Alexis C. Lewis

Quality Improvement of Non-manifold Hexahedral Meshes for Critical Feature Determination of Microstructure Materials

This paper describes a novel approach to improve the quality of non-manifold hexahedral meshes with feature preservation for microstructure materials. In earlier works, we developed an octree-based isocontouring method to construct unstructured hexahedral meshes for domains with multiple materials by introducing the notion of material change edge to identify the interface between two or more materials. However, quality improvement of non-manifold hexahedral meshes is still a challenge. In the present algorithm, all the vertices are categorized into seven groups, and then a comprehensive method based on pillowing, geometric flow and optimization techniques is developed for mesh quality improvement. The shrink set in the modified pillowing technique is defined automatically as the boundary of each material region with the exception of local non-manifolds. In the relaxation-based smoothing process, non-manifold points are identified and fixed. Planar boundary curves and interior spatial curves are distinguished, and then regularized using B-spline interpolation and resampling. Grain boundary surface patches and interior vertices are improved as well. Finally, the local optimization method eliminates negative Jacobians of all the vertices. We have applied our algorithms to two beta titanium datasets, and the constructed meshes are validated via a statistics study. Finite element analysis of the 92-grain titanium is carried out based on the improved mesh, and compared with the direct voxel-to-element technique.

Quantitative analysis and feature recognition in 3-D microstructural data sets

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Numerical Modeling of Pit Growth in Microstructure

This work presents the numerical modeling of two-dimensional stable corrosion pit growth by solving the Laplace equation which defines the electric potential within the electrolyte. Microstructural features representative of a 316 stainless steel provides the matrix in which the pit grows. Real microstructural features are incorporated into the computational model. The objective is to determine the influence of the microstructure, specifically crystallographic orientation, on the shape of the pit as it grows over time. The high-resolution definition of the microstructure is obtained by the orientation image microscopy (OIM) technique and is incorporated into the finite element model through a grid-based interpolation functionality. The steel-electrolyte corrosion front movement is simulated with the help of the arbitrary Lagrangian-Eulerian (ALE) meshing technique. The front speed, or the material dissolution rate, is approximated with the use of a Butler-Volmer relationship that relates the dissolution current density to the applied overpotential. The results show that small fluctuations (5–10%) in corrosion potential due to the changing crystal orientation ahead of the corrosion front result in variations in pit shape similar to experimental observations reported in the literature.Copyright

Automated Image Alignment and Distortion Removal for 3-D Serial Sectioning with Electron Backscatter Diffraction

Three-dimensional characterization is required for measuring the true shape, connectivity, and spatial distribution of microstructural features. For accurate property or processing correlations with 3D microstructures, a statistically significant volume of data is required. Serial sectioning has emerged as pertinent method to obtain large volumes of 3D structural information while maintaining a relatively high spatial resolution [1]. For this process, a large series of 2D images are collected on the same area in successive parallel planes then reconstructed to form the 3D volume. Focused Ion Beam (FIB) sectioning in conjunction with SEM imaging excels for the characterization of fine features (<5μm), but lacks the milling rate to collect large statistically relevant volumes of material. Serial sectioning, using mechanical polishing or alternative milling methods [2] for material removal is capable of collecting much larger volumes, with fields of view up to a several mm while imaging with resolutions better than 1μm, allowing it to operate on a length scale relevant for a large number of material problems.

High-Fidelity Reconstruction and Computational Modeling of Metallic Microstructure

The end-objective of this research is to identify critical microstructural features in metals that precipitate plastic flow, and therefore, cause degradation of mechanical performance at higher scales. The material focus is a titanium alloy-β21s. The three-dimensional (3D) microstructure in the mesoscale range was obtained using serial sectioning, optical microscopy, electron backscatter diffraction (EBSD) and computerized 3D reconstruction techniques. The reconstructed volumes, comprising hundreds of beta-Ti grains, provide information on morphology and crystallography. This data was used as input into 3D finite element models to analyze the spatial evolution of state variables, such as stress, strain and crystallographic slip under simple loading conditions. Single crystal hypoelasticity and the assumption of resolved shear stress causing crystal slip were used to represent microstructural material behavior. Evolution of plastic flow with applied loading was analyzed at grain boundary interfaces where most flow occurred. Rendering of large reconstructions into faithful but lean finite element meshes was identified to be the critical issue in simulating accurate material behavior near grain boundaries, establishing definite structure-property relationships at mesoscale and reducing the computational cost.© 2007 ASME