The development of a set of computational tools that permit microstructurally based predictions for the tensile properties of commercially important titanium alloys, such as Ti-6Al-4V, is a valuable step toward the accelerated maturation of materials. This paper will discuss the development of neural network models based on a Bayesian framework to predict the yield and ultimate tensile strengths of Ti-6Al-4V at room temperature. The development of such rules-based model requires the population of extensive databases, which in the present case are microstructurally based. The steps involved in database development include producing controlled variations of the microstructure using novel approaches to heat treatments, the use of standardized stereology protocols to characterize and quantify microstructural features rapidly, and mechanical testing of the heat-treated specimens. These databases have been used to train and test neural network models for prediction of tensile properties. In addition, these models have been used to identify the influence of individual microstructural features on the tensile properties, consequently guiding the efforts toward development of more robust mechanistically based models. Based on the neural network model, it is possible to investigate the influence of individual microstructural features on the tensile properties, and in certain cases these dependencies can point toward unrecognized phenomena. For example, the apparently unexpected trend of increase in tensile strength with increasing prior -grain size has led to the determination of the pronounced role of the basketweave microstructure in strengthening these alloys, especially in case of larger prior  grains.
Mechanical properties of α/β Ti alloys are closely related to their microstructure. The complexity of the microstructural features involved makes it rather difficult to develop models for predicting properties of these alloys. Advances in stereology and microscopy permit rapid characterization of various features in Ti alloys including Widmanstätten α-laths, grain sizes, grain shapes, colony structures and volume fractions of different phases. This research documents the stereology procedures for characterizing microstructural features in Ti alloys, including the use of three-dimensional serial sectioning and reconstruction procedures for developing through material measurements. The resulting data indicate the powerful characterization processes now available, and the ability to rapidly assess microstructural features in Ti alloys. The processes were tested using Ti-62222 by serial sectioning the sample and conducting automated stereology protocols to determine features. In addition, three-dimensional reconstruction was completed on a Ti-6242 sample to evaluate lath interactions within the alloy. Results indicate the tremendous potential for characterizing microstructures using advanced techniques.
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