Abstract
The common approach to crystal-plasticity finite element modeling for load-bearing prediction of metallic structures involves the simulation of simplified grain morphology and substructure detail. This paper details a methodology for predicting the structure–property effect of as-manufactured microstructure, including true grain morphology and orientation, on cyclic plasticity, and fatigue crack initiation in biomedical-grade CoCr alloy. The methodology generates high-fidelity crystal-plasticity finite element models, by directly converting measured electron backscatter diffraction metal microstructure grain maps into finite element microstructural models, and thus captures essential grain definition for improved microstructure–property analyses. This electron backscatter diffraction-based method for crystal-plasticity finite element model generation is shown to give approximately 10% improved agreement for fatigue life prediction, compared with the more commonly used Voronoi tessellation method. However, the added microstructural detail available in electron backscatter diffraction–crystal-plasticity finite element did not significantly alter the bulk stress–strain response prediction, compared to Voronoi tessellation–crystal-plasticity finite element. The new electron backscatter diffraction-based method within a strain-gradient crystal-plasticity finite element model is also applied to predict measured grain size effects for cyclic plasticity and fatigue crack initiation, and shows the concentration of geometrically necessary dislocations around true grain boundaries, with smaller grain samples exhibiting higher overall geometrically necessary dislocations concentrations. In addition, minimum model sizes for Voronoi tessellation–crystal-plasticity finite element and electron backscatter diffraction–crystal-plasticity finite element models are proposed for cyclic hysteresis and fatigue crack initiation prediction.
Highlights
The development of computational prediction methods for microstructure-sensitive fatigue crack initiation (FCI) is an important problem that will benefit a wide range of industries, including aerospace[1], medical-device 2, and power generation 3, 4
Von Mises stress values have a scatter of up to 11.7% when fewer than 100 elements are defined in individual grains 80. This quantitative representative volume element (RVE) convergence study ensures that the resulting simulation scatter due to inherent Voronoi tessellation (VT) algorithm shortcomings is minimized to a reasonable level so that both VT and electron backscatter diffraction (EBSD) models represent the same grain size distribution
A method is developed for converting electron back-scatter diffraction data images of microstructure into crystal plasticity finite element models, including grain morphology, size and orientation information
Summary
The development of computational prediction methods for microstructure-sensitive fatigue crack initiation (FCI) is an important problem that will benefit a wide range of industries, including aerospace[1], medical-device 2, and power generation 3, 4. The critical importance of characterisation of the interrelationships between manufacturing process, microstructure, material properties and mechanical performance (structural integrity) 5, 6 has been highlighted by a recent aeroengine fan 92 hub failure investigation 7, 8. This report provides detailed proof of the serious risks associated with not considering these relationships, for fatigue crack initiation in real applications, in this case, an aeroengine fan hub failure of TI-6Al-4V alloy, not previously considered susceptible to facet fatigue. CoCr alloys are employed in the medical device industry for a number of important 96 applications, including cardiovascular stents 9 and orthopaedic hip implants 10, 11, where design against premature fatigue failure is of critical importance. Advances in computational modelling methodologies, including crystal plasticity finite element (CPFE) 101 modelling, have permitted advanced microstructural mechanical characterisation 13
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