Authors:
Laura Haviez
1
;
Rosario Toscano
2
;
Siegfried Fourvy
2
and
Ghislain Yantio
3
Affiliations:
1
LTDS and SAGEM, France
;
2
LTDS, France
;
3
SAGEM, France
Keyword(s):
Fretting Wear Modeling, Artificial Intelligence, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Materials wear is a very complex, only partially-formalized phenomenon involving numerous parameters
and damage mechanisms. The need to characterize wear in many industrial applications prompted the
present research. The study concerns an original strategy investigating the effect of contact conditions on
the wear behavior of carburized stainless steels under fretting and reciprocating sliding motion. A physical
model was constructed, and pre-treated experimental data were incorporated in a neural network to model
wear volume. Three models are proposed and compared, according to input.