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The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA .

Carbon-based solid lubricants are excellent options to reduce friction and wear, especially with the carbon capability to adopt different allotropes forms. On the macroscale, these materials are sheared on the contact along with debris and contaminants to form tribolayers that govern the tribosystem performance. Using a recently developed advanced Raman analysis on the tribolayers, it was possible to quantify the contact-induced defects in the crystalline structure of a wide range of allotropes of carbon-based solid lubricants, from graphite and carbide-derived carbon particles to multi-layer graphene and carbon nanotubes. In addition, these materials were tested under various dry sliding conditions, with different geometries, topographies, and solid-lubricant application strategies. Regardless of the initial tribosystem conditions and allotrope level of atomic ordering, there is a remarkable trend of increasing the point and line defects density until a specific saturation limit in the same order of magnitude for all the materials tested.

The interactions of moving dislocations with various point, line and planar defects during hot working of polycrystalline face-centred cubic alloys are studied. Experimental parameters from three austenitic steels and a Ni-base superalloy are integrated with a dislocation model within a multi-scale framework. The combination of these inputs is used to explain the microstructure evolution and flow behaviour through atomic-scale phenomena and their manifestation at higher length scales.

The effects of grain size, source density, and misorientations on the dislocation configurational energy area density are investigated using two-dimensional discrete dislocation plasticity. Grain boundaries are modeled as impenetrable to dislocations. The considered grain size ranges from 0.4 μ m 2 to 8.0 μ m 2 . The configurational energy area density displays a strong size dependence, similar to the stress response. Two sets of materials are considered, with low and high source/obstacle density. The high-source-density specimens exhibit negative configurational energy, implying that the dislocation structure is more stable than for isolated dislocations . The contribution of misorientation to the configurational energy density is analyzed using specimens with a single orientation or a checkerboard arrangement. The configurational energy density is found not only to depend on the dislocation spacing but also to be related to the local stress states. Low source densities lead to higher (positive) configurational energy densities.

The mechanical response of Ni-based single-crystal superalloys is known to be sensitive to the microstructural state, i.e., the shape and size of the γ ′ precipitates when exposed to high-temperature conditions. The magnitude and sign of the natural lattice misfit between the γ and γ ′ phases play the most crucial role in establishing a controlled size, shape, and distribution of γ ′ precipitates during heat treatments as well as in defining the direction of rafting, viz. the directional coalescence of the γ ′ precipitates. In this study, a bottom-up scale bridging strategy of using phase-field informed finite-element (FE) crystal plasticity on realistic microstructures is followed to better understand the effect of the microstructural state on the macro-scale performance of a 001 -oriented Ni-based single-crystal superalloy. Strain-controlled tensile tests using FE crystal plasticity were performed on a set of different microstructural states: cuboidal, rafted, and topologically inverted imported from 3D phase-field simulations. The study revealed that a cuboidal microstructure with a natural lattice misfit of − 0.004 is the most ductile. As observed experimentally, the microstructure with rafts perpendicular to the loading axis ( N -type) is more ductile than the cuboidal one. The P -type microstructure, i.e., with rafts parallel to the loading axis, is found to have the lowest ductility, which was attributed to lesser dislocation mobility.