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 α platelet thickness, colony size, and β 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 α platelet thickness continuously. However, the α platelet thickness exerts influence on the elongation in a more complicated way. In addition, for a given α platelet thickness, the yield strength and the elongation both increase with decreasing β grain size and colony size. In general, the β grain size and colony size play a more important role in affecting the tensile properties of Ti-6Al-4V alloy than the α platelet thickness.