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Thesis Defenses
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PhD
QUANTRI project: Alizée Bouchot-Madrignac (LaMCoS) defended her thesis in Lyon
On The July 13, 2023
Alizée Bouchot-Madrignac, PhD student at the LaMCoS laboratory, sucessfully defended her thesis.
Alizée defended her thesis on July 11th. The jury awarded her her doctorate for her work on the QUANTRI research project (Quantitative identification of physical characteristics of solid interfaces correlated with the tribological properties).
Alizée Bouchot-Madrignac received her PhD from the Institut National des Sciences Appliquées de Lyon, a member of the Université de Lyon. She presented an original work highlighting the contribution of machine learning to tribology. She developed a relevant predictive method based on image processing of wear debris. In view of the work presented, the jury recommended her for the INSA Lyon thesis prize.
ABOUT THE QUANTRI PROJECT
The project is motivated by the current lack of experimental capabilities to access to direct measurements of the solid third body (i.e. tribological interface) rheology. The work follows five steps:
Alizée and her team published an article in the Journal of Electronic Imaging:
Alizée Bouchot-Madrignac received her PhD from the Institut National des Sciences Appliquées de Lyon, a member of the Université de Lyon. She presented an original work highlighting the contribution of machine learning to tribology. She developed a relevant predictive method based on image processing of wear debris. In view of the work presented, the jury recommended her for the INSA Lyon thesis prize.
ABOUT THE QUANTRI PROJECT
The project is motivated by the current lack of experimental capabilities to access to direct measurements of the solid third body (i.e. tribological interface) rheology. The work follows five steps:
- Generation of various third body function of contact conditions coupled with rheological measurements (friction, flows);
- Imaging of surface morphology & third body particles, with Scanning Electron Microscopy;
- Image processing with different strategies;
- Image analysis to extract quantitative descriptors (characteristics of particles and surface features). “Classical” geometrical descriptors (e.g. perimeter, circularity, elongation), but also more complex ones (e.g. contrast, homogeneity, entropy…), are studied in order to consider the whole diversity of third body particles layout and features;
- Finally, an integration of relevant descriptors in machine learning algorithms will allow a better understanding of the mechanisms involved in dry contacts.
Alizée and her team published an article in the Journal of Electronic Imaging:
- Alizée Bouchot, Amandine Ferrieux-Paquet, Guilhem Mollon, Sylvie Descartes, and Johan Debayle "Segmentation and morphological analysis of wear track/particles images using machine learning," Journal of Electronic Imaging 31(5), 051605 (8 July 2022).
https://doi.org/10.1117/1.JEI.31.5.051605
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