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Critical to the growth of digital manufacturing is the development of rapid yet accurate quality control technologies to assess the microstructure of each metal part produced. Typical surface analysis methods are limited in measurement throughput and impose constraints on maximum area size and surface quality, which enforce the tedious practice of extracting and preparing flat, small-scale samples for microstructure analysis. Here, we propose a new approach based on directional reflectance microscopy (DRM) which can yield part-scale microstructure information nondestructively and on curved, complex surfaces. We demonstrate our approach on the airfoil of a turbine blade and carry out a rigorous error analysis using other samples with variable surface geometry. Our results highlight the potential for part-specific quality control in the context of digital manufacturing. [ABSTRACT FROM AUTHOR]

Phase-field modeling offers a powerful tool for investigating the electrical control of the domain structure in ferroelectrics. However, its broad application is constrained by demanding computational requirements, limiting its utility in inverse design scenarios. Here, we introduce a machine-learning surrogate to accelerate 3D phase-field modeling of tip-induced electrical switching. By dynamically handling the boundary conditions, the surrogate achieves accurate reproduction of switching trajectories under various tip locations and applied voltages. With stable predictions throughout entire morphological evolution pathways and a relative error inferior to 10% compared to direct solvers, the model efficiently emulates intricate switching sequences. By successfully replicating the boundary conditions, the presented framework strides towards a holistic surrogate for the ferroelectric phase field. With up to 2500-fold speed-ups over classical methods, our approach opens the path for the tractable design of the domain structure and the resolution of realistic inverse problems. [ABSTRACT FROM AUTHOR]

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders. [ABSTRACT FROM AUTHOR]

Mapping grain orientation in crystalline solids is essential to investigate the relationships between local microstructure and crystallography and interpret materials properties. One of the main techniques used to perform these studies is electron backscatter diffraction (EBSD). Due to the limited measurement throughput, however, EBSD is not suitable for characterizing samples with long-range microstructure heterogeneity, nor for building large material libraries that include numerous specimens. We present a machine learning approach for high-throughput crystal orientation mapping, which relies on the optical technique called directional reflectance microscopy. We successfully apply our method on Inconel 718 specimens produced by additive manufacturing, which exhibit complex, spatially-varying microstructures. These results demonstrate that optical orientation mapping on a metal alloy is achievable. Since our method is data-driven, it can be easily extended to different alloy systems produced using different manufacturing processes. [ABSTRACT FROM AUTHOR]

Accelerator-based ion-beam irradiation has been widely used to mimic the effects of neutron radiation damage in nuclear reactors. However, ion radiation is most often monodisperse in the incoming ions' momentum direction, leading to excessive polarization in defect distribution, while the scattering under neutron irradiation is often more isotropic and has less radiation-induced polarization. Mitigation of the excess-polarization as well as the damage non-uniformity artifact might be crucial for making the simulation of neutron radiation by ion-beam radiation more realistic. In this work, a general radiation polarization theory in treating radiation as external polar stimuli is established to understand the natural material responses in different contexts, and the possibility to correct the defect polarization artifact in ion-beam irradiation. Inspired by Magic Angle Spinning in Nuclear Magnetic Resonance, we present a precise sample spinning strategy to reduce the point-defect imbalance effect in ion-beam irradiation. It can be seen that with optimized surface inclination angle and the axis of sample rotation, the vacancy-interstitial population imbalance, as well as the damage profile non-uniformity in a designated region in the target are both reduced. It is estimated that sample spinning frequency on the order of kHz should be sufficient to scramble the ion momentum monodispersity for commonly taken ion fluxes and dose rates, which is experimentally feasible. [ABSTRACT FROM AUTHOR]

