Publications

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To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. The technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control.

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The high-cycle fatigue life of nanocrystalline and ultrafine-grained Ni-Fe was examined for five distinct grain sizes ranging from approximately 50–600 nm. The fatigue properties were strongly dependent on grain size, with the endurance limit changing by a factor of 4 over this narrow range of grain size. The dataset suggests a breakdown in fatigue improvement for the smallest grain sizes <100 nm, likely associated with a transition to grain coarsening as a dominant rate-limiting mechanism. The dataset also is used to explore fatigue prediction from monotonic tensile properties, suggesting that a characteristic flow strength is more meaningful than the widely-utilized ultimate tensile strength.

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The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

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.

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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.

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Nanocrystalline metals offer significant improvements in structural performance over conventional alloys. However, their performance is limited by grain boundary instability and limited ductility. Solute segregation has been proposed as a stabilization mechanism, however the solute atoms can embrittle grain boundaries and further degrade the toughness. In the present study, we confirm the embrittling effect of solute segregation in Pt-Au alloys. However, more importantly, we show that inhomogeneous chemical segregation to the grain boundary can lead to a new toughening mechanism termed compositional crack arrest. Energy dissipation is facilitated by the formation of nanocrack networks formed when cracks arrested at regions of the grain boundaries that were starved in the embrittling element. This mechanism, in concert with triple junction crack arrest, provides pathways to optimize both thermal stability and energy dissipation. A combination of in situ tensile deformation experiments and molecular dynamics simulations elucidate both the embrittling and toughening processes that can occur as a function of solute content.

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