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The purpose of this paper is to investigate pressure distribution of the journal bearings with aluminium shafts with varying surface porosity in varying revolutions using experimental and neural network approach.

The collected experimental data such as pressure variations is employed as training and testing data for an artificial neural network (ANN). Back propagation algorithm is used to update the weight of the network during the training.

Neural network predictor has superior performance for modelling journal bearing systems with shafts of different surface porosities.

Back propagation algorithm is used training algorithm for proposed neural networks. Various training algorithms can be used to train proposed network. The spectrum of the journal surface porosity can be enlarged.

From the experimental and simulation results, neural network exactly follows the experimental results. Because of that, this kind of neural network predictors can be applied on journal bearing systems in practice applications.

This paper discusses a new modelling scheme known as ANNs. A neural network predictor has been employed to analyze of the effects of shaft surface porosity in hydrodynamic lubrication of journal bearing.

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing optimum assembly sequence for assembly systems.

The input to the assembly system is the assembly's connection graph that represents parts and relations between these parts. The output to the system is the optimum assembly sequence. In the constitution of assembly's connection graph, a different approach employing contact matrices and Boolean operators has been used. Moreover, the neural network approach is used in the determination of optimum assembly sequence. The inputs to the networks are the collection of assembly sequence data. This data is used to train the network using the back propagation (BP) algorithm.

The proposed neural network model outperforms the available assembly sequence‐planning model in predicting the optimum assembly sequence for mechanical parts. Due to the parallel structure and fast learning of neural network, this kind of algorithm will be utilized to model another types of assembly systems.

In the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.

The simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.

This paper discusses a new modelling scheme known as artificial neural networks. The neural network approach has been employed for analyzing feasible assembly sequences and optimum assembly sequence for assembly systems.

This paper presents an investigation for analysing the load carrying capacity of journal bearing in a variety of conditions using a proposed neural network (NN). The NN structure is very suitable for this kind of system. The network is capable of predicting the pressures of the experimental system. The network has parallel structure and fast learning capacity. It can be outlined from the results for both approaches, NN could be used to model journal bearing systems in real time applications.

The purpose of this paper is to study the effects of shaft surface profiles on the load carriage capacity of journal bearings using an experimental and neural network approach. The paper aims to inspect the performance characteristics of journal bearing systems; the presence of transverse and longitudinal roughness on journal‐shaft surfaces is studied using the proposed neural network.

The collected experimental data such as pressure variations are employed as training and testing data for an artificial neural network (ANN). Quick propagation algorithm is used to update the weight of the network during the training.

As a result, a shaft with a transverse profile displays a favorable performance as far as load carriage capacity is concerned. Moreover, the proposed neural network structure outperforms the available experimental model in predicting the pressure as well as the load carriage capacity.

The paper discusses a new modelling scheme known as ANN. A neural network predictor has been employed to analyze the effects of shaft surface profiles in the hydrodynamic lubrication of journal bearings.

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.

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.

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

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.

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

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.

The purpose of this paper is to experimentally and theoretically investigate slippers, which have an important role on power dissipation in the swash plate axial piston pumps.

The slipper geometry and working conditions affected on the slipper performance have been analyzed experimentally. The model of the slipper system has been established by original neural network (NN) method.

First, the effects of the slipper geometry with smooth and conical sliding surfaces on the slipper performance were experimentally analyzed. Smooth sliding surface slippers showed a better performance then the conical surface ones. According to the results, the neural predictor would be used as a predictor for possible experimental applications on modeling this type of system.

This paper discusses a new modeling scheme known as artificial NNs an experimental and a NN approach have been employed for analyzing axial piston pumps. The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modeling bearing system.

This paper presents an investigation for analyzing the efficiency of axial piston pumps in a variety conditions using a proposed neural network. Since slippers affect the performance of the system considerably, the effects of surface roughness on lubrication have been studied in slippers with varying hydrostatic bearing areas and surface roughness. The neural network structure is very suitable for this kind of system. The network is capable of predicting the leakage oil quantity of the experimental system. The network has parallel structure and fast learning capacity. It is also easy to see from the experimental results that the leakage oil quantity was caused by surface roughness, orifice diameter and the size of hydrostatic bearing area, loading pressure and the number of rotations. It can be outlined from the results for both approaches, neural network could be modeled slipper bearing systems in real time applications.

A neural network is employed to analyze axial piston pump of hydrostatic circular recessed bearing. Owing to complexity of the system, the neural network is used to predict the bearing parameters of the experimental system. The system mainly consists of cylinder block, piston, slipper, ball‐joint and swash plate. The neural model of the system has three layers, which are input layer with one neuron, hidden layer with ten neurons and output layer with three neurons. It can be outlined from the results for both approaches neural network could be modeled bearing systems in real time applications.

The purpose of this paper is to investigate and discuss the influence of the pattern, size and orientation of textures on journal bearing load carriage capacity. An important development in load carriage capacity of journal bearings can be obtained by forming regular surface structure in the form of threaded on their shaft surfaces. This is performed both theoretically and experimentally using shafts with textured (threaded) and untextured surfaces. Each screw thread can serve either as a micro‐hydrodynamic bearing in cases of full or mixed lubrication or as a micro reservoir for lubricant in cases of starved lubrication conditions.

The pressure distribution and the load‐carrying capacity are predicted using feed forward architecture of neurons. The inputs to the neurons are a collection of experimental data. These data are used to train the network using the delta‐bar‐delta, batch‐backprop, backprop, and backprop‐rand algorithms. The proposed neural model outperforms the available experimental system in predicting the pressure as well as load‐carrying capacity.

Theoretical models are developed using a neural network approach, and tests are performed, to investigate the potential of threaded textured surfaces in tribological components like mechanical seals, piston rings and journal bearings. In these tests, load carriage capacity is significantly increased with threaded textured shaft surfaces to the shafts with non‐textured surfaces.

The paper discusses a new modelling scheme known as artificial neural networks. A neural network predictor has been employed to analyze the effects of shaft surface profiles in hydrodynamic lubrication of journal bearing.

The purpose of this paper is to investigate experimentally slippers, which have an important role on power dissipation in the swash plate axial piston pumps. Since slippers affect the performance of the system considerably, the effects of surface roughness on lubrication have been studied in slippers with varying hydrostatic bearing areas and surface roughness.

An experimental set‐up was designed to determine the performance of slippers, which are capable of increasing the efficiency of axial piston pumps, in different conditions.

The findings suggest that the frictional power loss has been caused by surface roughness, capillary tube diameter, and the size of the hydrostatic bearing area, supply pressure and the relative velocity. In the case of the 0.7 and 9.5 μm surface roughness more power is needed to overcome the friction force between slippers and slipper plates, but less power loss occurs with the slippers with surface roughness of 1.5 μm. The slippers with surface roughness of 1.5 μm are considered, because of the optimum power loss. Moreover, the power loss decreases with increasing capillary tube diameter and supply pressure.

In order to investigate slipper behaviour under different operating conditions, with different capillary tube size and supply pressure an experimental work was carried out for finding exact design parameters of the real time system.