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

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

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 beari...

Cem Sinanoğlu

[1] Şahin Yildirim,et al. Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system , 2004 .

[2] Talat Tevrüz,et al. Tribological behaviours of carbon filled polytetrafluoroethylene (PTFE) dry journal bearings , 1998 .

[3] Şahin Yildirim,et al. Analysis of pressure variations on journal bearing system using artificial neural network , 2004 .

[4] Şahin Yildirim,et al. Neural network analysis of leakage oil quantity in the design of partially hydrostatic slipper bearings , 2004 .

[5] Joseph Edward Shigley,et al. Mechanical engineering design , 1972 .

[6] Cem Si˙nanoğlu,et al. The analysis of the effects of surface texture on the capability of load carriage of journal bearings using neural network , 2005 .

[7] Talat Tevrüz,et al. Tribological behaviours of bronze-filled polytetrafluoroethylene dry journal bearings , 1999 .

[8] D. Lee,et al. Failure analysis of asbestos–phenolic composite journal bearing , 2004 .

[9] Fazıl Canbulut,et al. An Investigation on the Performance of Hydrostatic Pumps Using Artificial Neural Network , 2004 .

[10] B. M. Satish,et al. Graphite particles reinforced ZA-27 alloy composite materials for journal bearing applications , 1998 .

[11] F. Canbulut,et al. Design of an Artificial Neural Network for Analysis of Frictional Power Loss of Hydrostatic Slipper Bearings , 2004 .