license: cc-by-4.0
language:
- en
pretty_name: RDDAC — Real Deep Drawing and Cutting Dataset (Teaser)
size_categories:
- n<1K
tags:
- croissant
- experimental-data
- measurement
- sim-to-real
- sim2real
- scientific-machine-learning
- sciml
- sheet-metal-forming
- metal-forming
- deep-drawing
- stamping
- cutting
- manufacturing
- materials-science
- process-monitoring
- laser-scan
- point-cloud
- time-series
- sensor-data
- digital-twin
- regression
- benchmark
- engineering
- automotive
- hdf5
task_categories:
- tabular-regression
- time-series-forecasting
- other
RDDAC — Real Deep Drawing and Cutting Dataset
Measured point clouds of one experiment after deep drawing (OP10, left) and cutting (OP20, right), colored by the deviation from the matching DDACS simulation.
A large-scale experimental dataset of 9,000 physical deep-drawing and cutting experiments — the real-world counterpart to the DDACS FEM simulations. Each experiment forms a modified quadratic cup from DP600 dual-phase steel (deep drawing in OP10, cutting in OP20) and records press force signals, sheet-thickness and oil-film traverses, and high-resolution 3D laser scans of the part after each operation. Use it to quantify the simulation-to-reality gap, train models on real process data, or validate DDACS-trained surrogates against physical measurements.
| Experiments | 9,000 |
| Total size | ~87 GB (HDF5, lossless) |
| Process steps per experiment | 2 (OP10 deep drawing, OP20 cutting) |
| Parameter space | 2 geometries x 3 blankholder forces x 3 oil types (18 categories) |
| Repetitions | up to 500 per category |
| Train / val / test | 7,200 / 900 / 900 (predefined, seed 42) |
| Matching simulations | DDACS rddac.zip (~9 GB), fetched by rddac download |
Documentation · Dataset DOI · Paper
About this sample
This is a ~174 MB teaser of RDDAC — 18 experiments (one per category) plus the Croissant 1.1 manifest, the complete process-parameter table, and the six tutorial notebooks — so you can explore the schema and run every tutorial in seconds before committing to the full download.
data/
metadata.json Croissant 1.1 manifest (the dataset schema)
process_parameters.csv parameters + splits for all 9,000 experiments
h5/sample.zip 18 experiments, one per category (all modalities)
rddac_documentation.pdf dataset documentation
notebooks/ six end-to-end tutorials (see notebooks/README.md)
Croissant manifest. data/metadata.json is the
Croissant 1.1 manifest — the machine-readable
schema (every HDF5 field and CSV column) that rddac.load() and any
Croissant-aware tool consume. It is the same manifest published with the full
dataset on DaRUS (doi:10.18419/DARUS-5589).
Installation
pip install rddac # add the PyTorch adapter with: pip install 'rddac[torch]'
Basic usage
rddac.open_h5 opens a single experiment in memory and returns an h5py.File;
the visualization helpers plot the raw modalities directly.
import rddac
# Open one bundled experiment. Four raw modalities per file.
with rddac.open_h5(0, data_dir="data") as f:
force = f["force/data"][:] # (n, 8): time, load cells, temp, position, total force
z = f["pointcloud/op10/z"][:] # (6400000,) flat laser-scan buffer
lumi = f["pointcloud/op10/luminescence"][:]
# The OP10 scan as a 3D point cloud.
points = rddac.scan_to_pointcloud(z, lumi, stride=4)
rddac.plot_point_cloud(points, point_size=0.4)
PyTorch integration
RDDACDataset is a torch.utils.data.IterableDataset over a Croissant view. It
auto-shards across DataLoader workers and DDP ranks, and silently skips
experiments whose zip is missing — so partial downloads (like this teaser) stream
cleanly.
from rddac.pytorch import RDDACDataset
from torch.utils.data import DataLoader
ds = RDDACDataset(view="force-curve", data_dir="data")
for batch in DataLoader(ds, batch_size=1, num_workers=0):
force = batch["force_data"]
break
Tutorials
Six end-to-end notebooks ship in notebooks/ and are published on
Read the Docs:
- Getting started — install, inspect one experiment, 3D point cloud plot.
- Build your own view —
rddac.add_view, manifest inspection, custom RecordSets. - PyTorch training —
RDDACDataset, filters, train/val/test splits. - Visualization — scans, point clouds, force curves, traverses.
- Loose HDF5 recipe — pandas +
h5pyafter--extract --remove-zip. - Streaming & numpy export —
iter_view,export_to_numpy.
Relationship to DDACS
RDDAC is the experimental counterpart to the DDACS
simulation dataset: same cup geometry, same DP600 steel, same two-stage OP10/OP20
process. The matching FEM simulations are published in the DDACS dataset as
rddac.zip (~9 GB); rddac download fetches them alongside the measurements. The
rddac package API mirrors ddacs one to one, so analysis code moves between
simulation and experiment by swapping the import.
⬇️ Get the full dataset
This sample contains 18 of 9,000 experiments. The complete RDDAC dataset — 9,000 experiments, ~87 GB of lossless HDF5, with the predefined 7,200 / 900 / 900 train/val/test split — is hosted on DaRUS with a citable DOI:
➡️ https://doi.org/10.18419/DARUS-5589
Everything you ran here scales to the full release unchanged — just point the same code at the full download, or let the package fetch it:
pip install rddac
rddac download # full release (rddac download --small for this sample)
Citation
If you use this dataset or code in your research, please cite both the dataset and the paper:
@dataset{baum2026rddac,
title={Real Deep Drawing and Cutting Dataset},
author={Baum, Sebastian and Heinzelmann, Pascal},
year={2026},
publisher={DaRUS},
doi={10.18419/DARUS-5589}
}
@article{baum2026deviation,
title={Statistical Analysis of Simulation to Reality Deviation in Deep Drawing with a Benchmark Dataset},
author={Baum, Sebastian and Heinzelmann, Pascal and Clau{\ss}, P. and others},
journal={Transactions of the Indian Institute of Metals},
volume={79},
pages={176},
year={2026},
doi={10.1007/s12666-026-03870-5}
}
License
Data: CC BY 4.0. Package code: MIT.