Abstract

Evaluation and selection of polycrystalline microstructures for fatigue resistance through computational means is hampered by the high cost of CPFEM for elastic-plastic analysis. In this work, novel approaches are employed to compare the projected HCF (and LCF) resistance of alpha-beta titanium microstructures with a variety of textures and boundary conditions based on mesoscopic FIPs. Specifically, a materials knowledge system approach for modeling of local grain responses based on spatial statistics is developed to quickly evaluate strain fields for a set of statistical volume elements (SVEs) representing a particular microstructure. Then, an explicit integration scheme (or a calibrated function) is developed to estimate the plastic strain in each voxel, allowing for the calculation of FIPs for each SVE and the evaluation of the robustness of each microstructure for HCF applications. This data science approach is orders of magnitude faster than traditional CPFEM methods, making it possible to compare large numbers of microstructures and identify those most suitable

Computationally Efficient Protocols to Evaluate the Fatigue Resistance of Polycrystalline Materials from npaulson </div>