Abstract
Hot compression tests of as-forged Ti–10V–2Fe–3Al alloy in a wide temperature range of 948–1123 K and a strain rate range of 0.001–10 s−1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively model the non-linear flow behaviors, support vector regression (SVR), as a machine learning method, was combined with Latin hypercube sampling (LHS) and genetic algorithm (GA) to respectively characterize the flow behaviors, namely LHS-SVR and GA-SVR. The significant characters of LHS-SVR and GA-SVR are that they, with identical training parameters, can maintain training accuracy and prediction accuracy at stable levels in different attempts. The study abilities, generalization abilities and modelling efficiencies of the mathematical regression model, artificial neural network (ANN), LHS-SVR and GA-SVR were compared in detail by using standard statistical parameters. After comparisons, the study abilities and generalization abilities of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR < LHS-SVR. The modeling efficiencies of these models were shown as follows in ascending order: mathematical regression model < ANN < LHS-SVR < GA-SVR. The flow behaviors outside experimental conditions were predicted by the well-trained LHS-SVR, which improves the simulation precision of the load-stroke curve.
Highlights
Ti–10V–2Fe–3Al alloy, a typical near-β titanium alloy, has the advantages of high strength, good toughness, excellent stress-corrosion resistance, etc., so it was widely utilized for key structural parts in the aerospace industry
It is universally acknowledged that stress-strain data play important roles in many areas, for instances speculating work hardening (WH) and dynamic recovery (DRV) [1], characterizing dynamic recrystallization evolution [2], improving processing maps [3], etc
C, γ and ζ, and this study indicates that the model is more accurate than artificial neural network (ANN) and the constitutive equation; besides, the sample dependence of the support vector regression (SVR) is lower [22]
Summary
Ti–10V–2Fe–3Al alloy, a typical near-β titanium alloy, has the advantages of high strength, good toughness, excellent stress-corrosion resistance, etc., so it was widely utilized for key structural parts in the aerospace industry. The mathematical regression equations of the phenomenological model cannot accurately track the highly non-linear flow behaviors at different strain rates and temperatures [15,17]. Because they are mathematically fitted based on limited experimental data. Compared to ANN and the method that manually adjusts the three parameters one by one to obtain an accurate prediction model, the intelligence algorithm LHS-SVR can automatically calculate the parameter combinations one by one in the search space to find the optimal value, which improves the computational efficiency to a certain extent. The stresses outside experimental conditions were predicted by the well-trained LHS-SVR, which enhances the simulation precision of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles
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