Artificial neural network-based prediction technique for wear loss quantities in Mo coatings
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Date
2006
Authors
Hakan Çetinel
Hasan Öztürk
Erdal Çelik
Bekir Karlik
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Science SA
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
Mo coated materials are used in automotive aerospace pulp and paper industries in order to protect machine parts against wear and corrosion. In this study the wear amounts of Mo coatings deposited on ductile iron substrates using an atmospheric plasma-spray system were investigated for different loads and environment conditions. The Mo coatings were subjected to sliding wear against AISI 303 counter bodies under dry and acid environments. In a theoretical study cross-sectional microhardness from the surface of the coatings loads environment and friction test durations were chosen as variable parameters in order to determine the amount of wear loss. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results is reasonably good. © 2006 Elsevier B.V. All rights reserved. © 2008 Elsevier B.V. All rights reserved.
Description
Keywords
Artificial Neural Networks, Mo Coatings, Plasma Spray, Wear, Friction, Mathematical Models, Molybdenum, Neural Networks, Plasma Spraying, Wear Of Materials, Mo Coatings, Neural Network Model, Sprayed Coatings, Friction, Mathematical models, Molybdenum, Neural networks, Plasma spraying, Wear of materials, Mo coatings, Neural network model, Sprayed coatings, Wear, Artificial Neural Networks, Plasma Spray, Mo Coatings
Fields of Science
0203 mechanical engineering, 02 engineering and technology, 0210 nano-technology
Citation
WoS Q
Scopus Q

OpenCitations Citation Count
68
Source
Wear
Volume
261
Issue
10
Start Page
1064
End Page
1068
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Citations
CrossRef : 34
Scopus : 78
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Mendeley Readers : 46
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