Datasets:
Languages:
English
Size:
100K<n<1M
Tags:
additive-manufacturing
laser-powder-bed-fusion
smoothed-particle-hydrodynamics
melt-pool
keyhole
physics-simulation
DOI:
License:
| license: cc-by-4.0 | |
| language: | |
| - en | |
| pretty_name: SPH-Simulated LPBF Melt-Pool Dataset | |
| size_categories: | |
| - 100K<n<1M | |
| task_categories: | |
| - image-classification | |
| - image-segmentation | |
| tags: | |
| - additive-manufacturing | |
| - laser-powder-bed-fusion | |
| - smoothed-particle-hydrodynamics | |
| - melt-pool | |
| - keyhole | |
| - physics-simulation | |
| - scientific-machine-learning | |
| # SPH-Simulated LPBF Melt-Pool Dataset | |
| Single-track laser powder bed fusion (LPBF) melt-pool simulations for Ti-6Al-4V, | |
| produced with the LAMAS smoothed-particle-hydrodynamics solver. 241 simulations | |
| sampled uniformly i.i.d. over a 4D process-parameter cube (laser power, scan | |
| speed, laser spot radius, substrate temperature), spanning conduction, transition, | |
| and keyhole regimes. | |
| Companion to the NeurIPS 2026 Evaluations & Datasets Track submission | |
| *A Simulation-Based Dataset for Melt Pool Dynamics in Single-Track Laser Powder | |
| Bed Fusion*. | |
| See examples folder in order to reproduce the results of the toy models. | |
| Maintained by Ioan-Daniel Crăciun, Chair for Physics-Enhanced Machine Learning, | |
| Technical University of Munich. | |
| ## Stats | |
| | | | | |
| |---|---| | |
| | Simulations | 241 | | |
| | Frames | 65,472 | | |
| | Images (3 perspectives) | 196,416 | | |
| | Top / front resolution | 256 × 256 px | | |
| | Side resolution | 512 × 256 px | | |
| | Material | Ti-6Al-4V | | |
| | Atmosphere | Argon | | |
| **Regime label distribution:** Initial Emptiness 5.0% / Forming 15.8% / Conduction 67.0% / Keyhole 0.8%. | |
| The keyhole share is a lower bound — manual relabeling is ongoing. | |
| ## Labels | |
| Two label types per frame: | |
| - **Per-pixel phase** (image RGB): red = melt, green = solid substrate, blue = gas. | |
| - **Per-frame regime** (4 classes): Initial Emptiness, Forming, Conduction, Keyhole. | |
| Regime labels are seeded by a depth-based heuristic and verified through a | |
| two-round human review with majority-vote consolidation. | |
| ## Process-parameter ranges | |
| | Parameter | Symbol | Min | Max | Unit | | |
| |---|---|---|---|---| | |
| | Laser power | P | 76.70 | 249.99 | W | | |
| | Scan speed | v | 0.204 | 0.998 | m/s | | |
| | Laser spot radius | r_s | 45.10 | 89.70 | µm | | |
| | Substrate temp. | T_s | 300.3 | 399.6 | K | | |
| Fixed: particle spacing 4 µm, track length 1200 µm, end time 2.1 ms, | |
| laser absorptance 0.2, wall temperature 300 K. | |
| ## Layout | |
| ``` | |
| experiments/<hash>/ | |
| ├── images/{top,side,front}/ # PNGs per timestep | |
| ├── monitoring/*.dat # scalar time series | |
| ├── labels.csv # per-frame regime labels | |
| └── metadata.json # process parameters | |
| index.json # hash → parameters | |
| ``` | |
| ## Usage | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| path = snapshot_download(repo_id="ioandanielc/sph_dataset", repo_type="dataset") | |
| ``` | |
| ## Limitations | |
| Single-track on bare substrate (no powder bed, no multi-track). Fixed laser | |
| absorptance. 2D cross-sections, not full 3D volumes. Keyhole class sparse by | |
| design — the dataset is the Stage-1 initialization for an active-learning loop. | |
| Not yet validated against experimental measurements. | |
| ## Citation | |
| ```bibtex | |
| [bibtex placeholder — fill in after acceptance] | |
| ``` | |
| ## License | |
| CC BY 4.0. | |
| ## Contact | |
| Ioan-Daniel Crăciun · TUM · <email-or-HF-handle> | |