锻压技术

尾灯安装加强件拉延工艺的动态NSGA-II多目标优化

英文标题：Multi-objective optimization of dynamic NSGA-II on drawing process for taillight mounting reinforcement

单位：1.雅安职业技术学院智能制造与信息工程学院 2.石家庄铁道大学机械工程学院

分类号：

出版年,卷(期):页码：2022,47(3):72-78

摘要：

为了提高车辆尾灯安装加强件的拉延成形质量，提出了基于动态NSGA-II算法的多目标优化方法。以减小最大减薄率和起皱趋势函数为目标建立了多目标优化模型，选择4个拉延筋阻力系数和压边力为优化参数，基于有限元法获得了不同条件下拉延件的性能参数。在NSGA-II算法中引入动态拥挤度计算方法，保持了选择染色体的多样性，提高了动态NSGA-II算法的优化能力。使用动态NSGA-II算法求解多目标优化模型，其Pareto前沿解优于传统NSGA-II算法。根据优化后的参数生产了10个试制件，试制件的最大减薄率和最大增厚率的均值均小于厂家产品的均值，且试制件最大减薄率和最大增厚率的标准差较小，实验结果说明：参数优化后的生产质量和稳定性均得到了提高。

To improve the drawing quality of vehicle taillight mounting reinforcement, based on dynamic NSGA-II algorithm, a multi-objective optimization method was proposed. Then, aiming at reducing the maximum thinning rate and wrinkling trend function, a multi-objective optimization model was established, and taking four drawbead resistance coefficients and blank holder force as the optimization parameters, the performance parameters of drawn parts under different conditions were obtained based on the finite element method. Furthermore, the dynamic congestion calculation method was introduced into NSGA-II algorithm to maintain the diversity of selected chromosomes and improve the optimization ability of dynamic NSGA-II algorithm, and using the dynamic NSGA-II algorithm to solve the multi-objective optimization model, its Pareto frontier solution was superior to that of traditional NSGA-II algorithm. According to the optimized parameters, ten trial pieces were produced, the mean values of the maximum thinning rate and the maximum thickening rate for trial pieces were less than the mean values of products for manufacturer, and the standard deviations of the maximum thinning rate and the maximum thickening rate of the trial pieces were small. The experimental results show that the production quality and stability are improved after the parameters optimization.

基金项目：

河北省科技计划项目(14212202D)

宋晓雯（1987-），女，学士，讲师 E-mail：n5j8jl@163.com

参考文献：

[1]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.

[2]周均, 王勇. 基于Autoform软件的冲压成形工艺参数优化[J]. 兵器材料科学与工程,2017,40(1):73-76.

Zhou J, Wang Y. Optimization of stamping forming parameters based on autoform software [J]. Ordnance Material Science and Engineering, 2017,40(1):73-76.

[3]谢晖, 沈云飞,王杭燕.基于改进响应面模型的冲压回弹工艺稳健性优化[J].塑性工程学报,2018,25(4):26-32.

Xie H, Shen Y F, Wang H Y. Robustness optimization of stamping springback based on improved response surface model [J]. Journal of Plasticity Engineering, 2018,25(4):26-32.

[4]熊文韬, 刘泓滨,孙元贵,等. 基于GS理论与神经网络的汽车覆盖件成形优化[J].兵器材料科学与工程,2017,40(4):84-89.

Xiong W T, Liu H B, Sun Y G, et al. Forming optimization of automobile covering parts based on GS theory and neural network [J]. Ordnance Material Science and Engineering, 2017,40(4):84-89.

[5]万志远, 陈银平.轿车后背门内板冲压工艺及模具设计[J].制造技术与机床,2020,(2):164-166.

Wan Z Y, Chen Y P. Stamping process and die design of the back door inner panel of car [J]. Manufacturing Technology & Machine Tool, 2020,(2):164-166.

[6]何泽歆，黄超群. 基于克里金模型和智能算法的汽车加强件热冲压工艺优化 [J]. 锻压技术，2020,45(10):47-52.

He Z X，Huang C Q. Optimization of hot stamping process for automobile reinforcement part based on Kriging model and intelligent algorithm [J]. Forging & Stamping Technology，2020,45(10):47-52.

[7]Sara Abolhasani, Ali Ahmadpour, Tahereh Rohani Bastami, et al. Facile synthesis of mesoporous carbon aerogel for the removal of ibuprofen from aqueous solution by central composite experimental design (CCD) [J]. Journal of Molecular Liquids, 2019, 281:261-268.

[8]王刚, 王馨,宋小三,等. 响应曲面法中BBD和CCD在优化巯基乙酰化壳聚糖制备条件中的比较[J].环境工程学报,2018,12(9):2502-2511.

Wang G, Wang X, Song X S, et al. Comparison between BBD and CCD in response surface methodology to optimize preparation conditions of mercaptoacetyl chitosan [J]. Chinese Journal of Environmental Engineering, 2018,12(9):2502-2511.

[9]Jaeger Anna, Coll Claudia, Posselt Malte, et al. Using recirculating flumes and a response surface model to investigate the role of hyporheic exchange and bacterial diversity on micropollutant half-lives[J]. Environmental Science：Processes & Impacts,2019, 21(12):2093-2108.

[10]张广鹏, 任利娟,王启文. 基于图像信息的砂带磨削材料去除率预测模型[J].仪器仪表学报,2019,40(12):127-134.

Zhang G P, Ren L J，Wang Q W. Image-based prediction model for material removal rate of abrasive belt grinding [J]. Chinese Journal of Scientific Instrument, 2019,40(12):127-134.

[11]蒋科坚, 祝长生.电磁轴承-柔性转子系统多目标加权的主动振动控制[J].浙江大学学报：工学版,2016,50(10):1946-1951.

Jiang K J, Zhu C S. Vibration suppressing with mixed weight for multi-targets in active magnetic bearing-flexible rotor system [J]. Journal of Zhejiang University：Engineering Science, 2016,50(10):1946-1951.

[12]王链, 张亮,赖枫鹏,等. 基于替代模型的油藏注采参数多目标优化设计[J].科学技术与工程,2019,19(26):178-185.

Wang L, Zhang L, Lai F P, et al. Multi-objective optimization design of reservoir injection-production parameters basedon sub-stitution model [J]. Science Technology and Engineering,2019,19(26):178-185.

[13]Yang S, Shao Y F, Zhang K. An effective method for solving multiple travelling salesman problem based on NSGA-II[J]. Systems Science & Control Engineering，2019, 7(2):108-116.

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