火箭贮箱箱底充液拉深成形工艺的多目标优化

doi:10.3901/JME.2022.05.78

机械工程学报 ›› 2022, Vol. 58 ›› Issue (5): 78-86.doi: 10.3901/JME.2022.05.78

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火箭贮箱箱底充液拉深成形工艺的多目标优化

张在房¹, 徐冯¹, 孙习武²

1. 上海大学机电工程与自动化学院上海 200444;
2. 上海航天设备制造总厂有限公司上海 200245

收稿日期:2021-03-08 修回日期:2021-08-13 出版日期:2022-03-05 发布日期:2022-04-28
通讯作者: 张在房(通信作者),男,1978年出生,博士,副教授,博士研究生导师。主要研究方向为产品服务智能设计,数字孪生,人工智能方法与应用。E-mail:zaifangzhang@shu.edu.cn E-mail:zaifangzhang@shu.edu.cn
作者简介:徐冯,女,1996年出生。主要研究方向为多学科优化与动力学仿真。E-mail:xufeng1822@163.com

Multi-objective Optimization of Hydroforming Process of Rocket Tank Bottom

ZHANG Zai-fang¹, XU Feng¹, SUN Xi-wu²

1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444;
2. Shanghai Aerospace Equipments Manufacture Co. LTD, Shanghai 200245

Received:2021-03-08 Revised:2021-08-13 Online:2022-03-05 Published:2022-04-28

摘要/Abstract

摘要： 充液拉深成形技术能实现大型贮箱箱底的整体成形，然而成形件的质量受到许多工艺参数的影响。针对大型贮箱整体箱底构件充液拉深成形的起皱和破裂缺陷，以预胀压力、液室压力、压边力、压边圈圆角半径等工艺参数为研究对象，建立多目标优化模型。对贮箱箱底成形过程进行模拟，在此基础上，使用拉丁超立方采样法获得样本数据。采用克里金插值法（Kriging）和径向基函数（Radial basis function, RBF）建立工艺参数和质量指标之间的代理模型。利用NSGA-III算法和粒子群算法（Particle swarm optimization, PSO）确定了贮箱箱底达到目标（壁厚减薄率最小、破裂趋势最小、法兰边起皱最小、起皱趋势最小）时的最优工艺参数。最后通过实验验证了方法的有效性和结果的准确性。

关键词: 贮箱箱底, 充液拉深成形, 多目标优化, NSGA-III

Abstract: The hydroforming technology can realize the overall forming of the large storage tank’s bottom, but the quality is affected by many technological parameters. In view of the wrinkling and cracking defects of the integral storage tank’s bottom in hydroforming, a multi-objective optimization model is established for the process parameters include prebulging pressure, hydraulic pressure, blank holder force and radius of blank holder’corner. Based on the simulation about forming process of the storage tank’s bottom, the Latin hypercube sampling method is used to obtain the sample data. The agent model between process parameters and quality indicators is established by using Kriging and radial basis function（RBF）. NSGA-III algorithm and particle swarm optimization（PSO） algorithm are used to determine the optimal process parameters when the storage tank’s bottom reaches the target（minimum wall thickness reduction rate, minimum fracture trend, minimum flange wrinkle and minimum wrinkle trend）. The validity of the method and the accuracy of the results are verified by simulation experiments.

Key words: storage tank bottom, hydroforming, multi-objective optimization, NSGA-III

中图分类号:

TG394

张在房, 徐冯, 孙习武. 火箭贮箱箱底充液拉深成形工艺的多目标优化[J]. 机械工程学报, 2022, 58(5): 78-86.

ZHANG Zai-fang, XU Feng, SUN Xi-wu. Multi-objective Optimization of Hydroforming Process of Rocket Tank Bottom[J]. Journal of Mechanical Engineering, 2022, 58(5): 78-86.

