An adaptive-remeshing framework to predict impact-induced skull fracture in infants.

Infant skull fractures are common in both accidental and abusive head trauma, but identifying the cause of injury may be challenging without adequate evidence. To better understand the mechanics of infant skull fracture and identify environmental variables that lead to certain skull fracture patterns, we developed an innovative computational framework that utilizes linear elastic fracture mechanics theory to predict skull fracture as a first step to study this problem. The finite element method and adaptive-remeshing technique were employed to simulate high-fidelity, geometrically explicit crack propagation in an infant skull following impact. In the framework, three modes of stress intensity factors are calculated by means of the M-integral using the commercial analysis code, FRANC3D, and are used as measures of crack driving force. The anisotropy of infant skulls is represented by means of a transversely isotropic constitutive model and a direction-dependent fracture-toughness locus. The ability of the framework to predict impact-induced fracture patterns is validated by comparison with experimentally observed fracture patterns from the literature.

Effects of laser-energy density and build orientation on the structure-property relationships in as-built Inconel 718 manufactured by laser powder bed fusion.

This study investigates the effects of build orientation and laser-energy density on the pore structure, microstructure, and tensile properties of Inconel 718 manufactured by laser powder bed fusion. Three different build conditions were selected for comparison based on previous research (namely, the conditions that resulted in the worst and best fatigue lifetimes): 0° build orientation and 38 J/mm3 laser-energy density, 0° build orientation and 62 J/mm3 laser-energy density, and 60° build orientation and 62 J/mm3 laser-energy density. Differences in porosity were measured between each build condition. In terms of microstructure, all three conditions exhibited a predominantly <001> texture in the build direction, grains elongated in the build direction, and a sub-grain structure oriented with the build direction that consisted of dislocation networks decorated with nano-scale precipitates. Build orientation (0° versus 60° with respect to the build plate) produced a difference in yield strength due to anisotropic grain morphology and effective grain size. The low laser-energy density specimens showed a significant decrease in all mechanical properties compared to the high laser-energy density specimens because the amount (6.91% by volume) and size of the lack-of-fusion porosity (from insufficient melting) sur-passed a level at which microstructure (the grain and sub-grain structure) no longer governs quasi-static mechanical properties. This work provides insight that could enable the tunability of structure-property relationships in as-built Inconel 718 by optimizing laser-energy density and build orientation.

High-throughput feature extraction for measuring attributes of deforming open-cell foams.

Metallic open-cell foams are promising structural materials with applications in multifunctional systems such as biomedical implants, energy absorbers in impact, noise mitigation, and batteries. There is a high demand for means to understand and correlate the design space of material performance metrics to the material structure in terms of attributes such as density, ligament and node properties, void sizes, and alignments. Currently, X-ray Computed Tomography (CT) scans of these materials are segmented either manually or with skeletonization approaches that may not accurately model the variety of shapes present in nodes and ligaments, especially irregularities that arise from manufacturing, image artifacts, or deterioration due to compression. In this paper, we present a new workflow for analysis of open-cell foams that combines a new density measurement to identify nodal structures, and topological approaches to identify ligament structures between them. Additionally, we provide automated measurement of foam properties. We demonstrate stable extraction of features and time-tracking in an image sequence of a foam being compressed. Our approach allows researchers to study larger and more complex foams than could previously be segmented only manually, and enables the high-throughput analysis needed to predict future foam performance.

Reconstructed and analyzed X-ray computed tomography data of investment-cast and additive-manufactured aluminum foam for visualizing ligament failure mechanisms and regions of contact during a compression test.

Three stochastic open-cell aluminum foam samples were incrementally compressed and imaged using X-ray Computed Tomography (CT). One of the samples was created using conventional investment casting methods and the other two were replicas of the same foam that were made using laser powder bed fusion. The reconstructed CT data were then examined in Paraview to identify and highlight the types of failure of individual ligaments. The accompanying sets of Paraview state files and STL files highlight the different ligament failure modes incrementally during compression for each foam. Ligament failure was classified as either "Fracture" (red) or "Collapse" (blue). Also, regions of neighboring ligaments that came into contact that were not originally touching were colored yellow. For further interpretation and discussion of the data, please refer to Matheson et al. (2017) [1].