Hi-Lock bolt lap joint structure is widely used in aircraft, such as wings, tails and etc. During the service of an aircraft, the joints are subjected to variable loading, which may usually tend to fatigue failure. The single-shear lap joint with four Hi-Lock bolts is taken as the research object. The static tensile test and fatigue test of the Hi-lock bolt lap joint structure are conducted. The macroscopic and microscopic fracture analysis were carried out. It is found that the crack source is located on the joint surface of the two plates, which is on the longitudinal direction away from the edge of the hole, but not on the edge of the connection plate. The results also show that there is no significant difference between the effect of clearance and interference fit on the fatigue life of the lap joint structure under high load conditions.
In order to solve the steady-state error in position tracking control for electro-hydraulic servo universal testing machine, caused by uncertain parameters in the system model, an adaptive sliding mode control strategy based on RBF neural network is proposed for this situation. This paper utilizes the adaptive ability of RBF neural network to improve the control quality of the electro-hydraulic position servo system. The strategy has three parts: the equivalent control, the reaching law control and the compensation control based on RBF network. Simulations verify that the control system can track the reference curve well with unknown parameters.
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