| Abstract [eng] |
Under cyclic loading, changes occur in the material microstructure, which lead to the degradation of mechanical properties. When designing structures, it is essential to account for potential material aging under cyclic loading conditions. This is particularly important in the design of safety-critical and environmentally hazardous systems, such as those used in nuclear power engineering, aviation, transportation, medical devices, and other high-reliability applications. Therefore, understanding material behaviour under cyclic loading is crucial, and it is especially important in the case of low-cycle fatigue, where structures undergo significant plastic deformation. Consequently, substantial attention is devoted to scientific investigations of low-cycle fatigue behaviour in materials. In this study, experimental investigations and numerical modelling of the low-cycle fatigue behaviour of steel were performed. Low-cycle fatigue tests were carried out on stainless steels AISI 304L and AISI 316L. Specimen loading was controlled using both strain-controlled and force-controlled regimes. The experiments were conducted at room temperature and at an elevated temperature of 300 °C, using solid specimens, hollow specimens with flowing water at 300 °C, and notched specimens. Experimental results were used to validate the numerical modelling outcomes. In modelling the elastic–plastic behaviour of the investigated steels under low-cycle fatigue loading, both kinematic and isotropic hardening mechanisms were considered. The parameters of the kinematic hardening material model were identified based on experimental data obtained at different strain amplitudes and temperatures. Based on the experimental results, relationships between the kinematic hardening parameters, strain amplitude, and operating temperature were established. These relationships were then employed in low-cycle fatigue simulations under various loading conditions and temperatures. The deviation between numerical simulation results and experimental data did not exceed 10%. The proposed relationships can be applied to predict the low-cycle fatigue life of steel under constant-amplitude, fully reversed loading conditions. |
