Flow Stress Prediction Model and Its Application

Author

Wang Yanping ; Wubing

Author_Institution

Sch. of Electr. & Electron. Eng., Shandong Univ. of Technol., Zibo, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

To improved the prediction accuracy of the flow stress, a hybrid model based on the hybrid least squares support vector machine (HLS-SVM) and mathematical models (MM) was proposed. In HLS-SVM model, the optimal parameters of LS-SVM were obtained by self-adaptive particle swarm optimization (PSO) based on simulated annealing (SA). Simulation experiment results revealed that this model could correctly recur to the flow stress in the sample data and accurately predict the non-sample data. The efficiency and accuracy of the predicted flow stress achieved by the proposed model were better than the methods used in most literature.

Keywords

least squares approximations; particle swarm optimisation; plastic flow; simulated annealing; support vector machines; flow stress prediction model; hybrid least squares support vector machine; mathematical models; self-adaptive particle swarm optimization; simulated annealing; Accuracy; Deformable models; Least squares methods; Mathematical model; Particle swarm optimization; Predictive models; Simulated annealing; Support vector machines; Thermal resistance; Thermal stresses;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5363118

Filename

5363118