rddac 1.0.0

Python package for the Real Deep Drawing and Cutting (RDDAC) Dataset

Real Deep Drawing and Cutting (RDDAC) 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

The rddac package ships with the dataset and provides a Croissant native interface: one CLI for the download, one Python module for access, and an optional PyTorch IterableDataset for training.

Installation

pip install rddac

The PyTorch adapter is an optional extra. For hardware specific PyTorch builds (CUDA, ROCm, MPS), install PyTorch first from pytorch.org, then install the extra:

pip install 'rddac[torch]'

Download the dataset

# Small sample bundle (~174 MB): manifest, CSV, and one experiment per category.
rddac download --small -y

# Full release (~87 GB), including the matching DDACS simulations (~9 GB).
rddac download

# Real measurements only (skip the simulations).
rddac download --no-sim

Basic usage

import rddac

with rddac.open_h5(0) as f:                    # one experiment by id
    force = f["force/data"][:]                 # (n, 8): time, load cells, temp, position, total force
    sheet = f["sheet_thickness/data"][:]       # (n, 2): sensor position, thickness
    z10   = f["pointcloud/op10/z"][:]          # (6400000,) flat scan buffer

The public surface mirrors the ddacs package one to one — load, add_view, open_h5, inspect_h5, streaming.iter_view / export_to_numpy / load_export, and the PyTorch IterableDataset share names, signatures, and semantics. Code written against DDACS ports by swapping the import:

# import ddacs as dataset_pkg                  # simulations
import rddac as dataset_pkg                    # real experiments

ds = dataset_pkg.load(data_dir="./data")
for record in dataset_pkg.streaming.iter_view("force-curve", data_dir="./data", dataset=ds):
    ...

See the documentation for the dataset reference (parameter space, HDF5 structure, Croissant manifest) and step-by-step tutorials from a first plot to PyTorch training.

Citation

@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

The dataset on DaRUS is licensed under CC BY 4.0. The rddac software is licensed under the MIT License — see LICENSE.