2026-07-22T03:36:37Z https://researchdata.tuwien.ac.at/oai2d

oai:researchdata.tuwien.ac.at:1x07g-g9j17 2026-06-18T10:37:59Z openaire_data

4.3 TUW.TUDATA 10.48436/1x07g-g9j17 oai:researchdata.tuwien.ac.at:1x07g-g9j17 Laa, Dominik Dominik Laa 0009-0007-9721-2446 TU Wien Geyer, Sebastian Sebastian Geyer 0009-0000-6882-4178 Hochschule Campus Wien Koch, Thomas Thomas Koch 0000-0003-2801-3113 TU Wien Stampfl, Jürgen Jürgen Stampfl 0000-0002-3626-5647 TU Wien TU Wien 2026 multi-material 3D printing vat photopolymerization stereolithography Cellular Solids Voxel-based Design Predictive Modeling 2026-06-17 2026-04-29 2026-06-18 eng 10.48436/2av64-bjr33 Creative Commons Attribution 4.0 International GNU General Public License v3.0 or later Context and methodology This dataset was developed within the field of Additive Manufacturing (AM), specifically focusing on Multi-Material Stereo Lithography (MMSL). It investigates the mechanical behavior of heterogeneous cellular structures fabricated using a modified MMSL platform. The dataset provides the experimental base for how different topologies and material distributions influence mechanical properties. The dataset was created through the following workflow: Design: 60 unique combinations where considered in the design space, 20 of them have been selected for the following training steps, 4 for the validation. The selection methodology is part of this dataset.  Fabrication: Specimens were printed on the MMSL machine with two distinct photopolymers. Testing: Tensile tests were performed on an Zwick Z050, the unfiltered results are available in this dataset.  Prediction: Results of tensile tests were used to build models and predict the results of all 60 unique combinations. Validation: Predictions were compared agains the later tested validation set.  Technical details The dataset is split into 4 sections: Boundary Represented Geometries (B-rep), Voxel Represented Geometries (V-rep), Results and R-Processing The B-rep geometries can be opened with any compatible software, as unit for the .STL files mm was utilized. The V-rep files can be opened layer per layer with any image viewer. Results are available for Microsoft Excel. The processing files are written in R and can be inspected by any text-editor or executed with R-Studio.  Using the R-Project Open the project in RStudio To modify the filter method or other basic properties, edit R_Setup.R Run Filter_Overview.qmd to apply the filtering logic and evaluate its effect Run Data_Overview.qmd to get an overview of the data after filtering Run any of the Analysis_Pipelines to run the respective machine learning method If R-Studio is unavailable, existing results can be reviewed by opening the .html files with any browser Further details If you use the dataset, please cite the original paper! Changelog Version 1: Initial Upload of the Dataset Version 2: Added nanoindendation results (Nanoindendation.xlsx) Added additional tenisile test results (Tensile_Tests_5.xlsx) Removed predictions and models, as they are dependent on the setup of the R-project (Model_Results_Detailed.xlsx, All_Predictions_Detailed.xlsx) Rewritten the complete R-Processing: Includs different filtering methods and statistical model methods now, split overall several files  Updated this description with information how the R-Project can be used and added the changelog