Springback compensation for multi-curvature part based on multi-objective optimization of fuzzy genetic algorithm

Author

Liu, Wenjuan ; Liang, Zhiyong

Author_Institution

Dept. of Comput. Sci., Zhaoqing Univ., Zhaoqing, China

fYear

2009

fDate

17-19 June 2009

Firstpage

3659

Lastpage

3664

Abstract

Springback for multi-curvature part is a very important factor influencing the quality of sheet metal forming. Accurate calculation and controlling of springback are essential for the design of tools for sheet metal forming. In this paper, a springback quick compensation model is proposed to solve the problem of springback, which is based on fuzzy optimization improved GA-ANN algorithm and sheet metal forming springback experiment of multi-curvature part. The springback test results indicate that the springback compensation and analysis based on fuzzy optimization GA-ANN model are practical and reasonable. Springback calculation results with some precision can be achieved. It can be taken as a reference for sheet metal forming tool design and controlling of springback.

Keywords

forming processes; fuzzy set theory; genetic algorithms; neural nets; production engineering computing; sheet metal processing; GA-ANN algorithm; fuzzy genetic algorithm; fuzzy optimization GA-ANN model; multicurvature part; multiobjective optimization; sheet metal forming springback experiment; springback analysis; springback compensation; springback quick compensation model; Artificial neural networks; Automotive engineering; Computer science; Fuzzy neural networks; Genetic algorithms; Neural networks; Optimization methods; Predictive models; Sheet materials; Testing; Springback; fuzzy optimization; genetic algorithm; multi-curvature part; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192370

Filename

5192370