Intelligent model of material fatigue cumulative damage based on neural networks

The fatigue cumulative damage of material is impacted by many factors and regarded as an unstable process. The relationships between its momentary fatigue damage and material properties, loading stress behave so complicate and no-linear that it is very difficult to depict the process precisely by applying definitive mathematical functions. The paper sets up a intelligent model of material fatigue damage and accumulation based on neural network, it can compensate the errors of damage parameters. So that the model can describes the real transition procedure of material fatigue damage, and it improves the accuracy of analyzing fatigue life. The test result has proved that the analysis accuracy is very high for normalizing 35 steel under random loads.