VHCF evaluation with BP neural network for centrifugal impeller material affected by internal inclusion and GBF region

Abstract

The application of back propagation neural network (BP neural network) in very-high cycle fatigue life evaluation of centrifugal impeller to explore the effect of internal inclusion and granular bright facet (GBF) region on the fatigue life is a key and potential issue. Numerical simulation analysis of centrifugal impeller is carried out in this study to clear the mechanical state of centrifugal impeller in operation condition. Then, the designed very-high cycle fatigue test is conducted out; the test data and fracture morphology are analyzed to reveal the effect of internal inclusion and GBF region on the fatigue failure and life distribution. Then, with the comprehensive application of BP neural network, the fatigue life with different input parameters are predicted. In the case of different input parameters, the prediction changed and the very-high cycle fatigue life with the consideration of both internal inclusion and GBF region is very satisfactory. Study on neural network fatigue life prediction approach of centrifugal impeller in VHCF affected by internal inclusion and GBF region is novel for the further fatigue study in theoretical research and engineering practice for mechanical component and engineering metallic material.