Optimization method of combined high and low cycle fatigue P-S S-N curve for aero-engine materials with small size sample

The P-S-N curve of high and low cycle fatigue for aero-engine materials are essential to evaluate the service life of rotor components. However, it requires extensive experimental time and high material cost. Based on the physical-informed machine learning (PIML ) method, a novel optimization method was proposed for combined high and low-cycle fatigue P-S-N curves with a small size sample, in which the equivalent principle of fatigue life and the consistency criteria of life distributions were introduced into the extreme learning machine(ELM) (ELM ) through its loss function. In addition, bi-level optimization was employed with the upper level of model input variables and the lower level of the ELM model parameters. Subsequently, the proposed PIML method was compared with a data-driven machine learning method and traditional P-S-N curve fitting methods through the fatigue test data. The results show that the method not only effectively solves the problem of nonlinearity between the stress level and the standard deviation of fatigue life, but also presents the highest accuracy of the predicted probabilistic lives.