目的 研究DP16射钉参数对钢/铝异质板材连接性能的影响规律,建立拉脱力预测模型。方法 首先,建立DP16射钉侵彻连接DP980高强钢和6082-T6铝合金板材的仿真模型,由此实现射钉完全侵入钢/铝异质板的仿真,并通过完全侵入后的模型开展射钉拉脱力仿真研究,以拉脱试验验证拉脱力仿真模型的可靠性。其次,利用仿真模型研究射钉主要结构参数对拉脱力的影响规律。最后,结合粒子群优化算法（PSO）与径向基神经网络（RBF）建立了拉脱力预测模型。结果 射钉的钉杆直径、钉尖角度与拉脱力呈正相关,钉身纹理角度为45°时拉脱力相对更大,具有较优的综合连接性能。实现了基于PSO-RBF的拉脱力预测。结论 射钉的钉杆直径是影响拉脱力的主导因素,连接钢/铝异质板DP16射钉的拉脱力预测模型精度较高,可为射钉连接异质板紧固性能的进一步研究提供参考。
Abstract
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.
关键词
射钉 /
异质板连接 /
连接性能 /
拉脱力 /
RBF神经网络
Key words
nail /
connection of heterogeneous plates /
connection performance /
force for pulling out /
RBF neural network
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基金
四川省科技计划重点研发项目（2021YFG0343）
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