Francesco Lenci
Machine Learning model for tribological data extraction from experimental tests.
Rel. Luigi Mazza, Achill Holzer. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022
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Abstract
Nowadays, increasing demand for performances and new environmental requirements have led to the need for new materials and lubricants in the fluid power field. The thesis is based on research carried out in the offices at the IFAS institute. Based on existing measurement data of a disc-on-disc tribometer, the shapes of the Stribeck curves were analysed. Furthermore, the essential characteristics of the curves were extracted, such as the minimum and maximum coefficient of friction, the speed at the curve's minimum point, the number of peaks, and the slopes of the curve. Some of the parameters and the experimental setup have the purpose of feeding a machine learning model.
After tuning and comparing different models, the Random Forest regression model was chosen
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