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

Abstract

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.

A model for Ti–6Al–4V microstructure evolution for arbitrary temperature changes

“…Indeed, the yield stress have been reported lower for thicker lath morphology (Kar et al 2006). lath thickness would consequently be an interesting microstructure variable to include in a flow stress model.…”

“…To the knowledge of the author, no quantitative tool correlating microstructure to mechanical properties of titanium alloys is yet available except for the work by Kar et al (2006). Their neural network model is developed to predict yield and ultimate tensile strengths, which are used to identify the influence of individual microstructure features on tensile properties.…”

An Overview of Densification, Microstructure and Mechanical Property of Additively Manufactured Ti-6Al-4V — Comparison among Selective Laser Melting, Electron Beam Melting, Laser Metal Deposition and Selective Laser Sintering, and with Conventional Powder

“…The model developed by Tiley et al [95,96] suggests that the mechanical performance of the (α+β) dual phase Ti-6Al-4V can be estimated based on a variety of microstructural parameters including the size of the α colony. The schematic graph in Figure 19 suggests that increasing size of the α colony may lead to reduced ductility (ε) and yield strength (σ 0.2 ) but in the meantime it may also contribute to better resistance to macro-cracks [95,96].…”

Discovery via Integration of Experimentation and Modeling: Three Examples for Titanium Alloys

“…This allows for consideration of both continuum and microscopic contributions, permitting an analysis of their interrelationships (e.g., a microstructural feature's contribution to the strength which is often associated with the continuum approach), as well as their distinct differences (e.g., the contribution of a microstructural feature on microcracking or void nucleation). This builds on previous efforts to model the tensile properties in both b and a + b-processed Ti-6Al-4V 5,6 with an associated uncertainty of ±2.5% error. These approaches have been based on neural-network models incorporating Bayesian statistics developed by MacKay 7-10 and include accurate descriptions of the microstructural features based on rigorously developed stereological methods, as described elsewhere.…”