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A Neural Model of Friction Material Behaviour
Technical Paper
2006-01-3200
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The neural computation ability to model complex non-linear relationships directly from experimental data, without any prior assumptions about nature of the input/output relationships, has been used in this paper. An artificial neural network technique was used to develop a neural model for predicting the friction materials behavior under prescribed testing conditions. By means of neural modeling of the friction materials behavior, the relationship between 26 input parameters and one output parameter (brake factor C) has been established. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). Prediction abilities of the neural model have been evaluated by comparison the real cold performance obtained during friction material testing on the single end full-scale inertia dynamometer and predicted ones.
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Citation
Aleksendric, D. and Duboka, C., "A Neural Model of Friction Material Behaviour," SAE Technical Paper 2006-01-3200, 2006, https://doi.org/10.4271/2006-01-3200.Also In
References
- Jang H. Lee J.S. Fash J.W. Compositional effects of the brake friction material on creep groan phenomena Wear 251 2001 1477 1483
- Jang H. Ko K. Kim S.J. Basch R.H. Fash J.W. The effect of metal fibers on the friction performance of automotive brake friction materials Wear 256 2004 406 414
- Österle. W. Griepentrog M. Gross T. Urban I. Chemical and microstructural changes induced by friction and wear of brakes Wear 251 2001 1469 1476
- Aleksendrić D. Artificial technologies in the design of braking systems Innovative Automotive Technology - IAT'05 Bled 21st-22st April 2005 Slovenia 41 48
- Aleksendrić D. Friction material development using artificial intelligence EAEC05-AD09, 10th EAEC European Automotive Congress 30th May 1st June Belgrade, Serbia & Montenegro 2005
- Issa R.A. Fletcher D. Neural networks in engineering applications Proceedings of the 29th Annual Conference Colorado State University Colorado April 15-17 1993 177 186
- Zhang Z. Friedrich K. Velten K Prediction on tribological properties of short fibre composites using artificial neural networks Wear 252 2002 668 675
- Devabhaktuni V.K. Yagoub M.C.E. Fang Y. Xu J. Zhang Q.J. Neural Networks for Microwave Modelling Model Development Issues and Nonlinear Modelling Techniques John Wiley & Sons, Inc. 2001
- Duboka C. Todorovic J. Arsenic Z. Application of an inertia dynamometer to check braking performance against theoretical predictions 6th International Heavy Vehicles Seminar Christchurch July New Zealand 1996
- Krose, Van der Samgt P. An introduction to neural networks University of Amsterdam 1996