Deformation and damage mechanisms of ODS steels under high-temperature cyclic loading

The aggressive operating conditions of future nuclear power plants including generation IV fission and fusion reactors will be beyond those experienced in current nuclear power plants. Hence, the high irradiation resistance, the high creep resistance as well as the high fatigue strength are the main material properties that will be required to build future reactors with enhanced efficiency and safety. Due to their good resistance to swelling under irradiation and their improved mechanical properties, oxide dispersion strengthened (ODS) steels are promising structural material candidates. Nevertheless, a clear understanding of their deformation and damage mechanisms under various loading conditions (especially cyclic loading) are still lacking. In this scope, this work has been performed to obtain a better description of the deformation and damage mechanisms of a tempered martensitic Fe-9%Cr based ODS steel. This includes understanding of its monotonic behavior by testing under tensile loading, pure-fatigue/continuous cycling (PF/CC) response by examining within low-cycle fatigue (LCF) regime and creep-fatigue (CF) behavior by introducing tensile hold-time in PF/CC waveform. Prior to testing, comprehensive description of the microstructure in undeformed state is necessary, which was uncovered by means of electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). Thereafter, tensile tests were performed within the temperature range varying from room temperature (RT) to 800 °C at the nominal strain rates of 10-3 s-1 and 10-4 s-1. The obtained results were analyzed to visualize the influence of temperature, strain rate and applied heat treatment. While the resulting microstructural evolution was characterized via TEM, the fracture surface investigations were carried by using scanning electron microscope (SEM). In addition, various active strengthening mechanism\u27s contributions to measured RT yield stress were estimated and compared. Comparisons were also made with respect to the strength and ductility of the similar non-ODS as well as other ODS steels reported in literature. High-temperature PF/CC behavior was delineated by performing fully reversed strain-controlled LCF tests (using nominal strain rate of 10-3 s-1 and triangular waveform with R = -1) in air at 550 °C and 650 °C for different strain amplitude values ranging from ± 0.4% to ± 0.9%. The cyclic stress-strain and strain-life relationships were obtained through the test results, and related LCF parameters were calculated. Postmortem microstructural investigations were carried out using both EBSD and TEM to shed light on the active deformation mechanisms. To explore damage mechanisms, fatigue crack initiation/propagation as well as fracture characteristics were examined via SEM. Lastly, thorough comparison of the measured cyclic stress response and lifetime were made with that of the similar non-ODS as well as other ODS steels tested or taken from literature. Finally, CF interaction was studied at 650 °C by introducing hold-time of up to 30 min at peak tensile strain of 0.7%. The observed cyclic stress response and lifetime were then compared with that obtained under PF/CC waveform. The additional deformation and damage modifications brought by introducing creep into the PF/CC waveform were scrutinized. Here, EBSD and TEM were used to compare microstructural evolution. Whereas, damage modifications in terms of important observations from the fatigue-cracked specimen surfaces, cross-sections and fracture surfaces were examined via SEM

Data-driven machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys

Additive manufacturing (AM) of multi-principal element alloys (MPEAs) has gained significant attention in recent years. However, the intricate nature of phenomena such as rapid solidification, heat gradients, and residual stresses presents challenges in controlling the properties of printed components. To overcome these challenges, this study utilized machine learning (ML) approach to investigate the correlations between composition, processing parameters, testing conditions, and yield strength of single-phase MPEAs within the CoCrFeMnNi system, produced via laser-melt deposition and laser powder-bed fusion. Multiple algorithms, including Random Forest, Gradient Boosting, and Extreme Gradient Boosting, were trained, and tested. SHapley Additive exPlanations algorithm was employed to analyze the contributions of input features. All models exhibited reasonable accuracy, with Random Forest performing the best. The impact of data sparsity was examined, and minimal sensitivity to data splitting was observed. Notably, the research yielded valuable insights into the key features influencing the yield strength of MPEAs, showcasing the potential of ML in accurately modeling the material properties of additively manufactured components

