A micromechanical analysis of intergranular stress corrosion cracking of an irradiated austenitic stainless steel
arXiv:2012.10198 · doi:10.1016/j.actamat.2020.116482
Abstract
Irradiation Assisted Stress Corrosion Cracking (IASCC) is a material degradation phenomenon affecting austenitic stainless steels used in nuclear Pressurized Water Reactors (PWR), leading to the initiation and propagation of intergranular cracks. Such phenomenon belongs to the broader class of InterGranular Stress Corrosion Cracking (IGSCC). A micromechanical analysis of IGSCC of an irradiated austenitic stainless steel is performed in this study to assess local cracking conditions. A 304L proton irradiated sample tested in PWR environment and showing intergranular cracking is investigated. Serial sectioning, Electron BackScatter Diffraction (EBSD) and a two-step misalignment procedure are performed to reconstruct the 3D microstructure over an extended volume, to assess statistically cracking criteria. A methodology is also developed to compute Grain Boundary (GB) normal orientations based on the EBSD measurements. The statistical analysis shows that cracking occurs preferentially for GB normals aligned with the mechanical loading axis, but also for low values of the Luster-Morris slip transmission parameter. Micromechanical simulations based on the reconstructed 3D microstructure, FFT-based solver and crystal plasticity constitutive equations modified to account for slip transmission at grain boundaries are finally performed. These simulations rationalize the correlation obtained experimentally into a single stress-based criterion. The actual strengths and weaknesses of such micromechanical approach are discussed.
References in corpus (4)
- A numerical method for computing the overall response of nonlinear composites with complex microstructure
- Review on Slip Transmission Criteria in Experiments and Crystal Plasticity Models
- Intergranular stress distributions in polycrystalline aggregates of irradiated stainless steel
- Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm