Code, quick-start examples and full documentation: <a href="https://github.com/BaumSebastian/RDDAC" target="_blank" rel="noopener">https://github.com/BaumSebastian/RDDAC</a>.<br><br>The Real Deep Drawing and Cutting (RDDAC) Dataset is the experimental counterpart to the <a href="https://doi.org/10.18419/DARUS-4801">DDACS</a> 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.<br><br>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).<br><br>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.