Knowing when, why, and how materials evolve, degrade, or fail in radiation environments is pivotal to a wide range of fields from semiconductor processing to advanced nuclear reactor design. A variety of methods, including optical and electron microscopy, mechanical testing, and thermal techniques, have been used in the past to successfully monitor the microstructural and property evolution of materials exposed to extreme radiation environments.Acoustic techniques have also been used in the past for this purpose, although most methodologies have not achieved widespread adoption. However, with an increasing desire to understand microstructure and property evolution in situ, acoustic methods provide a promising pathway to uncover information not accessible to more traditional characterization techniques. This work highlights how two different classes of acoustic techniques may be used to monitor material evolution during in situ ion beam irradiation. The passive listening technique of acoustic emission is demonstrated on two model systems, quartz and palladium, and shown to be a useful tool in identifying the onset of damage events such as microcracking.An active acoustic technique in the form of transient grating spectroscopy is used to indirectly monitor the formation of small defect clusters in copper irradiated with self-ions at high temperature through the evolution of surface acoustic wave speeds.These studies together demonstrate the large potential for using acoustic techniques as in situ diagnostics. Such tools could be used to optimize ion beam processing techniques or identify modes and kinetics of materials degradation in extreme radiation environments.
Here, we propose a dislocation adsorption-based mechanism for void growth in metals, wherein a void grows as dislocations from the bulk annihilate at its surface. The basic process is governed by glide and cross-slip of dislocations at the surface of a void. Using molecular dynamics simulations we show that when dislocations are present around a void, growth occurs more quickly and at much lower stresses than when the crystal is initially dislocation-free. Finally, we show that adsorption-mediated growth predicts an exponential dependence on the hydrostatic stress, consistent with the well-known Rice-Tracey equation.
Since the landmark development of the Scherrer method a century ago, multiple generations of width methods for X-ray diffraction originated to non-invasively and rapidly characterize the property-controlling sizes of nanoparticles, nanowires, and nanocrystalline materials. However, the predictive power of this approach suffers from inconsistencies among numerous methods and from misinterpretations of the results. Therefore, we systematically evaluated twenty-two width methods on a representative nanomaterial subjected to thermal and mechanical loads. To bypass experimental complications and enable a 1:1 comparison between ground truths and the results of width methods, we produced virtual X-ray diffractograms from atomistic simulations. These simulations realistically captured the trends that we observed in experimental synchrotron diffraction. To comprehensively survey the width methods and to guide future investigations, we introduced a consistent, descriptive nomenclature. Alarmingly, our results demonstrated that popular width methods, especially the Williamson-Hall methods, can produce dramatically incorrect trends. We also showed that the simple Scherrer methods and the rare Energy methods can well characterize unloaded and loaded states, respectively. Overall, this work improved the utility of X-ray diffraction in experimentally evaluating a variety of nanomaterials by guiding the selection and interpretation of width methods.
The competition between ductile rupture mechanisms in high-purity Cu and other metals is sensitive to the material composition and loading conditions, and subtle changes in the metal purity can lead to failure either by void coalescence or Orowan Alternating Slip (OAS). In situ X-ray computed tomography tensile tests on 99.999% purity Cu wires have revealed that the rupture process involves a sequence of damage events including shear localization; growth of micron-sized voids; and coalescence of microvoids into a central cavity prior to the catastrophic enlargement of the coalesced void via OAS. This analysis has shown that failure occurs in a collaborative rather than strictly competitive manner. In particular, strain localization along the shear band enhanced void nucleation and drove the primary coalescence event, and the size of the resulting cavity and consumption of voids ensured a transition to the OAS mechanism rather than continued void coalescence. Additionally, the tomograms identified examples of void coalescence and OAS growth of individual voids at all stages of the failure process, suggesting that the transition between the different mechanisms was sensitive to local damage features, and could be swayed by collaboration with other damage mechanisms. The competition between the different damage mechanisms is discussed in context of the material composition, the local damage history, and collaboration between the mechanisms.
The populations of flaws in individual layers of microelectromechanical systems (MEMS) structures are determined and verified using a combination of specialized specimen geometry, recent probabilistic analysis, and topographic mapping. Strength distributions of notched and tensile bar specimens are analyzed assuming a single flaw population set by fabrication and common to both specimen geometries. Both the average spatial density of flaws and the flaw size distribution are determined and used to generate quantitative visualizations of specimens. Scanning probe-based topographic measurements are used to verify the flaw spacings determined from strength tests and support the idea that grain boundary grooves on sidewalls control MEMS failure. The findings here suggest that strength controlling features in MEMS devices increase in separation, i.e., become less spatially dense, and decrease in size, i.e., become less potent flaws, as processing proceeds up through the layer stack. The method demonstrated for flaw population determination is directly applicable to strength prediction for MEMS reliability and design.
