Real Deep Drawing and Cutting (RDDAC) Dataset

Version 1.0
Real Deep Drawing and Cutting (RDDAC) Dataset
Baum, Sebastian; Heinzelmann, Pascal, 2026, "Real Deep Drawing and Cutting (RDDAC) Dataset", https://doi.org/10.18419/DARUS-5589, DaRUS, V1, UNF:6:z1OSuc7/svv2hP/9IbFI8w== [fileUNF]
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Subtitle Real measurements of a real deep drawing and cutting experiment with multiple modalities
Description
Code, quick-start examples and full documentation: https://github.com/BaumSebastian/RDDAC.

The Real Deep Drawing and Cutting (RDDAC) Dataset is the experimental counterpart to the DDACS simulation dataset. It contains physical measurements from a deep drawing and cutting process for DP600 sheet metal, captured on modified quadratic cups, and is intended for studying the deviation between finite element simulation and physical reality.

The dataset comprises approximately 9000 experiments spanning two base geometries (concave, convex) crossed with three blankholder forces (100, 300, 500 kN) and three lubrication patterns (coarse, medium, fine), giving 18 categories with up to 500 repetitions each. Each experiment is stored as a HDF5 file and captures, where available: press force and process signals (load cells and temperature); a sheet thickness traverse; an oil film traverse; and 3D laser scans (height and luminescence) of the part after deep drawing (OP10) and after cutting (OP20).

Each HDF5 file carries named scalar root attributes matching the columns of process_parameters.csv (process_parameters.tab on the DaRUS UI), the central index that maps every experiment to its parameters, data availability flags, and a recommended ML train, validation, and test split. A sample.zip (one experiment per category, 18 files in total) is provided for fast preview without downloading the full dataset. A Croissant 1.1 manifest (metadata.json) describes every field for direct use with mlcroissant. The accompanying Python package includes an optional preprocessing step that reconstructs cleaned 3D point clouds and aligns them to the matching DDACS simulations.
Subject Computer and Information Science; Engineering
Keyword Deep Drawing, Sheet Metal Forming, Machine Learning, Point Cloud, Experimental Measurement, DP600, Springback, Sim-to-Real
Related Publication Is Cited By: Baum, S., Heinzelmann, P., Clauß, P. et al. Statistical Analysis of Simulation-to-Reality Deviation in Deep Drawing with a Benchmark Dataset. Trans Indian Inst Met 79, 176 (2026).doi: 10.1007/s12666-026-03870-5
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CC BY 4.0

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metadata.json
JSON - 20.9 KB
Published Jul 13, 2026
87 Downloads
MD5: cbfbbf82f448aefbcda8cbfcca599b63
Croissant 1.1 metadata manifest. Describes every HDF5 field, the CSV columns, and use-case RecordSets (point cloud, force curve, thickness). Consumable by mlcroissant and ML-dataset tooling.
Metadata
process_parameters.csv
Tab-Delimited - 436.5 KB
Published Jul 13, 2026
86 Downloads
MD5: b78905067334bcd6860360b5869a85ac
Tabular Data: 10 Variables, 9000 Observations UNF:6:z1OSuc7/svv2hP/9IbFI8w==
Per-experiment parameter table mapping every HDF5 file to its geometry, process parameters, recommended train/test/val split, and data availability flags.
Data
Preview "process_parameters.tab"
rddac_documentation.pdf
Adobe PDF - 669.4 KB
Published Jul 13, 2026
28 Downloads
MD5: f5baa70420d7062c4d01f107c8d0ac19
One-page documentation: dataset overview, citation, and pointers to the GitHub repo for quick-start, schema reference and examples. https://github.com/BaumSebastian/RDDAC
Documentation
Preview "rddac_documentation.pdf"
concave.zip
h5/ZIP Archive - 42.1 GB
Published Jul 13, 2026
62 Downloads
MD5: 853540085b144d6e7ece154a9e204a19
Raw experimental measurements for the concave geometry (4491 experiments). One HDF5 file per experiment. The 9 concave experiments shown in sample.zip are not repeated here.
Data
Preview "h5/concave.zip"
convex.zip
h5/ZIP Archive - 38.7 GB
Published Jul 13, 2026
102 Downloads
MD5: 30cb9044460449f5f93b4ce5eb60f552
Raw experimental measurements for the convex geometry (4491 experiments). One HDF5 file per experiment. The 9 convex experiments shown in sample.zip are not repeated here.
Data
Preview "h5/convex.zip"
sample.zip
h5/ZIP Archive - 165.7 MB
Published Jul 13, 2026
86 Downloads
MD5: 4b61770b34f0517641df613e00a15e56
Small sample (one experiment per category, 18 files) for quick tryout and testing without downloading the full dataset. These experiments are not repeated in concave.zip / convex.zip.
Data
Preview "h5/sample.zip"
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Curation Status History
StatusDateAssigner
2026-07-13 07:41:19Sebastian Baum
Waiting for Feedback from Author2026-07-09 15:35:23Dataverse Admin
In Review by FoKUS2026-07-07 07:27:06Dataverse Admin
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