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汽车后围内板冲压工艺的高斯扰动粒子群优化
英文标题:Stamping process optimization of automobile rear inner panel based on Gaussian perturbation particle swarm
作者:胡锦达 
单位:沈阳职业技术学院汽车分院 
关键词:汽车后围内板 高斯扰动粒子群算法 冲压工艺 BP神经网络 正交实验 
分类号:TG386.1
出版年,卷(期):页码:2020,45(12):46-52
摘要:

为了提高汽车后围内板的制件质量,提出了基于高斯扰动粒子群算法的冲压工艺优化方法。针对冲压工艺流程,选择拉延工艺参数作为优化参数,以可以反映制件质量的参数作为目标参数,建立了优化目标函数。设计了4因素4水平的正交实验,并使用单隐含层BP神经网络对实验数据进行拟合。以粒子群算法为基础,提出了精英粒子分阶段高斯扰动策略,从而设计了基于高斯扰动粒子群算法的优化模型求解方法,得到了拉延工艺的最优参数。经模拟仿真成形和试制件验证,采用优化后的冲压工艺未出现起皱和开裂现象,验证了优化冲压工艺的有效性。

To improve the quality of automobile rear inner panel, based on Gaussian perturbation particle swarm algorithm, the stamping process optimization method was proposed, and for the stamping process, drawing process parameters were chosen as optimizing parameters. Then, the parameters reflecting workpiece quality were chosen as objective parameters, and the optimizing objective function was built. Furthermore, the orthogonal experiment with four factors and four levels was designed, and the experiment data were fit by BP neutral network with single hidden layer. On the basis of particle swarm algorithm, the elite particles staged gaussian perturbation strategy was put forward, and based on Gaussian perturbation particle swarm algorithm, the solving method of optimizing model was designed to obtain the optimal parameters of drawing process. Finally, it was clarified by simulation forming and trial workpiece that there was no wrinkling and cracking in the optimized stamping process, which proves the validity of optimized stamping process.

基金项目:
黑龙江省应用技术研发计划重大项目(GA17A401)
作者简介:
胡锦达(1981-),女,硕士,副教授 E-mail:1607164419@qqcom
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