Effective and back stresses evolution upon cycling a high-entropy alloy

We report on the effective and back stresses evolution of a CoCrFeMnNi high-entropy alloy (HEA) by partitioning its cyclic hysteresis loops. It was found that the cyclic stress response of the HEA predominantly originates from the back stress evolution. Back stress also increases significantly with increasing strain amplitude and reducing grain size. However, the change of effective stress is rather insignificant with altering cycle number, strain amplitude and grain size. This indicates that the effective stress is determined mainly by the lattice friction. Further comparisons to an austenitic steel and a medium-entropy alloy identified the origins of their peculiar cyclic strength. The effective stress and back stress upon cycling a HEA are assessed, both of which are higher than a conventional FCC steel, contributing to the HEA’s higher cyclic strength

Towards reducing tension-compression yield and cyclic asymmetry in pure magnesium and magnesium-aluminum alloy with cerium addition

In this study, we report the effect of cerium (Ce) addition on the tension-compression yield and cyclic asymmetry in commercially pure magnesium (Cp-Mg) and Mg-Al alloy at room temperature (RT). The materials examined include extruded and annealed Cp-Mg, Mg-0.5Ce, and Mg-3Al-0.5Ce alloys. Incorporation of 0.5wt.% Ce in pure Mg results in the weakening of its basal texture, uniform distribution of Mg12Ce precipitates, and refinement of the grain size. Consequently, the tensile yield strength and ductility of pure Mg increase, and tension-compression yield asymmetry is eliminated. However, the presence of 3wt.% Al in Mg suppresses the beneficial effects of Ce addition. The formation of complex precipitates, such as Mg-Al-Ce and Al11Ce3, limits the weakening of the basal texture, reduction in grain size, improvement in ductility, and elimination of tension-compression yield asymmetry observed in Mg-0.5Ce. Nevertheless, Al contributes to the solid solution strengthening in Mg, resulting in the highest tensile yield strength of Mg-3Al-0.5Ce. Finally, the addition of 0.5wt.% Ce enhances the cyclic strength, stabilizes cyclic stress response, reduces inelastic strain, and minimizes cyclic asymmetry in both pure Mg and Mg-Al alloy while maintaining a comparable fatigue life. Overall, Ce addition positively impacts the microstructure and mechanical behavior of pure Mg and its investigated alloy. The reasons for these improvements are discussed in detail

Text Extraction from Natural Images of Different Languages Using ISEF Edge Detection

In this paper, we proposed the algorithm text extraction of different images of languages. In computer vision research area, text is very important in images. Here, we use edge based extraction of text using ISEF (infinite symmetrical edge filter). ISEF is optimal edge detector which gives accurate results for text in images. Text extraction involves detection, localization, tracking and enhancement. Large numbers of technique have been proposed for the text extraction. Our aim is to present robust technique for text extraction of different languages images

Saccade Velocity Driven Oscillatory Network Model of Grid Cells

Grid cells and place cells are believed to be cellular substrates for the spatial navigation functions of hippocampus as experimental animals physically navigated in 2D and 3D spaces. However, a recent saccade study on head fixated monkey has also reported grid-like representations on saccadic trajectory while the animal scanned the images on a computer screen. We present two computational models that explain the formation of grid patterns on saccadic trajectory formed on the novel Images. The first model named Saccade Velocity Driven Oscillatory Network -Direct PCA (SVDON—DPCA) explains how grid patterns can be generated on saccadic space using Principal Component Analysis (PCA) like learning rule. The model adopts a hierarchical architecture. We extend this to a network model viz. Saccade Velocity Driven Oscillatory Network—Network PCA (SVDON-NPCA) where the direct PCA stage is replaced by a neural network that can implement PCA using a neurally plausible algorithm. This gives the leverage to study the formation of grid cells at a network level. Saccade trajectory for both models is generated based on an attention model which attends to the salient location by computing the saliency maps of the images. Both models capture the spatial characteristics of grid cells such as grid scale variation on the dorso-ventral axis of Medial Entorhinal cortex. Adding one more layer of LAHN over the SVDON-NPCA model predicts the Place cells in saccadic space, which are yet to be discovered experimentally. To the best of our knowledge, this is the first attempt to model grid cells and place cells from saccade trajectory