COF-RF-Tool: A Python software for predicting the coefficient of friction of open-cell AlSi10Mg-SiC composites using Random Forest model
Mihail Kolev
Abstract
COF-RF-Tool is a software that predicts the coefficient of friction (COF) of open-cell AlSi10Mg-SiC composite materials under dry sliding conditions employing a Random Forest model. The software addresses the research challenge of designing and optimizing materials that have low friction and high wear resistance, which are important for various engineering applications. The software has an impact on the field of tribology, as it can provide reliable predictions of the COF based on experimental data, using a machine learning technique that has high accuracy and generalization ability. The software can also plot the actual vs predicted COF as a function of sliding distance and save the performance metrics of the model in a txt file. The software is implemented in python and uses several packages for data manipulation, machine learning, and visualization. The software is available at https://github.com/mihail-15/COF-RF-Tool-.git under an MIT license.