ANN for tribological applications

Nasir, Touqeer and Salih, Nbhan D. and Hui, Liew T. and Wen, Chin Chee and Yousif, B. F. ORCID: https://orcid.org/0000-0003-3847-5469 (2010) ANN for tribological applications. In: 2009 ASME International Mechanical Engineering Congress and Exposition (IMECE 2009), 13-19 Nov 2009, Florida, United States.

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Abstract

The current work is an attempt to investigate the possibility of using artificial neural network (ANN) modelling as a tool for friction coefficient prediction. The ANN model was trained at various configurations with different functions of training to develop the optimal ANN model. The experimental data was obtained from previous works. The results revealed that single layered model has reasonable accuracy in prediction when trained with TrainLM function. The results were acceptable especially in predicting steady-state friction coefficient, which proved ANN technologys ability to predict the friction co-efficient.

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Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	Yes
Item Status:	Live Archive
Additional Information:	© 2010 by ASME. Paper no. IMECE2009-10161.
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013)
Date Deposited:	14 Jan 2021 04:33
Last Modified:	14 Jan 2021 04:39
Uncontrolled Keywords:	artificial neural network; experimental data; friction coefficients; layered model; reasonable accuracy; tribological applications; various configuration
Fields of Research (2008):	09 Engineering > 0913 Mechanical Engineering > 091309 Tribology 09 Engineering > 0913 Mechanical Engineering > 091307 Numerical Modelling and Mechanical Characterisation 09 Engineering > 0915 Interdisciplinary Engineering > 091502 Computational Heat Transfer
Fields of Research (2020):	40 ENGINEERING > 4017 Mechanical engineering > 401708 Tribology 40 ENGINEERING > 4017 Mechanical engineering > 401706 Numerical modelling and mechanical characterisation 40 ENGINEERING > 4012 Fluid mechanics and thermal engineering > 401204 Computational methods in fluid flow, heat and mass transfer (incl. computational fluid dynamics)
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI:	https://doi.org/10.1115/IMECE2009-10161
URI:	http://eprints.usq.edu.au/id/eprint/19246

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