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
|
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
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. Finally, predictions with new materials and lubricants were performed. In parallel, some significant comparisons between materials and lubricants were investigated. The latter does not need to create new specimens for the test bench, significantly saving time and money. Further research, increasing the sample size, is needed to improve the method and to get more accurate predictions. |
---|---|
Relators: | Luigi Mazza, Achill Holzer |
Academic year: | 2022/23 |
Publication type: | Electronic |
Number of Pages: | 140 |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica) |
Classe di laurea: | New organization > Master science > LM-25 - AUTOMATION ENGINEERING |
Ente in cotutela: | ifas - Institute for Fluid Power Drives and Systems (GERMANIA) |
Aziende collaboratrici: | Aachen University RWTH |
URI: | http://webthesis.biblio.polito.it/id/eprint/24645 |
Modify record (reserved for operators) |