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PurposeIn 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsIn the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.Practical implicationsThe simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.Originality/valueThis 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 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.

Purpose -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. Design/methodology/approach -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. Findings -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 mm 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 mm. The slippers with surface roughness of 1.5 mm are considered, because of the optimum power loss. Moreover, the power loss decreases with increasing capillary tube diameter and supply pressure. Originality/value -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.

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