NDLI: Research on the prediction of reduction ratio for pore closure in heavy forging stock based on particle swarm optimization and support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Mei Yi Chen Wei
Copyright Year	2010
Description	Author affiliation: College of Mechanical Engineering and Automation, Guizhou University, Guiyang 550003, China (Mei Yi; Chen Wei)
Abstract	At present, the prediction of reduction ratio for pore closure in heavy forgings stock is limited to the finite element numerical simulation and complex mathematical formula. To make it fit in forgings application, it is proposed that a new forecasting method by using the SVM model to predict the critical reduction ratio of pore closure. Firstly, it is selected that several major factors which affecting pore closure as the input features of SVM. Then, it is optimized that SVM's nuclear parameters with Particle Swarm Optimization in order to enhance its accuracy. Finally, a SVM model is trained by combining with LIB-SVM toolbox. The model can predict the critical reduction ratio of pore closure in heavy forging quickly in compared with computer simulation results, the correlation coefficient reached almost 85%, and it had a good prediction performance.
Starting Page	345
Ending Page	348
File Size	404781
Page Count	4
File Format	PDF
ISBN	9781424477050
e-ISBN	9781424477067
DOI	10.1109/CINC.2010.5643824
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Predictive models Numerical simulation Mathematical model Particle swarm optimization
Content Type	Text
Resource Type	Article

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