ABSTRACTRecent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image‐processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two‐dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two‐point correlation functions, and local thicknesses. The FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. We explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

Fatigue in metals involves gradual failure through incremental propagation of cracks under repetitive mechanical load. In structural applications, fatigue accounts for up to 90% of in-service failure 1 , 2 . Prevention of fatigue relies on implementation of large safety factors and inefficient overdesign 3 . In traditional metallurgical design for fatigue resistance, microstructures are developed to either arrest or slow the progression of cracks. Crack growth is assumed to be irreversible. By contrast, in other material classes, there is a compelling alternative based on latent healing mechanisms and damage reversal 4 – 9 . Here, we report that fatigue cracks in pure metals can undergo intrinsic self-healing. We directly observe the early progression of nanoscale fatigue cracks, and as expected, the cracks advance, deflect and arrest at local microstructural barriers. However, unexpectedly, cracks were also observed to heal by a process that can be described as crack flank cold welding induced by a combination of local stress state and grain boundary migration. The premise that fatigue cracks can autonomously heal in metals through local interaction with microstructural features challenges the most fundamental theories on how engineers design and evaluate fatigue life in structural materials. We discuss the implications for fatigue in a variety of service environments.

Powders under compression form mesostructures of particle agglomerations in response to both inter- and intra-particle forces. The ability to computationally predict the resulting mesostructures with reasonable accuracy requires models that capture the distributions associated with particle size and shape, contact forces, and mechanical response during deformation and fracture. The following report presents experimental data obtained for the purpose of validating emerging mesostructures simulated by discrete element method and peridynamic approaches. A custom compression apparatus, suitable for integration with our micro-computed tomography (micro-CT) system, was used to collect 3-D scans of a bulk powder at discrete steps of increasing compression. Details of the apparatus and the microcrystalline cellulose particles, with a nearly spherical shape and mean particle size, are presented. Comparative simulations were performed with an initial arrangement of particles and particle shapes directly extracted from the validation experiment. The experimental volumetric reconstruction was segmented to extract the relative positions and shapes of individual particles in the ensemble, including internal voids in the case of the microcrystalline cellulose particles. These computationally determined particles were then compressed within the computational domain and the evolving mesostructures compared directly to those in the validation experiment. The ability of the computational models to simulate the experimental mesostructures and particle behavior at increasing compression is discussed.

processing the reorganized data. Other portions of the multi-dimensional array store other portions of the raw imaging data or the encoded imaging data. The computer also processes the reorganized data using the convolutional neural network to construct the image, whereby a constructed image is formed.

Cladding and structural materials for nuclear reactors must withstand elevated temperatures, energetic particle fluxes, and sustained mechanical loading, while maintaining mechanical strength and integrity. To address this experimental need for new materials, an accelerator end-station has been developed that mates a micro tensile test frame with a 6 MV Van de Graaff Tandem accelerator. This allows ion species with energies up to 100 MeV to strike a specimen during simultaneous heating up to 1200 ℃, uniaxial loading up to 450 kg, or cyclic loading at rates of up to 50 Hz. The ability to perform temperature-controlled tensile, creep, and high-cycle fatigue experiments during ion implantation or irradiation will provide a deeper understanding of the deformation response of materials under various combinations of extreme environments. The capabilities of this facility are demonstrated by inducing irradiation-enhanced creep in coarse-grained copper and suppressing failure during fatigue loading by simultaneous irradiation in nanocrystalline Ni-Fe.

Microstructures and corresponding properties of compacted powders ultimately depend on the mechanical response of individual particles. In principle, computational simulations can predict the results of powder compaction processes, but the selection of appropriate models for both particle–particle interactions and particle deformations across all relevant length scales remain nontrivial tasks, especially in material systems lacking detailed mechanical property information. The work presented here addresses these issues by conducting uniaxial compressions in situ inside of a scanning electron microscope to characterize the mechanical response of individual micron-sized particles of a molecular crystal, hexanitrohexaazaisowurtzitane (CL-20). This experimental approach enabled the collection of quantitative force and displacement data alongside simultaneous imaging to capture morphology changes. The results reveal information about elastic deformation, yield, plastic deformation, creep, and fracture phenomena. Accordingly, this work demonstrates a generalizable approach for assessing the mechanical response of individual micron-sized molecular crystal particles and utilizing those responses in particle-level models.

