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AdaBoost_wear: Adaboost model-based Python software for predicting the coefficient of friction of babbitt alloy

Mihail Kolev

Abstract


AdaBoost_wear is a Python software that implements the AdaBoost algorithm to predict the coefficient of friction (COF) of B83 babbitt alloy as a function of time. The software uses data from pin-on-disk tests with different loads to train and test the model. The software also provides performance metrics, such as R2 score, mean squared error, and mean absolute error, to evaluate the accuracy of the predictions. The software also generates plots of the actual and predicted COF values, as well as histograms and boxplots of the COF distribution. The software is open source and released under the MIT license.


Keywords


machine learning; adaboost; coefficient of friction; babbitt alloy; python

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References


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DOI: https://doi.org/10.32629/jai.v7i4.1206

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Copyright (c) 2024 Mihail Kolev

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