README.md

---
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

[![Code License: MIT](https://img.shields.io/badge/Code-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Dataset License: CC BY 4.0](https://img.shields.io/badge/Dataset-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![Documentation](https://img.shields.io/badge/docs-readthedocs.io-blue.svg)](https://rddac.readthedocs.io) [![DaRUS Repository](https://img.shields.io/badge/repository-DaRUS-green.svg)](https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/DARUS-5589) [![DOI](https://img.shields.io/badge/DOI-10.18419%2FDARUS--5589-blue.svg)](https://doi.org/10.18419/DARUS-5589)

![Measured point clouds after OP10 and OP20, colored by deviation from the matching DDACS simulation](https://raw.githubusercontent.com/BaumSebastian/RDDAC/main/docs/images/sim2real_sweep.gif)

*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](https://ddacs.readthedocs.io) 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](https://rddac.readthedocs.io)** · **[Dataset DOI](https://doi.org/10.18419/DARUS-5589)** · **[Paper](https://doi.org/10.1007/s12666-026-03870-5)**

## 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](https://mlcommons.org/croissant/) 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](https://doi.org/10.18419/DARUS-5589)).

## Installation

```bash
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.

```python
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.

```python
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](https://rddac.readthedocs.io/en/latest/tutorials/):

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](https://ddacs.readthedocs.io)
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:

```bash
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:

```bibtex
@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.