The peridynamic theory of solid mechanics is applied to modeling the deformation and fracture of micrometer-sized particles made of organic crystalline material. A new peridynamic material model is proposed to reproduce the elastic–plastic response, creep, and fracture that are observed in experiments. The model is implemented in a three-dimensional, meshless Lagrangian simulation code. In the small deformation, elastic regime, the model agrees well with classical Hertzian contact analysis for a sphere compressed between rigid plates. Under higher load, material and geometrical nonlinearity is predicted, leading to fracture. The material parameters for the energetic material CL-20 are evaluated from nanoindentation test data on the cyclic compression and failure of micrometer-sized grains.

Nanoindentation provides a convenient and high-throughput means for mapping mechanical properties and for measuring the strain rate sensitivity of a material. Here, nanoindentation was applied to the study of microcrystalline cellulose. Constant strain rate nanoindentation revealed a depth dependence of nanohardness and modulus, mostly attributed to material densification. Nanomechanical maps of storage modulus and hardness resolved the shape and size of voids present in larger particles. In smaller, denser particles, however, where storage modulus varied little spatially, there was still some spatial dependence of hardness, which can be explained by cellulose's structural anisotropy. Additionally, hardness changed with the indentation strain rate in strain rate jump tests.The resulting strain rate sensitivity values were found to be in agreement with those obtained by other techniques in the literature.

This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.

Particle characteristics can drastically influence the process-structure-property-performance aspects of granular materials in compression. We aim to computationally simulate the mechanical processes of stress redistribution in compacts including the kinematics of particle rearrangement during densification and particle deformation leading to fragmentation. Confined compression experiments are conducted with three sets of commercial microcrystalline cellulose particles nearly spherical in shape with different mean particle size. Experimentally measured compression curves from tall powder columns are fitted with the Kenkre et al. (J. of American Chemical Society, Vol. 79, No. 12) model. This model provides a basis to derive several common two-parameter literature models and as a framework to incorporate statistical representations of critical particle behaviors. We focus on the low-stress compression data and the model comparisons typically not discussed in the literature. Additional single particle compressions report fracture strength with particle size for comparison to the apparent particle strength extracted from bulk compression data.

Mitigating corrosion remains a daunting challenge due to localized, nanoscale corrosion events that are poorly understood but are known to cause unpredictable variations in material longevity. Here, the most recent advances in liquid-cell transmission electron microscopy were employed to capture the advent of localized aqueous corrosion in carbon steel at the nanoscale and in real time. Localized corrosion initiated at a triple junction formed by a solitary cementite grain and two ferrite grains and then continued at the electrochemically-active boundary between these two phases. With this analysis, we identified facetted pitting at the phase boundary, uniform corrosion rates from the steel surface, and data that suggest that a re-initiating galvanic corrosion mechanism is possible in this environment. These observations represent an important step toward atomically defining nanoscale corrosion mechanisms, enabling the informed development of next-generation inhibition technologies and the improvement of corrosion predictive models.

This work characterizes the irradiation creep response of nanocrystalline zirconium by nanoscale quantitative tensile loading and ion irradiation experiments performed simultaneously in situ inside a transmission electron microscope. Microfabricated devices consisting of a freestanding Zr tensile specimen 100 nm in thickness on an elastic Si test frame were produced, and subsequent ex situ ion irradiations were performed on devices with 1.4 MeV Zr ions to a nominal damage level of 0.26 displacement per atom. Subsequent in situ creep experiments performed with and without 1.4 MeV Zr ion irradiation at different applied tensile loads revealed that creep rates were enhanced by the simultaneous radiation damage. This coupled in situ nanomechanical and irradiation methodology enables rapid quantification of both the irradiation creep compliance and associated microstructural evolution.

Metals across all industries demand anticorrosion surface treatments and drive a continual need for high-performing and low-cost coatings. Here we demonstrate polymer-clay nanocomposite thin films as a new class of transparent conformal barrier coatings for protection in corrosive atmospheres. Films assembled via layer-by-layer deposition, as thin as 90 nm, are shown to reduce copper corrosion rates by >1000× in an aggressive H2S atmosphere. These multilayer nanobrick wall coatings hold promise as high-performing anticorrosion treatment alternatives to costlier, more toxic, and less scalable thin films, such as graphene, hexavalent chromium, or atomic-layer-deposited metal oxides.