A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing-Reference-Cited by-同舟云学术

A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing

Published:2006-03-01 Issue:2 Volume:58 Page:95-109
ISSN:0036-8792
Container-title:Industrial Lubrication and Tribology
language:en
Short-container-title:

Author:

Sinanoğlu Cem

Abstract

PurposeTo discuss the effects of metal matrix composite (MMC) journal structure on the pressure distribution and, consequently, on the load‐carrying capacity of the bearing are predicted using feed forward architecture of neurons.Design/methodology/approachThe inputs to the networks are the collection of experimental data. These data are used to train the network using the Batch Back‐prop, Online Back‐prop and Quickprop algorithms.FindingsThe neural network (NN) model outperforms the available experimental model in predicting the pressure as well as the load‐carrying capacity.Research limitations/implicationsThe experiment specimens used in this study have been made of MMC with aluminum based reinforced with SiC ceramic particles, using the stir casting technique. Various composite journal structures can be investigated.Practical implicationsThe simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modelling journal bearing system.Originality/valueThis paper discusses a new modelling scheme known as artificial NNs. An experimental and a NN approach have been employed for analysing MMC journal structure for hydrodynamic journal bearings and their effects on the system performance.

Publisher

Emerald

Subject

Surfaces, Coatings and Films,General Energy,Mechanical Engineering

Reference11 articles.

1. Canbulut, F. and Sinanoğlu, C. (2004), “An investigation on the performance of hydrostatic pumps using artificial neural network”, JSME International Journal Series C, Vol. 47, pp. 864‐72.

2. Canbulut, F., Sinanoğlu, C. and Yıldırım, Ş. (2004a), “Neural network analysis of leakage oil quantity in the design of partially hydrostatic slipper bearings”, Industrial Lubrication and Tribology, Vol. 56 No. 4, pp. 231‐43.

3. Canbulut, F., Sinanoğlu, C. and Yıldırım, Ş. (2004b), “Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system”, KSME International Journal, Vol. 18 No. 3, pp. 432‐42.

4. Canbulut, F., Yıldırım, Ş. and Sinanoğlu, C. (2004c), “Design of an artificial neural network for analysis of frictional power loss of hydrostatic slipper bearings”, STLE Tribology Letters, Vol. 17, pp. 887‐99.

5. Lee, D.G. and Kim, S.S. (2004), “Failure analysis of asbestos – phenolic composite journal bearing”, Composite Structures, Vol. 65, pp. 37‐46.

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