Modeling the tensile properties in β-processed α/β Ti alloys

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 β-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 β grains. This article is based on a presentation made in the symposium “Computational Aspects of Mechanical Properties of Materials,” which occurred at the 2005 TMS Annual Meeting, February 13–17, 2005, in San Francisco, CA, under the auspices of the MPMD-Computational Materials Science & Engineering (Jt. ASM-MSCTS) Committee.