The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.
The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed.
The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly.
A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.
The supports given to this project by the National Natural Science Foundation of China (51375500, 51375162); Scientific Research Project of Hunan Province Department of Education (17C0886); and Open Funded Projects of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment (201605) are gratefully acknowledged.
Yang, D., Liu, Y., Li, S., Tao, J., Liu, C. and Yi, J. (2017), "Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm", Engineering Computations, Vol. 34 No. 4, pp. 1034-1053. https://doi.org/10.1108/EC-11-2015-0362
Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited