ioandanielc/sph_dataset
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… See the full description on the dataset page: https://huggingface.co/datasets/ioandanielc/sph_dataset.
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
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
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 → parametersUsage
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
License
CC BY 4.0.
Contact
Ioan-Daniel Crăciun · TUM · <email-or-HF-handle>
