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

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

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 placeholder — fill in after acceptance]

License

CC BY 4.0.

Contact

Ioan-Daniel Crăciun · TUM ·