Journal of Northeastern University:Natural Science ›› 2015, Vol. 36 ›› Issue (9): 1321-1326.DOI: 10.3969/j.issn.1005-3026.2015.09.023

• Mechanical Engineering • Previous Articles     Next Articles

Fatigue Life Prediction of Large-Span Samples Based on the Optimized SVR Model

YANG Da-lian1, LIU Yi-lun1,2, ZHOU Wei1, YI Jiu-huo1   

  1. 1.School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; 2. Light Alloy Research Institute, Central South University, Changsha 410083, China.
  • Received:2014-02-17 Revised:2014-02-17 Online:2015-09-15 Published:2015-09-14
  • Contact: LIU Yi-lun
  • About author:-
  • Supported by:
    -

Abstract: Aiming at the issue that the prediction accuracy of fatigue life is not high by the traditional methods with large-span and small samples, a new life prediction method based on the optimized SVR model was studied. Considering the traits of large-span samples, the effective sample pretreatment method, the training method for the SVR model and the criterion for parameter optimization were put forward. Taking the life prediction of LY12CZ (2A12) aluminum alloy for example, the effects of the kernel functions of Gauss, polynomial and multilayer perception on the training error of the SVR model were analyzed. The results showed that the Gaussian kernel function is more suitable for SVR model training and the kernel function parameter γ and the penalty factor C can be optimized by the bacterial foraging algorithm. Thus, the life prediction results verify the validity of this method.

Key words: large-span, support vector regression (SVR), fatigue, life prediction, aluminum alloy

CLC Number: