Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

Abstract The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s−1. Strain-compensated Arrhenius (SCA) and back-propagation artificial neural network (BPANN) methods were selected to model the constitutive relationship, and the models were further evaluated by statistical analysis and cross-validation. The stress−strain data extended by two models were implanted into finite element to simulate hot compression test. The results indicate that the flow stress is sensitive to deformation temperature and strain rate, and increases with increasing strain rate and decreasing temperature. Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors, but the fitting accuracy of BPANN is higher than that of SCA. Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy. Both models are effective and feasible in simulation, but BPANN model is superior in accuracy.