参考文献

[1] 毕祥军,陈炳全,吴浩,等.运载火箭线式捆绑分离装置的设计,分析与优化[J].机械工程学报,2020,55(14):60-68. BI Xiangjun,CHEN Bingquan,WU Hao,et al. Design,analysis and optimization of linear bundled separation device for launch vehicle[J]. Journal of Mechanical Engineering,2020,55(14):60-68.
[2] BELL C,CORNEY J,ZUELLI N,et al. A state of the art review of hydroforming technology its applications,research areas,history,and future in manufacturing[J]. International Journal of Material Forming,2020,13(5):789-828.
[3] MUNOZ-RUBIO A,BIENVENIDO-HUERTAS D,BERMUDEZ-RODRIGUEZ F J,et al. Design optimization of the aeronautical sheet hydroforming process using the taguchi method[J]. Applied Sciences-Basel,2019,9(9):1-12.
[4] BALLIKAYA H,SAVAS V,OZAY C. The limit drawing ratio in die angled hydromechanical deep drawing method[J]. International Journal of Advanced Manufacturing Technology,2020,106(1-2):791-801.
[5] SAFARI M,JOUDAKI J. Process parameters selection in manufacturing of sharp conical parts based on Taguchi design of experiments[J]. Sadhana-Academy Proceedings in Engineering Sciences,2020,45(1):1-12.
[6] MA R,WANG C,ZHAI R,et al. An iterative compensation algorithm for springback control in plane deformation and its application[J]. Chinese Journal of Mechanical Engineering,2019,32(1):1-12.
[7] LI F,FAN H,GUO Y,et al. Water-jet cavitation shock bulging as novel microforming technique[J]. Chinese Journal of Mechanical Engineering,2021,34(1):1-11.
[8] ALIZAD-KAMRAN M,HOSEINPOUR-GOLLO M. Theoretical and experimental investigation of hydro-mechanical deep drawing of hemi-prolate spheroid cups[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2017,39(12):5181-5194.
[9] MODI B,KUMAR D R. Optimization of process parameters to enhance formability of AA 5182 alloy in deep drawing of square cups by hydroforming[J]. Journal of Mechanical Science and Technology,2019,33(11):5337-5346.
[10] 刘伟,徐永超,陈一哲,等.薄壁曲面整体构件流体压力成形起皱机理与控制[J].机械工程学报,2018,54(9):37-44. LUI Wei,XU Yongchao,CHEN Yizhe,et al. Mechanism and controlling of wrinkles during hydroforming of integral thin-walled curved shell[J]. Journal of Mechanical Engineering,2018,54(9):37-44.
[11] GAJJAR N,MODI B,DIGAVALLI R K. Improvement in accuracy of failure prediction in sheet hydroforming of square cups using stress-based forming limit diagram[J]. Journal of Failure Analysis and Prevention,2019,19(6):1792-1800.
[12] CAI Gaoshen,FU Jubo,ZHANG Dongxing,et al. A novel approach to predict wrinkling of aluminum alloy during warm/hot sheet hydroforming based on an improved Yoshida buckling test[J]. Materials,2020,13(5):1165.
[13] CAI Gaoshen,YANG Jinlin,YUAN Yongfeng,et al. Mechanics analysis of aluminum alloy cylindrical cup during warm sheet hydromechanical deep drawing[J]. International Journal of Mechanical Sciences,2020,174:105556.
[14] Chen Dayong,XU Yong,ZHANG Shihong. Evaluation of numerical and experimental investigations on the hybrid sheet hydroforming process to produce a novel high-capacity engine oil pan[J]. International Journal of Advanced Manufacturing Technology,2018,97(9-12):3625-3636.
[15] LIU Wei,CHEN Yizhe,XU Yongchao,et al. Enhancement on plastic deformation of curved surface shell by sheet hydroforming with optimized pre-bulging process[J]. International Journal of Advanced Manufacturing Technology,2018,97(9-12):4145-4156.
[16] 郭鹏,张新艳,余建波.基于深度强化学习与有限元仿真集成的拉深成形控制[J].机械工程学报,2020,56(20):47-58. GUO Peng,ZHANG Xinyan,YU Jianbo. Control of deep drawing process based on integration of deep reinforcement learning and finite element method[J]. Journal of Mechanical Engineering,2020,56(20):47-58.
[17] TURKOZ M,CORA ON,GEDIKLI H,et al. Numerical optimization of warm hydromechanical deep drawing process parameters and its experimental verification[J]. Journal of Manufacturing Processes,2020,57:344-353.
[18] CHEBBAH M S,LEBAAL N. Tube hydroforming optimization using a surrogate modeling approach and genetic algorithm[J]. Mechanics of Advanced Materials and Structures,2018,27(6):515-524.
[19] BANSCH E,LUTTMANN A,MONTALVO-URQUIZO J,et al. Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process[J]. Journal of Mathematics in Industry,2020,10(1):2.

火箭贮箱箱底充液拉深成形工艺的多目标优化

Multi-objective Optimization of Hydroforming Process of Rocket Tank Bottom

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