Forming Path Optimization for Press Bending of
Aluminum Alloy Aircraft Integral Panel

doi:10.1007/s12204-012-1336-3

Abstract

Abstract: Because of the light weight, high stiffness and high structural efficiency, aluminium alloy integral panels are widely used on modern aircrafts. Press bend forming has many advantages, and it becomes a significant technique in aircraft manufacturing field. In order to design the press bend forming path for aircraft integral panels, we propose a novel optimization method which integrates the finite element method (FEM) equivalent model based on our previous study, the artificial neural network response surface, and the genetic algorithm. First, a multi-step press bend forming FEM equivalent model is established, with which the FEM experiments designed with Taguchi method are performed. Then, the backpropagation (BP) neural network response surface is developed with the sample data from the FEM experiments. Further more, genetic algorithm (GA) is applied with the neural network response surface as the objective function. Finally, experimental and simulation verifications are carried out on a single stiffener specimen. The forming error of the panel formed with the optimal path is only 5.37% and the calculating efficiency has been improved by 90.64%. Therefore, this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Key words: aluminum alloy integral panel| press bend forming path| neural network response surface| genetic algorithm (GA)| experimental verification

摘要： Because of the light weight, high stiffness and high structural efficiency, aluminium alloy integral panels are widely used on modern aircrafts. Press bend forming has many advantages, and it becomes a significant technique in aircraft manufacturing field. In order to design the press bend forming path for aircraft integral panels, we propose a novel optimization method which integrates the finite element method (FEM) equivalent model based on our previous study, the artificial neural network response surface, and the genetic algorithm. First, a multi-step press bend forming FEM equivalent model is established, with which the FEM experiments designed with Taguchi method are performed. Then, the backpropagation (BP) neural network response surface is developed with the sample data from the FEM experiments. Further more, genetic algorithm (GA) is applied with the neural network response surface as the objective function. Finally, experimental and simulation verifications are carried out on a single stiffener specimen. The forming error of the panel formed with the optimal path is only 5.37% and the calculating efficiency has been improved by 90.64%. Therefore, this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

关键词: aluminum alloy integral panel| press bend forming path| neural network response surface| genetic algorithm (GA)| experimental verification

CLC Number:

V 261.2

YAN Yu1 (阎昱), WANG Hai-bo1* (王海波), WAN Min2 (万敏). Forming Path Optimization for Press Bending of Aluminum Alloy Aircraft Integral Panel[J]. Journal of shanghai Jiaotong University (Science), 2012, 17(5): 635-642.

References

[1] Munroe J, Wilkins K, Gruber M. Integral airframe structures (IAS): Validated feasibility study of integrally stiffened metallic fuselage panels for reducing manufacturing costs [R]. Seattle, Washington: Boeing Commercial Airplane Group, 2000.

[2] Sheng Z Q, Jirathearanat S, Altan T. Adaptive FEM simulation for prediction of variable blank holder force in conical cup drawing [J]. International Journal of Machine Tools and Manufacture, 2004, 44(5): 487-494.

[3] Sun G Y, Li G Y, Gong Z H, et al. Multiobjective robust optimization method for drawbead design in sheet metal forming [J]. Materials and Design, 2010,31(4): 1917-1929.

[4] Lorenzo R D, Ingarao G, Chinesta F. Integration of gradient based and response surface methods to develop a cascade optimisation strategy for Y-shaped tube hydroforming process design [J]. Advances in Engineering Software, 2010, 41(2): 336-348.

[5] Mirzaali M, Seyedkashi S M H, Liaghat G H,et al. Application of simulated annealing method to pressure and force loading optimization in tube hydroforming process [J]. International Journal of Mechanical Sciences, 2012, 55(1): 78-84.

[6] Zeng G, Li S H , Yu Z Q, et al. Optimization design of roll profiles for cold roll forming based on response surface method [J]. Materials and Design, 2009, 30(6):1930-1938.

[7] Ou H, Wang P, Lu B, et al. Finite element modelling and optimisation of net-shape metal forming processes with uncertainties [J]. Computers and Structures,2012, 90-91: 13-27.

[8] Zhao G Q, Ma X W, Zhao X H, et al. Studies on optimization of metal forming processes using sensitivity analysis methods [J]. Journal of Materials Processing Technology, 2004, 147(2): 217-228.

[9] Ant′onio C C, Catarina C, Sousa L C. Optimization of metal forming processes [J]. Computers and Structures, 2004, 82(17-19): 1425-1433.

[10] Castro C F, Ant′onio C A C, Sousa L C. Optimization of shape and process parameters in metal forging using genetic algorithms [J]. Journal of Materials Processing Technology, 2004, 146(3): 356-364.

[11] Palaniswamy H, Ngaile G, Altan T. Optimization of blank dimensions to reduce springback in the flexforming process [J]. Journal of Materials Processing Technology, 2004, 146: 28-34.

[12] Ohata T, Nakamura Y, Katayama T, et al. Development of optimum process design system for sheet fabrication using response surface method [J]. Journal of Materials Processing Technology, 2003, 143-144:667-672.

[13] Lee H W, Arunasalam P, Laratta W P. Neurogenetic optimization of temperature control for a continuous flow polymerase chain reaction microdevice [J]. Journal of Biomechanical Engineering, 2007, 129(4):540-547.

[14] Jansson T, Nilsson L. Optimizing sheet metal forming processes: Using a design hierarchy and response surface methodology [J]. Journal of Materials Processing Technology, 2006, 178(1-3): 218-233.
[15] Takayama K, Fujikawa M, Obata Y, et al. Neural network based optimization of drug formulations [J].Advanced Drug Delivery Reviews, 2003, 55(9): 1217-1231.

