Strategies for rapid parametric assessment of microstructure-sensitive fatigue for HCP polycrystals

Abstract

Traditionally, crystal plasticity finite element method (CPFEM) simulations have been used to capture the variability in the microstructure-scale response of polycrystalline metals. However, these types of simulations are computationally expensive and require significant resources. To explore the large space of microstructures (reflecting a variety of grain shape, size, and orientation distributions) within the practical constraints of computational resources, a more efficient strategy is required. The purpose of this work is to explore the viability of leveraging the recently established, high-throughput Materials Knowledge System (MKS) for fast evaluation of high cycle fatigue (HCF) performance of candidate microstructures. More specifically, we explore the feasibility of estimating the mesoscale strain fields in hexagonal close packed (HCP) α-titanium polycrystals during HCF loading conditions using the computationally low-cost MKS approach, and subsequently estimating the slip system activities via decoupled numerical integration of the relevant crystal plasticity (CP) constitutive relations. The computed slip activities are then used to arrive at extreme value distributions (EVDs) of fatigue indicator parameters (FIPs). As critical validation of this reduced-cost computational strategy, it is shown that the FIP distributions in the HCF regime estimated using this novel strategy are in reasonable agreement with those computed directly using the conventional CPFEM approach. Additionally, the computational advantages of the MKS and decoupled numerical integration approach over the traditional, computationally-expensive, CPFEM approach are presented and discussed.