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The effect of crystallographic texture and deformation rate on the deformation behavior of titanium alloy VT16 after annealing at 700 – 880°C is studied. The compressive tests are performed at deformation rates 1, 10, 100, and 1000 mm/min. The stress-strain curves are plotted. The ultimate strain is determined. The microstructure of the alloy and its texture are studied using pole figures (PF) and orientation distribution functions (ODF). It is shown that the presence of an α-phase with axial texture 10 1 ¯ 0 lowers the yield strength and the flow stress of the alloy in the hardening range and the peripheral tensile stresses of the free surface of the compressed specimens, which raises the deformability of the alloy under compression. Elevation of the temperature of the specimens with growth of the deformation rate lowers the yield stress of the alloy, which also improves the deformability of the alloy under compression. [ABSTRACT FROM AUTHOR]

The mechanical properties of titanium alloy castings are very important for their wide applications in high-end equipment and engineering; however, testing and characterization of the mechanical parameters of titanium alloy castings are complicated and costly. Therefore, the present work proposed a novel method based on a back-propagation (BP) neural network to predict the mechanical properties of a TC4 titanium alloy casting, specifically, the presence of shrinkage cavities at a given location. It was found that the statistical error between predicted values of the BP neural network and experimental results was less than 10%, indicating that the proposed model is suitable for predicting the presence of shrinkage cavities in TC4 titanium alloy castings. Moreover, the BP neural network model was also used to predict the grain size and hardness of the titanium alloy casting. The correlation between predicted and experimental results was r = 0.99485, thus indicating that the proposed model could effectively predict the grain size and hardness of TC4 titanium alloy castings. [ABSTRACT FROM AUTHOR]

• A novel approach for estimating the low cycle fatigue (LCF) life of titanium alloy structural parts was proposed. • The LCF life of titanium alloy structural parts were accurately predicted using GABP-ANN. • The GABP-ANN model improves the shortage of the BP model at randomly selected initial training parameters. • The accuracy and stability of five titanium alloy structural parts under two machine learning (ML) models are analyzed. • The GABP-ANN model has a significant advantage in predicting the fatigue life of titanium alloy structural parts. This paper proposes a novel approach for estimating titanium alloy structural parts' low cycle fatigue (LCF) life based on the continuous damage mechanics (CDM) model. Using a genetic algorithm-optimized back-propagation artificial neural network (GABP-ANN), the LCF life of titanium alloy structural parts is accurately predicted. Firstly, experimental data and finite element simulation data are combined to constitute a fatigue life database, and the relative error is taken as the training target for the BP neural network. The relationship is established between the relative error and the initial parameters (such as hidden layer nodes and learning rate). Secondly, the GABP-ANN model improves the shortage of the BP model at randomly selected initial training parameters and further enhances the predictive performance. Then, the accuracy and stability of five titanium alloy structural parts under two machine learning (ML) models are analyzed. The experimental values, finite element model (FEM) simulated values, and predicted values of the two ML models are compared and analyzed. It is found that the GABP-ANN model has a significant advantage in predicting the fatigue life of titanium alloy structural parts. Finally, it is verified that the proposed ML models have better data learning ability. The above results indicate that the GABP-ANN technique provides a highly accurate and stable method for predicting the LCF life of titanium alloy structural parts. [ABSTRACT FROM AUTHOR]