In reengineering technical components, the robust automation of reverse engineering (RE) could overcome the need for human supervision in the surface reconstruction process. Therefore, an enhanced computer-based geometric reasoning to derive tolerable surface deviations for reconstructing optimal surface models would promote a deeper geometric understanding of RE downstream processes. This approach integrates advanced surface information into a deep learning-based recognition framework by explicitly labeling geometric outliers and subsurface boundaries. For this purpose, a synthetic dataset is created that morphs nominal surface models to resemble the macroscopic surface pattern of physical components. For the detection of regular geometry primitives, a 3D-CNN is used to analyze the voxelized components based on signed distance field data. This explicit labeling approach enables surface fitting to derive suitable shape features that fulfill the underlying surface constraints.

While application of clustering algorithms to atom probe tomography data have enabled quantification of solute clusters in terms of number density, size, and subcomposition there exist other properties (e.g., volume, surface area, and composition) that are better determined by defining an interface between the cluster and the surrounding matrix. The limitation in composition results from an ion selection step where the expected matrix ion types are omitted from the cluster search algorithm to enhance the contrast between the matrix and cluster and to reduce the complexity of the search. Previously, composition determination within solute clusters has utilized a secondary envelopment and erosion step on top of conventional methods such as maximum separation. In this work, we present a novel stochastic method that combines the particle identification fidelity of a conventional clustering algorithm with the analytical flexibility of mesh-based approaches through the generation of alpha shapes for each identified cluster. The corresponding mesh accounts for concave components of the clusters and determines the volume and surface area of the clusters; additionally, the mesh boundary is utilized to update the total composition according to the internal ions.

We demonstrate that the surface topography of a sample can be reconstructed from electron backscatter diffraction (EBSD) patterns collected with a commercial EBSD system. This technique combines the location of the maximum background intensity with a correction from Monte Carlo simulations to determine the local surface normals at each point in an EBSD scan. A surface height map is then reconstructed from the local surface normals. In this study, a Ni sample was machined with a femtosecond laser, which causes the formation of a laser-induced periodic surface structure (LIPSS). The topography of the LIPSS was analyzed using atomic force microscopy (AFM) and reconstructions from EBSD patterns collected at 5 and 20 kV. The LIPSS consisted of a combination of low frequency waviness due to curtaining and high frequency ridges. The morphology of the reconstructed low frequency waviness and high frequency ridges matched the AFM data. The reconstruction technique does not require any modification to existing EBSD systems and so can be particularly useful for measuring topography and its evolution during in situ experiments.

In this article a novel model framework to simulate cells and their internal structure is described. The model is agent-based and suitable to simulate single cells with a detailed internal structure as well as multi-cellular compounds. Cells are simulated as a set of many interacting particles, with neighborhood relations defined via a Delaunay triangulation. The interacting sub-particles of a cell can assume specific roles – i.e., membrane sub-particle, internal sub-particle, organelles, etc –, distinguished by specific interaction potentials and, eventually, also by the use of modified interaction criteria. For example, membrane sub-particles may interact only on a two-dimensional surface embedded on three-dimensional space, described via a restricted Delaunay triangulation. The model can be used not only to study cell shape and movement, but also has the potential to investigate the coupling between internal space-resolved movement of molecules and determined cell behaviors.

The influence of surface structure and chemistry on high-temperature dewetting of silicate liquids on ceramic surfaces has been investigated. Model systems based on well-defined crystallography and known chemistry have been used to illustrate the effect of surface roughness and chemistry on the dewetting process. Reconstructed ceramic surfaces provide ideal substrates to study effects of surface roughness. It has been shown that the morphology of dewet droplets depend on the length scale and the crystallography of the facets on the surface. Complex pattern formation due to solute redistribution during dewetting is illustrated in the case of SiO2 dewetting on (001) rutile substrates. The role of kinetics on the dewetting process has also been clarified.

This paper describes an algorithm for surface reconstruction from a set of scattered three-dimensional points extracted from an image sequence. In this process, additional information (such as location of the viewpoints and the points visible from a particular viewpoint) is available and can be exploited for accurately recovering the shape of the objects portrayed in the images. Initially, the set of points is subjected to a Delaunay triangulation that fills the convex hull of the set of points with disjoint tetrahedra. The key idea of the shape recovery algorithm is to eliminate triangles that obstruct the visibility of points from certain viewpoints, whose locations are known from the image acquisition process. The major contribution of this paper is that we have been able to design an algorithm for surface reconstruction that handles a wide variety of shapes, as opposed to currently existing techniques.

Reconstruction of the Cu(001) surface due to oxygen gas impingement on a clean copper surface was directly observed by in situ UHV-TEM. Strain contrast between differently oriented surface reconstruction domains, assumed to be √2 × 2√2 R45, were clearly visible by this method. The reconstruction precedes the nucleation of Cu2O islands. When Cu2O islands were partially reduced and then re-oxidized, a dwell time before formation of Cu2O was noted, demonstrating that a reconstructed Cu-O surface monolayer is necessary before oxide formation.

Molecular beam epitaxy growth utilising an RF-plasma nitrogen source was used to study surface reconstruction and surface morphology of GaN on GaAs (001) at 580 °C. While both the nitrogen flow and plasma excitation power were constant, the grown layers were characterised as a function of Ga-flux. In the initial growth stage a (3×3) surface reconstruction was observed. This surface periodicity only lasted up to a maximum thickness of 2.5 ML, followed by a transition to the unreconstructed surface. Samples grown under N-rich, Ga-rich and stoichiometric conditions were characterised by high-resolution scanning electron microscopy and atomic force microscopy. We found that the smoothest surfaces were provided by the N/Ga-ratio giving the thickest layer at the (3×3)=>(1×1) transition. The defect formation at the GaN/GaAs interface also depended on the N/Ga-flux ratio.