Friction Coefficient Data and Prediction Using Decision Tree Model for Open-Cell AlSi10Mg-SiC Composites Under Dry-Sliding Condition

Description

The materials used in the data files are: open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with pore sizes in a range of 1000 – 1200 μm fabricated by liquid-state-processing. The materials were subjected to pin-on-disk experiments at dry-friction wear conditions and the results for the coefficient of friction (COF) are included in this repository. The materials are labeled with “E” designating the open-cell AlSi10Mg material and “SE” designating the open-cell AlSi10Mg-SiC composite. The data files are located in 3 folders. The first folder is named “RAW_data” in which the raw files (.dwf) from the pin-on-disk tests for both materials are present. The second folder is named “Analyzed_data” in which the analyzed files (.xlsx) from the pin-on-disk tests for both materials are present. The third folder is named “Python_code”. It contains a Python file (.py) with a Decision Tree Model that calculates and predicts the COF as a function of time from the pin-on-disk tests for both materials. In the folder, there are two subfolders. The first subfolder “Input” includes two subfolders named “E” and “SE” according to the labels of the materials with the files used for the prediction of the COF of both materials in the Python code. The second subfolder “Output” also has two subfolders in it named “E” and “SE”, in which the output of the Python code for each material is saved.

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