基于多策略改进复合麻雀搜索算法的自冲铆成形质量预测

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关键词：自冲铆；神经网络；优化算法；成形质量预测；仿真

中图分类号：TG938

DOI：10.3969/j.issn.1004-132X.2026.02.022 开放科学（资源服务）标识码（OSID）：

Prediction of Self-piercing Riveting Quality Based on Multi-strategy Improved Composite Sparrow Search Algorithm

LIU Yangl* WU Qingjun1 GUO Hao1QI Kaifei1ZHUANG WeiminFUGuangsheng？ 1.School of Mechanical and Automotive Engineering，Qingdao University of Technology， Qingdao， Shandong，266520 2.National Key Laboratory of Automotive Chassis Integration and Bionics， Jilin University， Changchun，130022 3.Qingdao Wuling Special Purpose Vehicle Co.，Ltd.，Qingdao， Shandong，266555

Abstract：To eficiently predict the forming quality of self-piercing riveted joints，a finite element model of self-piercing riveting for AA5754 aluminum alloys was established，and the efectiveness of the simulation model was verified through experiments. Based on the simulation analysis，176 sets of effective cross-sectional data of the joints were obtained.By integrating the sparrow search algorithm and the butter fly algorithm，a composite optimization algorithm was constructed.The algorithm's convergence speed and solution quality were improved by employing population initialization and lens reverse learning strategies. Multidirectional learning and Levy flight strategies were introduced to enhance the algorithm's ability to escape local optima，thereby improving the global search capabilities.Research indicates that the prediction results of the established model have a MAPE of less than 10% ，a correlation coeficient R2 higher than 0.99，and a mean square error MSE consistently less than O.OO1. Therefore，the proposed improved model has high predictive accuracy and robustness.

Key words： self-piercing riveting； neural network；optimization algorithm； forming quality prediction；simulation

0 引言

随着节能减排要求的提高，铝合金、镁合金和复合材料等轻质材料在新能源汽车车身结构中广泛应用]。（剩余17149字）