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Hex Me If You Can.

P-A Beaufort1, M Reberol2, D Kalmykov1

  • 1University of Bern.

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|January 13, 2023
PubMed
Summary
This summary is machine-generated.

The HexMe dataset provides 189 tetrahedral meshes for evaluating hexahedral meshing algorithms. Current state-of-the-art methods struggle, with low success rates for meshing feature curves and surfaces.

Keywords:
CCS Concepts• Computing methodologies → Mesh geometry models• General and reference → Evaluation• Information systems → Test collections

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Area of Science:

  • Computational geometry
  • Computer-aided design (CAD)
  • Finite element analysis (FEA) meshing

Background:

  • Evaluating hexahedral meshing algorithms requires standardized datasets.
  • Existing benchmarks lack comprehensive feature tagging and diverse complexity.
  • Automated and sustainable mesh generation workflows are crucial for research.

Purpose of the Study:

  • Introduce the HexMe dataset, comprising 189 tetrahedral meshes with tagged features.
  • Enable consistent and meaningful evaluation of hexahedral meshing algorithms.
  • Provide a benchmark for assessing meshing accuracy on feature points, curves, and surfaces.

Main Methods:

  • Generated 189 tetrahedral meshes using Gmsh from 63 CAD models across simple, nasty, and industrial categories.
  • Classified CAD models to represent diverse challenges in hexahedral mesh generation.
  • Developed an automated, extensible, and sustainable mesh generation workflow using Snakemake.

Main Results:

  • The HexMe dataset includes three mesh types per CAD model: uniform, curvature-adapted, and box-embedded.
  • A public GitHub repository hosts the HexMe workflow, encouraging contributions and future development.
  • State-of-the-art hexahedral meshing algorithms show limitations, with only 19% of inputs correctly meshing all features.

Conclusions:

  • HexMe dataset reveals significant challenges in hexahedral mesh generation, particularly for feature curves (48.5% success) and surfaces (34.6% success).
  • The dataset facilitates robust evaluation and highlights areas for improvement in meshing algorithm development.
  • HexMe is designed for evolution, supporting future research and external contributions.