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

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:

1. 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. 
2. Fabrication: Specimens were printed on the MMSL machine with two distinct photopolymers.
3. Testing: Tensile tests were performed on an Zwick Z050, the unfiltered results are available in this dataset. 
4. Prediction: Results of tensile tests were used to build models and predict the results of all 60 unique combinations.
5. 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