperature flow behaviors of Ti-6Al-3Nb2Zr-1Mo titanium alloy by establishing the BPANN and Arrhenius model, which showed that the accuracy of regression model was lower than BPANN [13]. E SVR model optimized by the genetic algorithm (GA-SVR) model, ANN model, and improved Arrhenius constitutive model had been established to study the flow behaviors of Ti-6Al-2Zr-1Mo-1V alloy.
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