Vehicle Front Longitudinal Beam Seal Plate Stamping Optimization Based on Aggregation Adaptive PSO Algorithm
In order to improve the quality of stamping parts for vehicle front longitudinal beam sealing plate,a stamping process optimization method based on aggregation degree adaptive particle swarm optimization algorithm was proposed.The three-dimensional model,blank design result and stamping forming principle of front longitudinal beam sealing plate are introduced.Based on the forming limit curve,a multi-objective optimization model was established to reduce the thinning rate,thickening rate and springback of stamping parts.32 groups of experiments with 5 factors and 5 levels were designed by compound center ex-periment method,and the experimental data were obtained based on AutoForm software.BP neural network is used to fit the re-gression relationship between input and output,and the accuracy of the regression model is verified.On the basis of particle swarm optimization(PSO),Levy flight is integrated into PSO according to particle similarity and population aggregation,so as to improve the diversity of particles and the optimization ability of the algorithm.The improved particle swarm optimization algo-rithm is applied to solve multiple optimization models,the convergence speed of the improved particle swarm optimization algo-rithm is earlier than the traditional algorithm,and the objective function value of the improved particle swarm optimization algo-rithm is reduced by 3.71%compared with the traditional algorithm,which shows the advantages of the improved algorithm.After verification,the appearance of the optimized trial parts is qualified,and the thinning rate,thickening rate and springback meet the quality requirements,and can be mass produced.
Front Longitudinal Beam Seal PlateParticle SimilarityDegree of Population AggregationLevy Flight
NETL
NSTL
万方数据
维普
