Optimization of press bend forming path of aircraft integral panel

Abstract In order to design the press bend forming path of aircraft integral panels, a novel optimization method was proposed, which integrates FEM equivalent model based on previous study, the artificial neural network response surface, and the genetic algorithm. First, a multi-step press bend forming FEM equivalent model was established, with which the FEM experiments designed with Taguchi method were performed. Then, the BP neural network response surface was developed with the sample data from the FEM experiments. Furthermore, genetic algorithm was applied with the neural network response surface as the objective function. Finally, verification was carried out on a simple curvature grid-type stiffened panel. The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%. Therefore, this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

[1]  Tomas Jansson,et al.  Optimizing sheet metal forming processes—Using a design hierarchy and response surface methodology , 2006 .

[2]  Taylan Altan,et al.  Adaptive FEM simulation for prediction of variable blank holder force in conical cup drawing , 2004 .

[3]  Henry W. Altland,et al.  Engineering Methods for Robust Product Design , 1996 .

[4]  Catarina F. Castro,et al.  Optimization of metal forming processes , 2004 .

[5]  Zou Lin,et al.  Optimization of die profile for improving die life in the hot extrusion process , 2003 .

[6]  Ba Nghiep Nguyen,et al.  A Numerical Process Control Method for Circular-Tube Hydroforming Prediction , 2004 .

[7]  Yong H. Kim,et al.  Optimal design of superplastic forming processes , 2001 .

[8]  王海波,et al.  FEM equivalent model for press bend forming of aircraft integral panel , 2009 .

[9]  Mgd Marc Geers,et al.  An adaptive simulation approach designed for tube hydroforming processes , 2005 .

[10]  Tapabrata Ray,et al.  Optimal process design of sheet metal forming for minimum springback via an integrated neural network evolutionary algorithm , 2004 .

[11]  Ramana V. Grandhi,et al.  Studies on optimization of metal forming processes using sensitivity analysis methods , 2004 .

[12]  Wei Shyy,et al.  Response surface and neural network techniques for rocket engine injector optimization , 1999 .

[13]  Luísa Costa Sousa,et al.  Optimisation of shape and process parameters in metal forging using genetic algorithms , 2004 .

[14]  J Munroe,et al.  Integral Airframe Structures (IAS)---Validated Feasibility Study of Integrally Stiffened Metallic Fuselage Panels for Reducing Manufacturing Costs , 2000 .

[15]  Baldev Raj,et al.  Artificial neural network modeling of composition–process–property correlations in austenitic stainless steels , 2008 .

[16]  Taylan Altan,et al.  Optimization of blank dimensions to reduce springback in the flexforming process , 2004 .

[17]  Jean-Philippe Ponthot,et al.  Parameter identification and shape/process optimization in metal forming simulation , 2003 .

[18]  Hing Wah Lee,et al.  Neuro-genetic optimization of temperature control for a continuous flow polymerase chain reaction microdevice. , 2007, Journal of biomechanical engineering.

[19]  Kuang-Jau Fann,et al.  Optimization of loading conditions for tube hydroforming , 2003 .

[20]  Min Wan,et al.  FEM equivalent model for press bend forming of aircraft integral panel , 2009 .

[21]  Qiusheng Li,et al.  A new artificial neural network-based response surface method for structural reliability analysis , 2008 .

[22]  Tomas Jansson,et al.  Optimization of Draw-In for an Automotive Sheet Metal Part An evaluation using surrogate models and response surfaces , 2005 .

[23]  Kozo Takayama,et al.  Neural network based optimization of drug formulations. , 2003, Advanced drug delivery reviews.

[24]  Surjya K. Pal,et al.  Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals , 2008 .

[25]  Luigi Carrino,et al.  A posteriori optimisation of the forming pressure in superplastic forming processes by the finite element method , 2003 .

[26]  B.H.M. Sadeghi,et al.  A BP-neural network predictor model for plastic injection molding process , 2000 .

[27]  Eiji Nakamachi,et al.  Development of optimum process design system for sheet fabrication using response surface method , 2003 .

[28]  Hasan Kurtaran,et al.  A novel approach for the prediction of bend allowance in air bending and comparison with other methods , 2008 .

[29]  C. Aghanajafi,et al.  Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms , 2008 .