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license: mit
task_categories:
- tabular-classification
- tabular-regression
language:
- code
tags:
- finite-element-analysis
- meshing
- structural-engineering
- cae
- simulation
- geometry
- stress-analysis
- adaptive-mesh-refinement
- engineering-education
- ansys
- python
pretty_name: MeshRefine-FEA Corner Bracket Dataset
size_categories:
- 10k<n<100k
---
# MeshRefine-FEA Corner Bracket Dataset
## Overview
This dataset contains simulation-driven mesh refinement information for right-angle corner bracket geometries under various loading conditions. All simulation data was generated using the ANSYS Mechanical Python API, applying up to five localized mesh refinement iterations per case based on stress concentration analysis.
The dataset supports research in:
- Feature-aware mesh refinement
- Convergence prediction
- Intelligent simulation-preprocessing assistants
- Lightweight agentic decision-making for FEA
---
## Dataset Structure
### feature_level_augmented.csv
Each record corresponds to a single CAD face and includes:
- Geometric category: `{hole, plane, fillet}`
- Spatial information: center `(cx, cy, cz)`, normal `(nx, ny, nz)`
- Area of the face
- Load relation: `{load, fixed, free}`
- Distance from load application region
- Final **local mesh size**
- Binary refinement label
Primary use: feature-level refinement recommendation & local sizing
---
### part_level.csv
Each record represents a full simulation case and includes:
- Total and per-type face counts
- Load type, direction, and magnitude
- Final **global mesh size**
- Convergence status (after ≤5 iterations)
Primary use: global mesh sizing & convergence prediction
---
## Data Generation Workflow
- CAD Source: Public STEP models from McMaster-Carr (corner brackets)
- Initial mesh based on bounding box + thickness metrics
- Stress-driven refinement:
- Local size reduction: 0.75–0.85× per iteration
- Convergence rule: <15% change in max von Mises stress
- Marked as singularity if not converged after 5 iterations
All simulations were scripted using `ansys.mechanical.core` to ensure consistency and automation.
---
## Target ML Tasks
This dataset enables supervised learning for 4 predictive objectives:
| Task | Level | Type |
|------|------|------|
| Region refinement decision | Feature | Binary classification |
| Local mesh sizing | Feature | Regression |
| Convergence prediction | Part | Binary classification |
| Global mesh sizing | Part | Regression |
These tasks reflect the common failure points for early-career FEA users.
---
## Dataset Statistics
| Property | Value |
|---------|------|
| # of CAD parts | 25 |
| # of simulation cases | 7,500 |
| Max refinement iterations | 5 |
| Domain | Industrial brackets |
---
## Example Use Cases
- Predict stress-driven refinement regions
- Prevent non-convergent simulation setup
- Automatically adjust mesh density for faster solvers
- Provide explainable refinement guidance for students
---
## License
- Based on publicly accessible mechanical CAD
- Free for research and educational use
- Please cite this dataset in derivative work
---
## Acknowledgements
Created to support intelligent mesh refinement for beginner FEA simulation tools.
---
## Citation
```bibtex
@dataset{meshrefine_fea_2025,
title={MeshRefine-FEA Corner Bracket Dataset},
author={X. Tang et al.},
year={2025},
note={Finite Element mesh refinement dataset for feature-aware learning}
} |