--- 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.