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metadata 
license: cc-by-4.0
language:
  - en
pretty_name: DDACS — Deep Drawing and Cutting Simulations (Teaser)
size_categories:
  - n<1K
tags:
  - croissant
  - surrogate-model
  - surrogate-modeling
  - scientific-machine-learning
  - sciml
  - physics-simulation
  - simulation
  - finite-element-analysis
  - finite-element-method
  - fem
  - fea
  - computational-mechanics
  - solid-mechanics
  - sheet-metal-forming
  - metal-forming
  - deep-drawing
  - stamping
  - springback
  - forming-limit
  - manufacturing
  - materials-science
  - digital-twin
  - mesh
  - point-cloud
  - graph-neural-network
  - gnn
  - regression
  - benchmark
  - engineering
  - automotive
  - hdf5
task_categories:
  - graph-ml
  - tabular-regression
  - other

DDACS — Deep Drawing and Cutting Simulations Dataset

License: MIT Python 3.10+ Documentation DaRUS Repository DOI Paper

Simulation overview

Simulation with the tool geometries showing sheet metal thinning, stress, and strain.

A large-scale dataset and benchmark for training AI models that replace computationally expensive FEA simulations in industrial sheet metal manufacturing. Each simulation models a two-stage stamping process (deep drawing in OP10 and trimming with elastic recovery in OP20) for a cup geometry parameterised by 8 input dimensions. Train ML surrogates that predict mesh deformation, stress, strain, and springback in seconds instead of the minutes-to-hours a CAE solver would take.

Simulations 32,466
Total size ~640 GB (HDF5, lossless)
Process steps per sim 2 (OP10 deep drawing, OP20 trimming)
Input parameters 8 (4 geometric + 4 process)
Train / val / test 25,973 / 3,246 / 3,247 (predefined)
Mesh-node states ~2.1 B across all sims, timesteps, components

Documentation · Dataset DOI · Paper

About this sample

This is a 22 MB teaser of DDACS — one full simulation 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    8 input parameters for all 32,466 simulations
  h5/258864.zip             one full simulation (OP10 + OP20, all components)
ddacs_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 ddacs.load() and any Croissant-aware tool consume. It is the same manifest published with the full dataset on DaRUS (doi:10.18419/DARUS-4801).

Installation 

pip install ddacs          # add the PyTorch adapter with: pip install 'ddacs[torch]'

Basic usage

ddacs.load parses the Croissant manifest; ddacs.open_h5 opens a single simulation in memory and returns an h5py.File.

import ddacs

# Load the dataset manifest bundled with this sample.
ds = ddacs.load(data_dir="data")
print([rs.id for rs in ds.metadata.record_sets])

# Open the included simulation. OP10 carries the blank and the three tools.
with ddacs.open_h5(258864, data_dir="data") as f:
    blank_thickness = f["OP10/blank/element_shell_thickness"][-1]
    print("final-timestep thickness:", blank_thickness.shape)

PyTorch integration

DDACSDataset is a torch.utils.data.IterableDataset over a Croissant view. It auto-shards across DataLoader workers and DDP ranks, and silently skips simulations whose zip is missing — so partial downloads (like this teaser) stream cleanly.

from ddacs.pytorch import DDACSDataset
from torch.utils.data import DataLoader

ds = DDACSDataset(view="springback-minimal", data_dir="data")
for batch in DataLoader(ds, batch_size=1, num_workers=0):
    forming    = batch["op10_blank_node_displacement_forming"]
    springback = batch["op10_blank_node_displacement_springback"]
    break

Tutorials

Six end-to-end notebooks ship in notebooks/ and are published on Read the Docs:

  1. Getting started — install, load, first plot.
  2. Build your own view — ddacs.add_view, manifest inspection, SIM-KAx provenance.
  3. PyTorch training — DDACSDataset, filters, train/val/test splits.
  4. Visualization — thickness, components, springback, vectors.
  5. Loose HDF5 recipe — pandas + h5py after --extract --remove-zip.
  6. Streaming & numpy export — iter_view, export_to_numpy, ~1000× speedup.

Version compatibility

The ddacs package major version tracks the DaRUS dataset major version, enforced by the bundled Croissant manifest.

Package DaRUS dataset
ddacs 3.x v3.0 and any future v3.x updates (current)
ddacs 2.x v1.0 and v2.0

Pin the major to the dataset you target, e.g. pip install 'ddacs~=3.0'.

⬇️ Get the full dataset

This sample contains a single simulation. The complete DDACS dataset — 32,466 simulations, ~640 GB of lossless HDF5, with the predefined 25,973 / 3,246 / 3,247 train/val/test split — is hosted on DaRUS with a citable DOI:

➡️ https://doi.org/10.18419/DARUS-4801

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 ddacs
ddacs download            # full release  (ddacs download --small for this 22 MB sample)

Citation

If you use this dataset or code in your research, please cite both the dataset and the paper:

@dataset{baum2025ddacs,
  title={Deep Drawing and Cutting Simulations Dataset},
  subtitle={FEM Simulations of a deep drawn and cut dual phase steel part},
  author={Baum, Sebastian and Heinzelmann, Pascal},
  year={2025}, version={3.0}, publisher={DaRUS},
  doi={10.18419/DARUS-4801}, license={CC BY 4.0},
  url={https://doi.org/10.18419/DARUS-4801}
}

@article{heinzelmann2025benchmark,
  title={A Comprehensive Benchmark Dataset for Sheet Metal Forming: Advancing
         Machine Learning and Surrogate Modelling in Process Simulations},
  author={Heinzelmann, Pascal and Baum, Sebastian and Riedmueller, Kim Rouven
          and Liewald, Mathias and Weyrich, Michael},
  journal={MATEC Web of Conferences}, volume={408}, year={2025}, pages={01090},
  doi={10.1051/matecconf/202540801090},
  url={https://www.matec-conferences.org/articles/matecconf/abs/2025/02/matecconf_iddrg2025_01090/matecconf_iddrg2025_01090.html}
}

License

Data: CC BY 4.0. Package code: MIT.