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OA

To efficiently 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 effectiveness 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 es-cape 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 coefficient R2 higher than 0.99,and a mean square error MSE consistently less than 0.001.Therefore,the proposed improved model has high predictive accuracy and robustness.