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Prediction model for fatigue life considering microstructures of steel

2016; Elsevier BV; Volume: 2; Linguagem: Inglês

10.1016/j.prostr.2016.06.322

2452-3216

Koya Ueda, Kazuki Shibanuma, Masao KINEFUCHI, Yoshiki Nemoto, Katsuyuki Suzuki, Manabu Enoki,

Microstructure and Mechanical Properties of Steels

In fatigue life, crack initiation and crack propagation is considered separately. Behavior of large crack propagation is explained on the Paris equation. However, there is no model to simulate the behavior from the crack initiation to the large crack propagation. One of this cause is that fatigue life can varies greatly thanks to material microstructures and manufacturing and change of stress. Especially to think the effect of material microstructures is important in materials development. However, there is no model considering quantitative effect of material microstructures. We made prediction model for fatigue life considering material microstructure. This model is for the ferrite-pearlite, most popular steel for the structure. Using FEM analysis, this model gets stress on the specimen and divides surface of the test piece into small squares, and fills the squares with grains using Monte Carlo method based on distribution of the grain size. The model gives each grains crystal orientation randomly. In each squares, from the stress and the crystal orientation, this model judges the initiation of the crack on grain. This model simulates the propagation of small crack from the crack nucleation. From the stress and the crystal orientation and the interaction between the slip band and the grain boundary, this model simulates the propagation of the small crack thinking the interaction with grain boundaries. To estimate exact fatigue life, this model needs the parameter of the small crack propagation. This parameter is inherent to each material. We conducted some fatigue experiments and observed the propagation of the crack in detail. We conducted fatigue experiments with another distribution of grain size to validate our model and we got good agreement with the experiments and the model.