锻压技术

车辆悬架纵臂冲压工艺的改进花授粉算法优化

英文标题：Optimization on stamping process of vehicle suspension longitudinal arm based on improved flower pollination algorithm

作者：刘宝生邓三鹏

单位：天津交通职业学院天津职业技术师范大学

关键词：悬架纵臂冲压成形开裂临界状态改进花授粉算法减薄率

分类号：TG386

出版年,卷(期):页码：2021,46(11):130-136

摘要：

为了减小车辆悬架纵臂的冲压减薄率、提高产品的加工合格率，提出了冲压工艺参数的改进花授粉算法优化方法。介绍了悬架纵臂三维模型和冲压成形过程。使用有限元仿真分析了开裂临界状态下的等效应力分布和厚度分布，并确定了开裂危险位置。以减小危险位置减薄率为目标建立了优化模型，选择了上模压力和摩擦因数作为优化参数。使用最优拉丁超立方抽样设计了80组实验，建立了2输入1输出的BP神经网络结构，用于拟合工艺参数与质量参数的非线性关系。对花授粉算法的全局搜索方法进行了改进，提高了算法的搜索能力，利用改进花授粉算法求解优化模型得到了最佳参数组合。经验证，优化后悬架纵臂冲压件危险位置的厚度均值为1.637 mm、标准差为0.091 mm；产品合格率由现行的60%提高到了97%。

In order to reduce the thinning rate of vehicle suspension longitudinal arm in the stamping and improve the processing qualification rate of products, the optimizated method of stamping process parameters based on flower pollination algorithm was proposed, and the 3D model and the stamping process of suspension longitudinal arm were introduced. Then, the distributions of equivalent stress and thickness in the critical state of cracking were analyzed by finite element simulation, and the critical location of cracking was determined. Furthermore, the optimization model was established with the goal of reducing the thinning rate of dangerous locations, the upper die pressure and friction coefficient were chosen as the optimization parameters. Optimal Latin hypercube sampling was used to design eighty groups of experiment, and BP neutral network structure with two inputs and one output was established to fit the nonlinear relationship between process parameters and quality parameters. Finally, the global search method of flower pollination algorithm was improved to enhance the search ability of the algorithm, and the optimal parameters combination was obtained by using the improved flower pollination algorithm to solve the optimization model. It is clarified that after optimization, the average thickness of staming parts for suspension longitudinal arm at the dangerous location is 1.637 mm, the standard deviation is 0.091 mm, and the product qualification rate increases from the current 60% to 97%.

基金项目：

国家青年自然科学基金资助项目（61301040）

作者简介：刘宝生（1974-），男，硕士，副教授，E-mail：284078387@qq.com

参考文献：

[1]刘强, 俞国燕,梅端.基于Dynaform与RBFNSGAII算法的冲压成形工艺参数多目标优化[J].塑性工程学报,2020, 27(3):16-25.

Liu Q, Yu G Y, Mei D. Multiobjective optimization of stamping forming process parameters based on Dynaform and RBFNSGAII algorithm [J]. Journal of Plasticity Engineering, 2020,27(3):16-25.

[2]邓振鹏, 周惦武,蒋朋松,等.基于正交试验的锆合金薄板带材冲压工艺参数优化[J].锻压技术,2019,44(9):12-17.

Deng Z P, Zhou D W, Jiang P S, et al. Optimization on stamping process parameters for zirconium alloy sheet strip based on orthogonal experiment [J]. Forging & Stamping Technology, 2019,44(9):12-17.

[3]苏友煌, 王军辉,冯怡爽,等.汽车内覆盖件无油冲压的数值模拟及工艺优化[J].塑性工程学报,2019,26(5):42-50.

Su Y H, Wang J H，Feng Y S, et al. Numerical simulation and process optimization of dry stamping for automotive interior cover parts [J]. Journal of Plasticity Engineering, 2019,26(5):42-50.

[4]陈剑, 戴南山,陈小龙,等.汽车下加强件前横梁冲压成型工艺模拟仿真及优化研究[J].机械工程师,2020,(1):72-75.

Chen J, Dai N S, Chen X L, et al. Simulation and optimization research on stamping forming process of front beam in automobile lower reinforcement [J]. Mechanical Engineer, 2020,(1):72-75.

[5]李奇涵, 景淑帆,高嵩,等.基于响应面法的22MnB5高强钢热冲压成形性优化[J].锻压技术,2020,45(6):93-101.

Li Q H, Jing S F, Gao S, et al. Optimization on hot stamping formability for 22MnB5 high strength steel based on response surface method [J]. Forging & Stamping Technology, 2020,45 (6):93-101.

[6]陆克中, 章哲庆,刘利斌.自适应变异的量子花授粉算法[J].控制工程,2020,27(4):683-691.

Lu K Z, Zhang Z Q, Liu L B. Adaptive mutation quantumbehaved flower pollination algorithm [J]. Control Engineering of China, 2020,27(4):683-691.

[7]Shambour M K Y, Abusnaina A A, Alsalibi A I. Modified global flower pollination algorithm and its application for optimization problems[J]. Interdisciplinary Sciences: Computational Life Sciences, 2019, 11(3):496-507.

[8]王志娟, 戴秀浪,刘秉翼.高强钢在量产车型轻量化中的实际应用[J].汽车工艺师,2020,(4):38-41.

Wang Z J, Dai X L, Liu B Y, et al. The practical application of high strength steel in the lightweight of mass production vehicle [J]. Auto Lightweight, 2020,(4):38-41.

[9]夏元峰. 变厚度汽车B柱冲压成形工艺研究及模具设计[D].哈尔滨：哈尔滨工业大学,2013.

Xia Y F. Research on Stamping Process and Mold Design of B Pillar with Variable Thickness [D]. Harbin: Harbin Institute of Technology, 2013.

[10]衣杰栋. DC05板料成形性能参数测定及汽车座椅支撑板冲压工艺优化研究[D].镇江：江苏大学,2019.

Yi J D. Determination of Formability Parameters of DC05 Sheet and Optimization of Stamping Process for Automobile Seat Support Plate [D]. Zhenjiang: Jiangsu University, 2019.

[11]蔡明浩, 兰少明,黄坤兰,等. 细化Kriging模型在轻轨车轴优化设计中的应用[J].机械设计与制造, 2019,(8):176-179.

Cai M H, Lan S M, Huang K L, et al. Application of refined Kriging model in optimization design of light rail axles [J]. Machinery Design & Manufacture, 2019,(8):176-179.

[12]武文斌, 唐燕武,朱家明,等. 基于犹豫模糊集和BP神经网络的集成预测模型及其应用[J].统计与决策,2020,36 (11):41-45.

Wu W B, Tang Y W, Zhu J M, et al. An Integrated forecasting model based on hesitant fuzzy sets and bp neural networks and its application [J]. Statistics & Decision, 2020,36(11):41-45.

[13]陆克中, 章哲庆,刘利斌. 自适应变异的量子花授粉算法[J].控制工程,2020,27(4):683-691.

Lu K Z, Zhang Z Q, Liu L B. Adaptive mutation quantumbehaved flower pollination algorithm [J]. Control Engineering of China, 2020,27(4):683-691.

[14]康帅, 俞建成,张进,等. 基于粒子群优化神经网络的水下链式机器人直航阻力预报[J].机械工程学报, 2019,55 (21):29-39.

Kang S, Yu J C, Zhang J, et al. Direct route drag prediction of chainstructured underwater vehicle based on neural network optimized by particle swarm optimization [J]. Journal of Mechanical Engineering, 2019,55(21):29-39.

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