The work aims to investigate the influence of DP16 nail parameters on the connection performance of steel/aluminum heterogeneous plates and establish a prediction model of force for pulling out. Firstly, a simulation model for the penetration connection of DP16 nails into DP980 high-strength steel and 6082-T6 aluminum alloy plates was established to simulate the complete penetration of the nail into the steel/aluminum heterogeneous plates. Based on this fully penetrated model, a simulation study on the force for pulling out was conducted, and the reliability of the simulation model was validated through pull-out tests. Secondly, the effects of the main structural parameters of the nail on the force for pulling out were researched with the simulation model. Finally, a prediction model of force for pulling out was established by integrating the Particle Swarm Optimization (PSO) algorithm with Radial Basis Function (RBF) neural networks. The results showed that as the diameter of nail rod and the angle of the nail tip increased, the force for pulling out increased accordingly, showing a positive correlation. When the angle of the nail body texture was 45°, the relatively higher force for pulling out and superior comprehensive connection performance was achieved. The prediction of force for pulling out was successfully developed based PSO-RBF. The dominant factor influencing the force for pulling out is identified as the diameter of nail rod, and the prediction model of force for pulling out of DP16 nails used in connection steel/aluminum heterogeneous plates demonstrates high accuracy, which is of great significance for providing a reference for future research on the fastening performance of nails in connection of heterogeneous plates.
Key words
nail /
connection of heterogeneous plates /
connection performance /
force for pulling out /
RBF neural network
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