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Artificial Neural Network Prediction of Fretting Wear Behavior of Structural Steel, En 24 Against Bearing Steel, En 31

Artificial Neural Network Prediction of Fretting Wear Behavior of Structural Steel, En 24 Against... In this study, artificial neural network (ANN) technique is used to predict the friction and wear behavior of various surface-treated structural steel (En 24) fretted against bearing steel (En 31). A three-layer neural network with a back propagation algorithm is used to train the network. Fretting wear volume and coefficient of friction obtained at different normal loads (ranging between 2.4 and 29.4 N) for various treated samples (hardened, thermo-chemically treated, MoS2 coated) were used in the formation of training data of ANN. Results of the predictions of ANN are in good agreement with the experimental results. The degree of accuracy of predictions was 96.3 and 95.7% for fretting friction coefficient and wear, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Materials Engineering and Performance Springer Journals

Artificial Neural Network Prediction of Fretting Wear Behavior of Structural Steel, En 24 Against Bearing Steel, En 31

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References (14)

Publisher
Springer Journals
Copyright
Copyright © ASM International 2007
Subject
Materials Science; Characterization and Evaluation of Materials; Tribology, Corrosion and Coatings; Quality Control, Reliability, Safety and Risk; Engineering Design
ISSN
1059-9495
eISSN
1544-1024
DOI
10.1007/s11665-007-9100-9
Publisher site
See Article on Publisher Site

Abstract

In this study, artificial neural network (ANN) technique is used to predict the friction and wear behavior of various surface-treated structural steel (En 24) fretted against bearing steel (En 31). A three-layer neural network with a back propagation algorithm is used to train the network. Fretting wear volume and coefficient of friction obtained at different normal loads (ranging between 2.4 and 29.4 N) for various treated samples (hardened, thermo-chemically treated, MoS2 coated) were used in the formation of training data of ANN. Results of the predictions of ANN are in good agreement with the experimental results. The degree of accuracy of predictions was 96.3 and 95.7% for fretting friction coefficient and wear, respectively.

Journal

Journal of Materials Engineering and Performance – Springer Journals

Published: Dec 1, 2007

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