Fabrication-aware structural form-finding for additive manufacturing - An equilibrium-based Approach - Data
Authors/Creators
Description
README.txt
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1. Title of Dataset
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Fabrication-aware structural form-finding for additive manufacturing: An equilibrium-based Approach - Data
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2. Authors
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[List all authors with affiliations and optional contact details]
Example:
- Frederic Chovghi, Professorship of Structural Design, Department of Architecture, TUM School of Engineering and Design, Technical University of Munich, Germany
- Christiane Richter, Professorship of Structural Design, Department of Architecture, TUM School of Engineering and Design, Technical University of Munich, Germany
- Prof. Dr. Pierluigi D’Acunto, Professorship of Structural Design, Department of Architecture, TUM School of Engineering and Design, Technical University of Munich, Germany
Contact: frederic.chovghi@tum.de
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3. Description
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This dataset accompanies the paper *Fabrication-aware structural form-finding for additive manufacturing: An equilibrium-based approach*
and contains all relevant data for reproducing and understanding the workflow from computational form finding to physical fabrication.
Included files:
1. Grasshopper Definition (.ghx) — The parametric model used for form finding.
2. OBJ Files (.obj) — Generated and laser-scanned geometries for comparison.
3. MP4 Files (.mp4) — Videos documenting the additive manufacturing / robotic printing process.
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4. File Overview
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| File Type | Folder / File Name Example | Description |
|------------|----------------------------|--------------|
| .ghx | /scripts/form_finding_definition.ghx | Grasshopper file performing structural form finding. |
| .obj | /geometry/generated_form.obj /geometry/laser_scan.obj | Mesh geometries: computational vs. scanned. |
| .mp4 | /videos/printing_sequence_01.mp4 | Video documentation of the printing process. |
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5. Software Requirements
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- Rhinoceros 7 or later
- Grasshopper (pre-installed with Rhino)
- Optional plug-ins: Karamba3D, Human
- Any 3D viewer for .obj files (e.g., MeshLab, Rhino, Blender)
- Standard media player for .mp4 files
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6. Data Usage and Reproduction
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To reproduce the workflow:
1. Open the .ghx file in Rhino + Grasshopper.
2. Adjust parameters as needed (e.g., boundary conditions, loads, constraints).
3. Export resulting geometries to .obj format.
4. Compare exported .obj to the laser-scanned version using Rhino or CloudCompare.
5. Review the .mp4 files for fabrication details and material behavior.
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7. Structure of the Repository
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/formfinding_data/
│
├── scripts/
│ └── CEM_REM_Dome.ghx
│ └── CEM_REM_Dome_non_planar.ghx
│
├── geometry/
│ ├── Digital_Model.obj
│ └── Laser_Scan_Hyperboloid.obj
│
├── videos/
│ ├── Print + REM - Dome.mp4
│ ├── Print + REM - Hyperboloid.mp4
│
└── README.txt
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8. File Naming Conventions
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- CEM_REM_*.ghx : Form-finding scripts
- Digital_Model_*.obj : Computationally derived geometries
- Laser_Scan_*.obj : Laser-scanned geometries
- Video_*.mp4 : Recorded fabrication sequences
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9. License
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This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
You are free to share and adapt the material with proper attribution.
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10. Citation
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Please cite this dataset as:
Chovghi F., Richter C., D’Acunto P., Fabrication-aware structural form-finding for additive manufacturing: An equilibrium-based Approach - Data, Zenodo, 2025, DOI: https://doi.org/10.5281/zenodo.17857879
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11. Funding and Acknowledgements
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The research was partially supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
under project number 414265976 – TRR 277: Additive Manufacturing in Construction.
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12. Related Publications
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Chovghi F., Richter C., D’Acunto P., Fabrication-aware structural form-finding for additive manufacturing: An equilibrium-based approach, Automation in Construction, vol. 178, 2025, DOI: https://doi.org/10.1016/j.autcon.2025.106386
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End of README.txt
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Files
Fabrication-aware structural form-finding for additive manufacturing - An equilibrium-based Approach - Data.zip
Files
(209.0 MB)
| Name | Size | Download all |
|---|---|---|
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md5:b31d2363c1a8884a52d605e0fff063f9
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Additional details
Related works
- Is supplement to
- Publication: 10.1016/j.autcon.2025.106386 (DOI)
Funding
- Deutsche Forschungsgemeinschaft
- 414265976