Numerical descriptions of hot flow behaviors across β transus for as-forged Ti-10V-2Fe-3Al Alloy by LHS-SVR and GA-SVR and improvement in forming simulation accuracy

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.