Shape memory ceramics, such as yttria-stabilized tetragonal zirconia (YSTZ), offer unique properties including ultra-high operating temperatures and high resistance to oxidation. However, they are susceptible to formation of defects during manufacturing and/or by mechanical deformation. To completely take advantage of their shape memory properties, it is necessary to fully understand the nano-structural evolution of defects under external stimuli. In this study, defect evolution behaviors in YSTZ nanopillars are investigated by atomistic simulations. Two characteristic orientations of 01 1 ¯ and [001] are selected to represent the dominant deformation mechanisms of phase transformation and dislocation migration, respectively. Volume expansion associated with the tetragonal to monoclinic phase transformation is observed to promote healing of crack and void. Atom stress analyses reveal stress concentrations along the newly formed monoclinic phase bands. A critical crack width is identified, less than which the crack can be fully closed in compression. For [001]-oriented YSTZ nanopillars, dislocation migration leads to formations of an amorphous phase, which also assist the crack and void closure process. The revealed crack/void healing mechanisms may provide a path for mitigating internal defects that influences the mechanical properties and deformation mechanisms of shape memory ceramics. [ABSTRACT FROM AUTHOR]

The recent studies on nanotwinned (NT) and hierarchical nanotwinned (HNT) face-centered cubic (FCC) metals are presented in this review. The HNT structures have been supposed as a kind of novel structure to bring about higher strength/ductility than NT counterparts in crystalline materials. We primarily focus on the recent developments of the experimental, atomistic and theoretical studies on the NT and HNT structures in the metallic materials. Some advanced bottom-up and top-down techniques for the fabrication of NT and HNT structures are introduced. The deformation induced HNT structures are available by virtue of severe plastic deformation (SPD) based techniques while the synthesis of growth HNT structures is so far almost unavailable. In addition, some representative molecular dynamics (MD) studies on the NT and HNT FCC metals unveil that the nanoscale effects such as twin spacing, grain size and plastic anisotropy greatly alter the performance of NT and HNT metals. The HNT structures may initiate unique phenomena in comparison with the NT ones. Furthermore, based on the phenomena and mechanisms revealed by experimental and MD simulation observations, a series of theoretical models have been proposed. They are effective to describe the mechanical behaviors of NT and HNT metals within the applicable scope. So far the development of manufacturing technologies of HNT structures, as well as the studies on the effects of HNT structures on the properties of metals are still in its infancy. Further exploration is required to promote the design of advanced materials. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in developing truly bottom-up models for the performance of polycrystalline materials is the lack of robust quantitative structure–property relationships for interfaces. As a first step in analyzing such relationships, we present a polyhedral unit model to classify the geometrical nature of atomic packing along grain boundaries. While the atomic structure in disordered systems has been a topic of interest for many decades, geometrical analyses of grain boundaries has proven to be particularly challenging because of the wide range of structures that are possible depending on the underlying macroscopic crystallographic character. In this article, we propose an algorithm that can partition the atomic structure into a connected array of three-dimensional polyhedra, and thus, present a three-dimensional polyhedral unit model for grain boundaries. A point-pattern matching algorithm is also provided for quantifying the distortions of the observed grain boundary polyhedral units. The polyhedral unit model is robust enough to capture the structure of high-Σ, mixed character interfaces and, hence, provides a geometric tool for comparing grain boundary structures across the five-parameter crystallographic phase-space. Since the obtained polyhedral units circumscribe the voids present in the structure, such a description provides valuable information concerning segregation sites within the grain boundary. We anticipate that this technique will serve as a powerful tool in the analysis of grain boundary structure. The polyhedral unit model is also applicable to a wide array of material systems as the proposed algorithm is not limited by the underlying lattice structure. Polyhedral structures: Building blocks for metallic interfaces US researchers have designed a model to aid understanding of how crystals pack in metals such as aluminum and copper. Most metals form as polycrystalline microstructures, i.e., they are composed of small crystals of varying size and orientation. The lack of structural order, along the interfaces where crystals of different orientations meet, makes it difficult to model their properties. Arash Banadaki and Srikanth Patala at North Carolina State University have taken the first step towards quantifying these relationships, by developing a three-dimensional model that classifies the geometrical nature of how atoms pack along these disordered regions in the microstructure. The model was tested on face-centered cubic crystals, but is also applicable to systems with different lattice structures. This is a key first step in the bottom-up design of structural materials with desired properties. [ABSTRACT FROM AUTHOR]