Multiaxial fatigue life prediction method based on the back-propagation neural network

• A multiaxial fatigue parameter was proposed considering additional hardening. • BPNN was used as an alternative to the traditional physical model. • A simplified combination parameter σ b /E was chosen for life prediction. • Effectiveness of the new method was verified by eight materials. The study was devoted to developing a reliable multiaxial fatigue life prediction method with the effect of additional cyclic hardening considered. Based on the original assumptions of traditional critical plane methods, a new characteristic plane (subcritical plane) was defined to describe the particularity of additional cyclic hardening under non-proportional loading condition. On the new defined subcritical plane, a new multiaxial fatigue damage control parameter containing the effect of additional hardening was also built, by which the dynamic path of stress spindle, combining material property and loading environment, was fully analysed. In addition, a multiaxial fatigue life prediction back-propagation neural network (BPNN), as an alternative to the traditional physical model, was proposed to calculate the fatigue life under multiaxial loading. To calculate the multiaxial fatigue life of different materials, a more simplified combination parameter σ b /E for different types of materials was chosen as input parameter in BPNN training. The availability of the proposed method was validated by reasonable correlations with experimental data of six alloy steel materials and two Non alloy steel materials under diverse loading paths.