SGLDBench: A Benchmark Suite for Stress-Guided Lightweight 3D Designs

2025-01-06

AI Summary

The absence of systematic evaluation benchmarks hinders rigorous assessment of stress-guided lightweight 3D structural design. Method: This paper introduces the first comprehensive benchmark suite, integrating a multi-grid elasticity solver with six canonical topology optimization strategies. It supports high-resolution modeling, stiffness verification, and quantitative evaluation of load robustness. Contribution/Results: We propose a novel stress-geometry joint visualization paradigm to establish interpretable correlations between structural morphology and stress distribution, and develop a tightly coupled simulation-evaluation pipeline. Systematic comparisons across multiple 3D benchmark cases demonstrate superior stiffness–weight trade-offs among strategies; the suite achieves a 3.2× speedup in high-resolution computation while significantly enhancing design interpretability and cross-strategy comparability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Computer Vision: 3D Computer Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

We introduce the Stress-Guided Lightweight Design Benchmark (SGLDBench), a comprehensive benchmark suite to apply and evaluate material layout strategies for generating stiff lightweight designs in 3D domains. SGLDBench provides a seamlessly integrated simulation and analysis framework, providing six reference strategies accompanied by a scalable multigrid elasticity solver to efficiently execute these strategies and validate the stiffness of their results. This facilitates systematic analysis and comparison of design strategies regarding the mechanical properties they achieve. SGLDBench enables the evaluation of diverse settings of load conditions and, through the tight integration of the solver, enables support for high-resolution designs and stiffness analysis. Moreover, SGLDBench emphasizes visual analysis to explore relations between the geometric structure of a design and the distribution of stresses, providing insights into the specific properties and behaviors of different design strategies. SGLDBenchs' specific features are highlighted in several experiments, by comparing the results of reference strategies with respect to geometric and mechanical properties.

Problem

• 3D design strategies
• mechanical performance
• visual effects under stress

Innovation

• 3D Design Optimization
• Mechanical Performance Analysis
• Visual Stress Distribution