Viscoelastoplastic Deformation and Damage Response of Titanium Alloy, Ti-6Al-4V, at Elevated Temperatures

Time-dependent deformation and damage behavior can significantly affect the life of aerospace propulsion components. Consequently, one needs an accurate constitutive model that can represent both reversible and irreversible behavior under multiaxial loading conditions. This paper details the characterization and utilization of a multi-mechanism constitutive model of the GVIPS class (Generalized Viscoplastic with Potential Structure) that has been extended to describe the viscoelastoplastic deformation and damage of the titanium alloy Ti-6Al-4V. Associated material constants were characterized at five elevated temperatures where viscoelastoplastic behavior was observed, and at three elevated temperatures where damage (of both the stiffness reduction and strength reduction type) was incurred. Experimental data from a wide variety of uniaxial load cases were used to correlate and validate the proposed GVIPS model. Presented are the optimized material parameters, and the viscoelastoplastic deformation and damage responses at the various temperatures

Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data

Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed packed alloys, are predicted. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy. The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored. The effects of elastic anisotropy on GNN model performance and outlooks for extension of the framework are discussed