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Prediction of Wear Behavior of Aluminum Alloy Reinforced with Carbon Nanotubes Using Nonlinear Identification
Technical Paper
2014-01-0947
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Aluminum metal matrix composites reinforced with particulates have attracted much attention in the automotive industry, due to their improved wear resistance in comparison to aluminum alloys, in recent years.
The wear behavior is the critical factor influencing the product life and performance in engineering components. Carbon nanotubes (CNT) are one of the most promising candidates of reinforcements used to improve mechanical strength such as wear in metal matrix composites (MMCs). However, in industrial applications, wear tests are relatively expensive and prolonged. As a result, for several years, research has been increasingly concentrated on development of wear prediction models. In this study, prediction of wear behavior of aluminum (Al) matrix (MMCs) reinforced with different amounts (0, 0.5, 1 and 2 wt%) of CNTs was investigated.
A nonlinear autoregressive exogenous (NARX) model structure was chosen for the modeling. The wear load was considered as the input parameter, whereas the wear rate and friction coefficient as the output parameters. Simulations using the identified models were compared with experimental results and it was found that the modeling of wear process was satisfactory.
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Istif, I., Isın, O., Uzunsoy, D., Peng, T. et al., "Prediction of Wear Behavior of Aluminum Alloy Reinforced with Carbon Nanotubes Using Nonlinear Identification," SAE Technical Paper 2014-01-0947, 2014, https://doi.org/10.4271/2014-01-0947.Also In
References
- Kuzumaki , T. et al. Processing of carbon nanotube reinforced aluminium composite Journal of Materials Research 13 9 2445 2449 1998
- George , R. et al. Strengthening in carbon nanotube/aluminium (CNT/Al) composites Scripta Materialia 53 10 1159 1163 2005
- Esawi , A.M.K. et al. Fabrication and properties of dispersed carbon nanotube-aluminium composites Materials Science and Engineering a-Structural Materials Properties Microstructure and Processing 508 1-2 167 173 2009
- He , C.N. et al. An approach to obtaining homogeneously dispersed carbon nanotubes in Al powders for preparing reinforced Al-matrix composites Advanced Materials 19 8 1128 1132 2007
- Deng , C.F. et al. Preparation and characterization of carbon nanotubes/aluminium matrix composites Materials Letters 61 8-9 1725 1728 2007
- Gopalakrishnan K. , Mudgal A. , Hallmark S. Neuro-Fuzzy Approach to Predictive Modeling of Emissions from Biodiesel Powered Transit Buses Transport 26 4 344 352 2011
- Zhang Q. , Tian D. Study Of Cws/Diesel Dual Fuel Engine Emissions By Means of Rbf Neural Network Chengdu, China, 5449248 2010 10.1109/APPEEC
- Obodeh O. , Ajuva C.I. Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine NOX Emissions Eur. J. Sci. Res. 33 4 642 653 2009
- Karri V. , Ho T.N. Predictive Models for Emission of Hydrogen Powered Car Using Various Artificial Intelligent Tools Neural Comput. Appl. 18 469 476 2009 10.1007/s00521-008-0218-y
- Ghobadian B. , Rahimi H. , Nikbakht A.M. , Najafi G. , Yusaf T.F. Diesel Engine Performance and Exhaust Emission Analysis Using Waste Cooking Biodiesel Fuel With An Artificial Neural Network Renew. Energ. 34 4 976 982 2009 10.1016/j.renene.2008.08.008
- Yusaf T.F. , Buttsworth D.R. , Saleh K.H. , Yousif B.F. CNG-Diesel Engine Performance and Exhaust Emission Analysis with the Aid of Artificial Neural Network Appl. Energ. 87 1661 1669 2010 10.1177/0957650911402546
- Ramesh C.S. , Khan A.R. , Ravikumar N. , Savanprabhu P. Prediction of Wear Coefficient of Al6061-TiO 2 Composites Wear 259 602 608 2005
- Aleksendric D. Neural Network Prediction of Brake Friction Materials Wear Wear 268 117 125 2010
- Ljung L. System Identification - Theory for the User 2nd Upper Saddle River PTR Prentice Hall 1999
- Matlab R2011b Documentation http://www.mathworks.com/help/toolbox/ident/uq/bq5o xw-1.html 2011
- Liao J. , Tan M.J. Mixing of carbon nanotubes and aluminum powder for powder metallurgy use Powder Technology 208 42 48 2011