NDLI: Prediction and optimization on springback and process parameters of S-Rail forming

Content Provider	IEEE Xplore Digital Library
Author	Cheng Lei Zhang Wei Lu Bao Chun Song Yong Zheng Ding Yu Lan
Copyright Year	2010
Description	Author affiliation: Nantong Metal forming equipment Co. Ltd., China (Song Yong Zheng; Ding Yu Lan) \|\| School of Mechanical Engineering, Nanjing University of Science and Technology, China (Cheng Lei; Zhang Wei; Lu Bao Chun)
Abstract	In stamping process, springback is always determined by process parameters, such as blank-holder force, mould parameters, material parameters, and so on. Prediction of springback and parameters is a multi-objective optimization problem. Firstly, based on the same quantity of orthogonal experimental samples, prediction accuracy and efficiency of back propagation neural network (BPNN) prediction model and the response surface prediction model (RSPM) for springback of S-Rail forming were compared. As a result, RSPM was adopted benefit to less influence by sample scale and higher accuracy. Furthermore, a self-adaptive global optimizing of probability search algorithm, neighborhood cultivation genetic algorithm (NCGA) was proposed to optimize the prediction of process parameters. Then optimized parameters can be obtained quickly. Finally, valid of optimized parameters set, as well as the feasible of the prediction model based on both RSPM and NCGA were confirmed by the finite element analysis (FEA) test of S-Rail springback.
Starting Page	3822
Ending Page	3826
File Size	386559
Page Count	5
File Format	PDF
ISBN	9781424477371
e-ISBN	9781424477395
DOI	10.1109/MACE.2010.5536024
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models S-Rail forming Design for experiments Response surface methodology Finite element methods Parameter optimization Mechanical engineering Neighborhood cultivation genetic algorithm Genetic algorithms Materials science and technology Springback prediction Neural networks Response surface prediction model Concrete Back propagation neural network Testing
Content Type	Text
Resource Type	Article

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