SGLDBench

Code Repository: GitHub – SGLDBench
Contact: junpeng.wang@tum.de | dennis.bukenberger@tum.de

Description

This page is created to showcase and exchange the use of SGLDBench. Reporting bugs, leaving comments, and sharing cool application cases are warmly welcomed!

SGLDBench is a comprehensive benchmark suite for applying and evaluating material layout strategies to generate stiff, lightweight structural designs in 3D domains, with a special focus on high-resolution designs.

SGLDBench provides a seamlessly integrated simulation and analysis framework, including six reference strategies and a scalable multigrid elasticity solver to efficiently execute these strategies and validate the stiffness of their results.

SGLDBench is primarily implemented using MATLAB, but also has external calls to C++ code through the MEX interface for performance reasons, and to executables and Python codes for functionality reasons, which provides a handy stencil for future extensions.

For an overview of this tool, please have a look at the README of the code repository and watch the guide videos.

Updates

What can SGLDBench do?

Data Format

Related Publication

Title: "SGLDBench: A Benchmark Suite for Stress-Guided Lightweight 3D Designs"
Authors: Junpeng Wang, Dennis Bukenberger, Simon Niedermayr, Christoph Neuhauser, Jun Wu, Rüdiger Westermann
Venue: IEEE Transactions on Visualization and Computer Graphics, 2025
[Read the Paper]

Application-specific Branches/Extensions

  • TBA
  • Demonstration Cases

    Case 3: Creating a bone-mimicking infill for a spine model via porous infill optimization

    Case 2: Infill Design for a Molar

    Context: Given a geometric model of a molar described in a triangular surface mesh, one can generate different types of infill patterns under the same material budget (e.g., 50%) and compare the stiffness measured by the compliance value (C).

    Design domain
    FEA model
    C=1.26C0
    C=1.59C0
    C=1.92C0
    C=1.86C0
    C=2.49C0
    C=3.25C0

    Case 1: Topology Optimization Practices with SGLDBench

    Contexts: With SGLDBench, beginners stepping into high-resolution topology optimization can quickly create topology-optimized high-resolution designs, exploring the effects of different boundary conditions, simulation resolutions, filtering radius. SGLDBench also integrates several simple shapes (cuboid, L-shape, cylinder) to facilitate this.

  • Practice 1.1 Same cuboid design domain but with different boundary conditions. Resolution: 300x150x150
  • Practice 1.2 Same optimization problem with different simulaton resolutions
  • 100x50x50, C=12.22C0
    200x100x100, C=9.64C0
    400x200x200, C=9.20C0
    800x400x400, C=7.91C0
  • Practice 1.3 Same optimization problem with a different filter radius (r). Resolution: 500x250x250
  • r=2, C=8.08C0
    r=4, C=8.59C0
    r=8, C=9.78C0
  • Practice 1.4 Others