The process of ductile fracture in metals often begins with void nucleation at second-phase particles and inclusions. Previous studies of rupture in high-purity face-centered-cubic metals, primarily aluminum (Al), concluded that second-phase particles are necessary for cavitation. A recent study of tantalum (Ta), a body-centered-cubic metal, demonstrated that voids nucleate readily at deformation-induced dislocation boundaries. These same features form in Al during plastic deformation. This study investigates why void nucleation was not previously observed at dislocation boundaries in Al. Here, we demonstrate that void nucleation is impeded in Al by room-temperature dynamic recrystallization (DRX), which erases these boundaries before voids can nucleate at them. If dislocation cells reform after DRX and before specimen separation by necking, voids nucleation is observed. These results indicate that dislocation substructures likely plays an important role in ductile rupture.
The classic models for ductile fracture of metals were based on experimental observations dating back to the 1950’s. Using advanced microscopy techniques and modeling algorithms that have been developed over the past several decades, it is possible now to examine the micro- and nano-scale mechanisms of ductile rupture in more detail. This new information enables a revised understanding of the ductile rupture process under quasi-static room temperature conditions in ductile pure metals and alloys containing hard particles. While ductile rupture has traditionally been viewed through the lens of nucleation-growth-and-coalescence, a new taxonomy is proposed involving the competition or cooperation of up to seven distinct rupture mechanisms. Generally, void nucleation via vacancy condensation is not rate limiting, but is extensive within localized shear bands of intense deformation. Instead, the controlling process appears to be the development of intense local dislocation activity which enables void growth via dislocation absorption.
This project has developed models of variability of performance to enable robust design and certification. Material variability originating from microstructure has significant effects on component behavior and creates uncertainty in material response. The outcomes of this project are uncertainty quantification (UQ) enabled analysis of material variability effects on performance and methods to evaluate the consequences of microstructural variability on material response in general. Material variability originating from heterogeneous microstructural features, such as grain and pore morphologies, has significant effects on component behavior and creates uncertainty around performance. Current engineering material models typically do not incorporate microstructural variability explicitly, rather functional forms are chosen based on intuition and parameters are selected to reflect mean behavior. Conversely, mesoscale models that capture the microstructural physics, and inherent variability, are impractical to utilize at the engineering scale. Therefore, current efforts ignore physical characteristics of systems that may be the predominant factors for quantifying system reliability. To address this gap we have developed explicit connections between models of microstructural variability and component/system performance. Our focus on variability of mechanical response due to grain and pore distributions enabled us to fully probe these influences on performance and develop a methodology to propagate input variability to output performance. This project is at the forefront of data-science and material modeling. We adapted and innovated from progressive techniques in machine learning and uncertainty quantification to develop a new, physically-based methodology to address the core issues of the Engineering Materials Reliability (EMR) research challenge in modeling constitutive response of materials with significant inherent variability and length-scales.
Architected structural metamaterials, also known as lattice, truss, or acoustic materials, provide opportunities to produce tailored effective properties that are not achievable in bulk monolithic materials. These topologies are typically designed under the assumption of uniform, isotropic base material properties taken from reference databases and without consideration for sub-optimal as-printed properties or off-nominal dimensional heterogeneities. However, manufacturing imperfections such as surface roughness are present throughout the lattices and their constituent struts create significant variability in mechanical properties and part performance. This study utilized a customized tensile bar with a gauge section consisting of five parallel struts loaded in a stretch (tensile) orientation to examine the impact of manufacturing heterogeneities on quasi-static deformation of the struts, with a focus on ultimate tensile strength and ductility. The customized tensile specimen was designed to prevent damage during handling, despite the sub-millimeter thickness of each strut, and to enable efficient, high-throughput mechanical testing. The strut tensile specimens and reference monolithic tensile bars were manufactured using a direct metal laser sintering (also known as laser powder bed fusion or selective laser melting) process in a precipitation hardened stainless steel alloy, 17-4PH, with minimum feature sizes ranging from 0.5-0.82 mm, comparable to minimum allowable dimensions for the process. Over 70 tensile stress-strain tests were performed revealing that the effective mechanical properties of the struts were highly stochastic, considerably inferior to the properties of larger as-printed reference tensile bars, and well below the minimum allowable values for the alloy. Pre- and post-test non-destructive analyses revealed that the primary source of the reduced properties and increased variability was attributable to heterogeneous surface topography with stress-concentrating contours and commensurate reduction in effective load-bearing area.
The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
