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

Abstract

Purpose

To 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/approach

The 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.

Findings

The neural network (NN) model outperforms the available experimental model in predicting the pressure as well as the load‐carrying capacity.

Research limitations/implications

The 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 implications

The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modelling journal bearing system.

Originality/value

This 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.

Citation

Sinanoğlu, C. (2006), "A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing", Industrial Lubrication and Tribology, Vol. 58 No. 2, pp. 95-109. https://doi.org/10.1108/00368790610651512