Transactions of Materials and Heat Treatment

Title：Optimization on bending process for minimizing springback angle of U-shaped workpiece based on particle swarm algorithm

Authors：

Unit：

KeyWords：

ClassificationCode：TG386

year,vol(issue):pagenumber：2020,45(11):60-66

Abstract：

In order to reduce the springback angle of U-shaped workpiece for aluminum alloy during bending, the optimization method for forming process based on three-black-hole system particle swarm algorithm was proposed. Then, the bending process flow and the definition method of springback angle for U-shaped workpiece were introduced, and the objective function was built for minimizing the springback angle of U-shaped workpiece. Furthermore, taking heating temperature, blank holder force, die clearance and stamping speed as the optimization parameters, the optimizing space was confirmed, the experiments were designed by optimal Latin hypercube sampling method, and the function model between the target parameters and the optimization parameters was fitted by adaptive neural network. Finally, in order to improve the particle diversity of particle swarm algorithm, three-black-hole theoretical particle swarm algorithm was put forward, and the optimization model of springback angle was solved by the improved particle swarm algorithm to obtain the optimal parameter combination with the heating temperature of 240 ℃, the blank holder force of 50 kN, the die clearance of 1.2 mm and the stamping speed of 800 mm·s-1. The verified experiments show that the mean value of springback angle after optimization is reduced by 9.37% compared with that before optimization, and the standard deviation is also slightly decreased. Therefore, after optimization, the springback angle of U-shaped workpiece is reduced to a certain extent, and the quality stability of U-shaped workpiece is improved.

Funds：

邢台市科技计划项目（2019ZC060）

胡丽华（1989-），女，硕士，讲师 E-mail：z2t3kg@163.com

Reference：

[1]姜天亮,龚红英,施为钟,等.基于响应曲面法U形件弯曲成形工艺参数优化[J].上海工程技术大学学报,2019,33(3):278-282.

Jiang T L, Gong H Y, Shi W Z, et al. Process parameters optimization of U-shaped bending based on response surface methodology[J]. Journal of Shanghai University of Engineering Science, 2019,33(3):278-282.

[2]李鑫，周杰，丁蓉蓉，等. 基于响应面法的副车架内高压成形工艺参数优化[J].锻压技术，2018,43(10):111-116.

Li X，Zhou J，Ding R R，et al. Optimization on process parameters for hydroforming of sub-frame based on response surface method [J]. Forging & Stamping Technology，2018, 43(10):111-116.

[3]林浩波,刘军辉,吴立国.基于遗传算法的防撞钢梁热冲压成形工艺优化[J].塑性工程学报,2019,26(5):65-69.

Lin H B, Liu J H, Wu L G. Optimization of hot stamping process for anti-collision beam based on genetic algorithms [J]. Journal of Plasticity Engineering, 2019,26(5):65-69.

[4]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.

[5]钟文,项辉宇,冷崇杰,等. 成形工艺参数对U形件回弹影响的仿真分析[J].机床与液压，2019,47(19):145-152.

Zhong W，Xiang H Y, Leng C J，et al.Simulation analysis of springback effect of forming process parameters on U-shaped workpiece[J]. Machine Tool & Hydraulics, 2019,47(19):145-152.

[6]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 parameterssed on Dynaform and RBF-NSGA-II algorithm [J]. Journal of Plasticity Engineering, 2020,27(3):16-25.

[7]李凯强,屈华鹏,冯翰秋,等.温变形0Cr14Mn21NiN奥氏体不锈钢的组织性能[J].金属热处理,2019,44(11):8-13.

Li K Q, Qu H P, Feng H Q, et al. Microstructure and properties of warm deformed OCr14Mn21NiN austenitic stainless steel [J]. Heat Treatment of Metals, 2019,44(11):8-13.

[8]李玉森,岳振明,妥之彧,等.铝合金管材6061自由弯曲成形工艺仿真及优化[J].工程科学学报,2020,42(6):769-777.

Li Y S, Yue Z M, Tuo Z Y, et al. Simulation and optimization of the free bending process of aluminum alloy 6061 pipe [J]. Chinese Journal of Engineering, 2020,42(6):769-777.

[9]季宁,张卫星,于洋洋,等.基于最优拉丁超立方抽样方法和NSGA-Ⅱ算法的注射成型多目标优化[J].工程塑料应用,2020,48(3):72-77.

Ji N, Zhang W X, Yu Y Y, et al. Multi-objective optimization of injection molding based on optimal latin hypercube sampling method and NSGA-II algorithm[J]. Engineering Plastics Application, 2020,48(3):72-77.

[10]蔺想红,郑鉴洋,王向文,等.基于深度学习网络的神经元自适应投影分类方法[J].电子学报,2020,48(7):1321-1329.

Service：

【This site has not yet opened Download Service】【Add Favorite】