Neural Network for Fretting Wear Modeling

Laura Haviez, Rosario Toscano, Siegfried Fourvy, Ghislain Yantio

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.

References

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Paper Citation


in Harvard Style

Haviez L., Toscano R., Fourvy S. and Yantio G. (2014). Neural Network for Fretting Wear Modeling . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 617-621. DOI: 10.5220/0004908506170621


in Bibtex Style

@conference{icaart14,
author={Laura Haviez and Rosario Toscano and Siegfried Fourvy and Ghislain Yantio},
title={Neural Network for Fretting Wear Modeling},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={617-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004908506170621},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Neural Network for Fretting Wear Modeling
SN - 978-989-758-015-4
AU - Haviez L.
AU - Toscano R.
AU - Fourvy S.
AU - Yantio G.
PY - 2014
SP - 617
EP - 621
DO - 10.5220/0004908506170621