Multi-objective Optimization of Sheet Metal Forming Die Using Genetic Algorithm Coupled with RSM and FEA

Present study describes the approach of applying response surface methodology (RSM) with a Pareto-based multi-objective genetic algorithm to assist engineers in optimization of sheet metal forming. In many studies, finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi-objective problem into a single-objective problem. This paper aims to minimize objective functions of fracture and wrinkle simultaneously. Design variables are blank-holding force and draw-bead geometrical parameters (length and diameter). RSM has been used for design of experiment and finding relationship between variables and objective functions. Forming limit diagram has been used to define objective functions. Finite element analysis applied for simulating the process. Proposed approach has been investigated on a fuel tank drawing part and it has been observed that it is more effective and accurate than traditional finite element analysis method and the “trial and error” procedure.

[1]  Ruxu Du,et al.  Minimization of the thickness variation in multi-step sheet metal stamping , 2006 .

[2]  Khalil Khalili,et al.  Blank Optimization in Elliptical-Shape Sheet Metal Forming Using Response Surface Model Coupled with Reduced Basis Technique and Finite Element Analysis , 2011 .

[3]  Eiji Nakamachi,et al.  Development of optimum process design system by numerical simulation , 1996 .

[4]  Yang Yuying,et al.  Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm , 2008 .

[5]  Tomoyuki Hiroyasu,et al.  Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model , 2002 .

[6]  Long Chen,et al.  Finite element simulation and model optimization of blankholder gap and shell element type in the stamping of a washing-trough , 2007 .

[7]  Hakim Naceur,et al.  Optimization of drawbead restraining forces and drawbead design in sheet metal forming process , 2001 .

[8]  Robert M. Caddell,et al.  Metal Forming: Contents , 2007 .

[9]  A. Makinouchi,et al.  Sheet metal forming simulation in industry , 1996 .

[10]  Wang Hu,et al.  Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method , 2008 .

[11]  W. Hosford,et al.  Metal Forming: Mechanics and Metallurgy , 1993 .

[12]  Qing Li,et al.  Multiobjective robust optimization method for drawbead design in sheet metal forming , 2010 .

[13]  Y. Liu,et al.  Effect of technological parameter on the process performance for electric discharge milling of insulating Al2O3 ceramic , 2008 .

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

[15]  Hakim Naceur,et al.  Recent developments on the analysis and optimum design of sheet metal forming parts using a simplified inverse approach , 2000 .

[16]  Hakim Naceur,et al.  An Heuristic Optimization Algorithm for the blank shape design of high precision metallic parts obtained by a particular stamping process , 2008 .

[17]  A. Farhang-Mehr,et al.  Entropy-based multi-objective genetic algorithm for design optimization , 2002 .

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

[19]  K. Lewis,et al.  Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method , 2001 .

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

[21]  O. Kayabaşi,et al.  Automated design methodology for automobile side panel die using an effective optimization approach , 2007 .

[22]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..