Patch-type Segmentation of Voxel Shapes using Simplified Surface Skeletons

We present a new method for decomposing a 3D voxel shape into disjoint segments using the shape’s simplified surface-skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore- and background skeletons map one-to-one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low-curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel-based implementation of our approach and illustrate it on several realistic examples.

Detailed evaluation of topographical effects of Hirtisation post-processing on electron beam powder bed fusion (PBF-EB) manufactured Ti-6Al-4V component

Metal additive manufacturing surface topographies are complex and challenging to characterise due to e.g. steep local slopes, re-entrant features, varying reflectivity and features of interest in vastly different scale ranges. Nevertheless, average height parameters such as Ra or Sa are commonly used as sole parameters for characterisation. In this paper, a novel method for selecting relevant parameters for evaluation is proposed and demonstrated using a case study where the smoothing effects after three processing steps of the electro chemical post-process Hirtisation of a metal AM surface are quantified. The method uses a combination of conventional areal texture parameters, multiscale analysis and statistics and can be used to efficiently achieve a detailed and more relevant surface topography characterisation. It was found that the three process steps have different effects on the surface topography regarding the types and sizes of features that were affected. In total, Sdq was reduced by 97 %, S5v was reduced by 81 % and Sa was reduced by 78 %. A surface texture with much lower average roughness, less deep pits and less steep slopes was produced, which is expected to be beneficial for improved fatigue properties

Modeling the replication of submicron-structured surfaces by micro injection molding

Abstract The replication of submicron surface structures by micro injection molding is a crucial factor in achieving advanced functionalities, such as antimicrobial resistance, in mass-produced plastic products. In this work, we investigate and model the replication quality of laser-induced periodic surface structures by micro injection molding of different bio-based polymers. A comprehensive multiscale model was developed to predict the submicron scale polymer flow, using a numerical model to analyze the polymer behavior in the mold macro cavity and determine the boundary conditions for the filling of the surface structures. The replication of the mold topography was modeled considering topographical parameters, polymer rheology and thermal behavior, and the mold surface energy, which was modified by depositing an atomic layer of alumina on the steel surface structures. The modeling approach was validated against injection molding experiments, in which the mold temperature was varied due to its well-known influence on replication. The sensitivity to polymer selection, mold surface properties, and mold temperature, was captured. A maximum error of 8% showed the accuracy of the multi-scale model

Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders

Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this paper, we propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales. Quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods. Furthermore, with the multiscale deformation components extracted by our method, the user can edit shapes in a coarse-to-fine fashion which facilitates effective modeling of new shapes.Comment: 15 page