Microstructure-Tensile Properties Correlation for the Ti-6Al-4V Titanium Alloy

Finding the quantitative microstructure-tensile properties correlations is the key to achieve performance optimization for various materials. However, it is extremely difficult due to their non-linear and highly interactive interrelations. In the present investigation, the lamellar microstructure features-tensile properties correlations of the Ti-6Al-4V alloy are studied using an error back-propagation artificial neural network (ANN-BP) model. Forty-eight thermomechanical treatments were conducted to prepare the Ti-6Al-4V alloy with different lamellar microstructure features. In the proposed model, the input variables are microstructure features including the alpha platelet thickness, colony size, and beta grain size, which were extracted using Image Pro Plus software. The output variables are the tensile properties, including ultimate tensile strength, yield strength, elongation, and reduction of area. Fourteen hidden-layer neurons which can make ANN-BP model present the most excellent performance were applied. The training results show that all the relative errors between the predicted and experimental values are within 6%, which means that the trained ANN-BP model is capable of providing precise prediction of the tensile properties for Ti-6Al-4V alloy. Based on the corresponding relations between the tensile properties predicted by ANN-BP model and the lamellar microstructure features, it can be found that the yield strength decreases with increasing alpha platelet thickness continuously. However, the alpha platelet thickness exerts influence on the elongation in a more complicated way. In addition, for a given alpha platelet thickness, the yield strength and the elongation both increase with decreasing beta grain size and colony size. In general, the beta grain size and colony size play a more important role in affecting the tensile properties of Ti-6Al-4V alloy than the alpha platelet thickness.