BaumSebastian/rddac-teaser

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:

1. Getting started — install, inspect one experiment, 3D point cloud plot.
2. Build your own view — rddac.add_view, manifest inspection, custom RecordSets.
3. PyTorch training — RDDACDataset, filters, train/val/test splits.
4. Visualization — scans, point clouds, force curves, traverses.
5. Loose HDF5 recipe — pandas + h5py after --extract --remove-zip.
6. 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.