[16] Mandal S, Sivaprasad P V, Venugopal S. Artificial neural network modeling of composition-processproperty correlations in austenitic stainless steels [J]. Materials Science and Engineering A, 2008, 485(1-2):571-580.

[17] Pal S, Pal S K, Samantaray A K. Artificial neural network modeling of weld joint strength prediction of a pulsed metal insert gas welding process using arc signals [J]. Journal of Materials Processing Technology,2008, 202(1-3): 464-474.

[18] Kurtaran H. A novel approach for the prediction of bend allowance in air bending and comparison with other methods [J]. International Journal of Advanced Manufacturing Technology, 2008, 37(5-6): 486-495.

[19] Cheng J, Li Q S, Xiao R C. A new artificial neural network-based response surface method for structural reliability analysis [J]. Probabilistic Engineering Mechanics,2008, 23(1): 51-63.

[20] Yan Y, Wan M, Wang H B. FEM equivalent model for press bend forming of aircraft integral panel [J].Transactions of Nonferrous Metals Society of China,2009, 19(2): 414-424.

[21] Fowlkes W Y, Creveling C M. Engineering methods for robust product design [M]. Massachusetts, MA:Addison Wesley Longman, Inc., 1995.

Forming Path Optimization for Press Bending of Aluminum Alloy Aircraft Integral Panel

Forming Path Optimization for Press Bending of Aluminum Alloy Aircraft Integral Panel

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles 0

Metrics

Comments

[1]	SUN Qianyang, ZHOU Li, DING Shifeng, LIU Renwei, DING Yi. An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships [J]. Journal of Shanghai Jiao Tong University, 2024, 58(2): 156-165.
[2]	SU Yiyi, XU Qiping, LIU Jinyang. Theoretical Modeling, Simulation Analysis, and Experimental Investigation of a Pneumatic Toothed Soft Actuator [J]. Journal of Shanghai Jiao Tong University, 2023, 57(8): 1016-1027.
[3]	ZHAO Zhibin, LUO Bin, TANG Ting, WANG Chunfang, SUN Zhonghua. Improved Self-Excited Resonant Wireless Power Transmission System [J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 859-867.
[4]	HE Ke, WU Zishuai, WANG Daoai, ZHANG Zhinan. Development of a Triboelectric Performance Test System [J]. Journal of Shanghai Jiao Tong University, 2021, 55(5): 624-630.
[5]	LI Jie (李杰), LIU Yongzhi (刘勇智), SHAN Chenglong (鄯成龙), DAI Cong (戴聪). Implementation of Simplified Fractional-Order PID Controller Based on Modified Oustaloup's Recursive Filter [J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 44-50.
[6]	WANG Menghan* (王梦寒), XIAO Guiqian (肖贵乾), WANG Jinqiang (王晋强), LI Zhi (李志). Optimization of Clinching Tools by Integrated Finite Element Model and Genetic Algorithm Approach [J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(2): 262-272.
[7]	MENG Yu *(孟宇), GAN Xin (甘鑫), WANG Yu (汪钰), GU Qing (顾青). LQR-GA Controller for Articulated Dump Truck Path Tracking System [J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(1): 78-85.
[8]	JIAO Qinglong (焦庆龙), XU Da (徐达). A Discrete Bat Algorithm for Disassembly Sequence Planning [J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(2): 276-285.
[9]	DING Siyi,JIN Sun,LI Zhimin,WEI Zhenqi,YANG Fuyong. Deviation Propagation Model and Optimization of Concentricity for Aero-Engine Rotor Assembly [J]. Journal of Shanghai Jiaotong University, 2018, 52(1): 54-62.
[10]	PAN Qian1*(潘谦), HE Xing1 (何星), CAI Yun-ze1 (蔡云泽),WANG Zhi-hua2 (王治华), SU Fan2 (苏凡). Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands [J]. Journal of shanghai Jiaotong University (Science), 2015, 20(2): 218-223.
[11]	LIU Changhuia,JIN Suna,LAI Xinmina,LI Feib. Molding and Optimization of Injection Conditions on Shrinkage for Thin-Walled Wax Part Using RSM-GA Method [J]. Journal of Shanghai Jiaotong University, 2015, 49(09): 1380-1386.
[12]	FENG Jinzhi1，YU Fan2，ZHENG Songlin1，SUN Tao1，GAO Dawei1. Hierarchy Control Strategy for Active Hydro-Pneumatic Suspension Vehicles Based on Genetic Algorithm [J]. Journal of Shanghai Jiaotong University, 2014, 48(04): 525-531.
[13]	HUANG Qiang1,2 (黄强), LOU Xin-yuan3 (楼新远), WANG Wei4* (王薇), NI Shao-quan1 (倪少权). Research of Order Allocation Model Based on Cloud and Hybrid Genetic Algorithm Under Ecommerce Environment [J]. Journal of shanghai Jiaotong University (Science), 2013, 18(3): 334-342.
[14]	XU Ji-xiang* (许继祥), ZHAO Jin-cheng (赵金城), DUAN Hai-juan (段海娟). Risk-Identification-Based Hybrid Method for Estimating the System Reliability of Existing Jacket Platforms Under Fire [J]. Journal of shanghai Jiaotong University (Science), 2013, 18(1): 70-75.
[15]	SHI Yu-Lei, LU Zhi-Qiang. Order Allocation in Supply Chain Considering the Economic Production Quantity of Suppliers [J]. Journal of Shanghai Jiaotong University, 2011, 45(12): 1747-1752.