Abstract
In order to estimate the residual life of waste drive axle housing, the prediction model of waste axle housings with artificial neural networks is built in this paper. Take the deformation, residual stress and the gradient of magnetic intensity Kmax relating to axle housing’s fatigue damage degree as the input of neural network, and compare the testing residual life of the waste drive axle housing with its predicting residual life. The result demonstrates that: the deformation, residual stress and the gradient of magnetic intensity Kmax of axle housing as the characteristic parameter estimating the degree of fatigue damage, adopting trainbr training function can get good network performance and comparatively high precision of prediction. Besides, the longer the residual life of the waste axle housing is, the more precise the prediction life will be.
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