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Data · dataset · 2026

Research Data for "Topological Influence on the Tensile Performance of Multi-Material SLA 3D Printed Cellular Structures"

Listed in TU Wien Research Data

Context and methodology This dataset was developed within the field of Additive Manufacturing (AM), specifically focusing on Multi-Material Stereo Lithography (MMSL).

Description

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.

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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

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Where it is published

Catalogue records · 1

Topics

From keywords
Life Sciences
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Image 75%
Provenance · 1 source records, 14 field assertions
SourceKeyLast seenRaw
TU Wien Research Data1x07g-g9j1713 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[modality].local:modality:imageenrichment · researchdata tuwien ac atkeyword-concept-rules@1.0.0title+description (75%)
concepts[topic].invenio_subject:researchdata_tuwien_ac_at:cellular-solidssource · researchdata tuwien ac atconnector:researchdata_tuwien_ac_at@1.0.0
concepts[topic].invenio_subject:researchdata_tuwien_ac_at:multi-material-3d-printingsource · researchdata tuwien ac atconnector:researchdata_tuwien_ac_at@1.0.0
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concepts[topic].invenio_subject:researchdata_tuwien_ac_at:stereolithographysource · researchdata tuwien ac atconnector:researchdata_tuwien_ac_at@1.0.0
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