Cyclic plasticity modelling of austenitic steels with complex behavior
Advances in Engineering
Structural components are susceptible to failure under different loading conditions. Therefore, it is important to understand the different causes of structural failures so as to enable the optimization and design of long-lasting structural components. Steel is the widely used material for various applications globally due to its excellent properties such as toughness, corrosion resistance and stiffness. Unfortunately, cyclic behavior prediction of structural materials is not fully explored. Presently, several cyclic models have been developed to describe the cyclic behaviors of materials and structures. For instance, non-linear kinematic rules were proposed to enhance ratcheting effect description as well as extending it to cyclic plastic modelling analysis. Generally, austenitic steels are favorable for analysis of cyclic behavior under low cycle fatigue thus attracting interest among researchers. Cyclic modeling of 316L steels comprises of ratcheting response, hardening and strain range memory. For 316L steels, pre-hardening effect and the cyclic behavior have been investigated based on the microstructural analysis. Therefore, back stress is the main cause of strain memory effect, particularly during uniaxial cyclic loading. These stresses are associated with the interaction of the short-range and long-range dislocations. This calls for a great need for a better description of microstructural evolution during cyclic deformation. A group of researchers at the University of Technology of Troyes: Dr. Jianqiang Zhou, Dr. Zhidan Sun, Dr. Pascale Kanouté and Prof. Delphine Retraint performed an experimental analysis and cyclic modeling of 316L austenitic stainless steels in the low cycle fatigue region. Their research work is published in the research journal, International Journal of Plasticity. Briefly, the authors commenced their experimental work by performing low cyclic fatigue tests on two steel samples at room temperature and strain amplitudes in the range of ±0.3% to ±1.5%. They then investigated the cyclic behavior and properties of the materials including Young’s modulus and yield point. The cyclic softening and hardening effects in the back stress were incorporated using the nonlinear kinematic hardening rules with an introduced multiplicative coefficient. The material parameters were identified based on the experimental results of the steel samples. Eventually, the simulation and experimental results were compared to validate the effectiveness of the model. The research team observed that under low strain amplitudes, the two samples underwent hardening followed by long-range softening. However, for the 316L-B sample, secondary hardening was achieved under relatively high strain amplitudes. Additionally, the two steel samples showed strain range memory effect, particularly during cyclic loading. This was attributed to the back stress that influenced hardening, softening and strain range memory effect. In a nutshell, University of Technology of Troyes scientists successfully developed and implemented a cyclic constitutive model to predict the cyclic behavior of steel samples throughout the entire cyclic loading. The accuracy and feasibility of the method were validated by the similarities in the experimental and simulations results of the austenitic 316L-A steel which also proved suitable for material analysis in the low cyclic fatigue regime. The method is versatile and thus can be extended to investigate other material features. This will advance the design of efficient structural materials for various applications.