Publications

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Product designs from a wide range of industries such as aerospace, automotive, biomedical, and others can benefit from new metamaterials for mechanical energy dissipation. In this study, we explore a novel new class of metamaterials with unit cells that absorb energy via sliding Coulombic friction. Remarkably, even materials such as metals and ceramics, which typically have no intrinsic reversible energy dissipation, can be architected to provide dissipation akin to elastomers. The concept is demonstrated at different scales (centimeter to micrometer), with different materials (metal and polymer), and in different operating environments (high and low temperatures), all showing substantial dissipative improvements over conventional non-contacting lattice unit cells. Further, as with other ‘programmable’ metamaterials, the degree of Coulombic absorption can be tailored for a given application. An analytic expression is derived to allow rapid first-order optimization. This new class of Coulombic friction energy absorbers can apply broadly to many industrial sectors such as transportation (e.g. monolithic shock absorbers), biomedical (e.g. prosthetics), athletic equipment (e.g. skis, bicycles, etc.), defense (e.g. vibration tolerant structures), and energy (e.g. survivable electrical grid components).

With the recent advances in additive manufacturing (AM), long-range periodic lattice assemblies are being developed for vibration and shock mitigation components in aerospace and military applications with unique geometric and topological structures. There has been extensive work in understanding the static properties associated with varying topology of these lattice architectures, but there is almost no understanding of microstructural affects in such structures under high-strain rate dynamic loading conditions. Here we report the shock behavior of lattices with varying intrinsic grain structures achieved by post process annealing. High resolution 316L stainless steel lattices were 3D printed by a laser-powder bed fusion machine and characterized by computed tomography. Subsequent annealing resulted in stress-relieved and recrystallized lattices. Overall the lattices had strong cubic texture aligning with the x-, y- and z-directions of the build with a preference outside the build direction (z). The recrystallized sample had more equiaxed polygonal grains and a layer of BCC ferrite at the surface of the structure approximately 1 grain thick. Upon dynamic compression the as-deposited lattice showed steady compaction behavior while the heat-treated lattices exhibit negative velocity behavior indicative of failure. We attribute this to the stiffer BCC ferrite in the annealed lattices becoming damaged and fragmenting during compression.

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While lattice metamaterials can achieve exceptional energy absorption by tailoring periodically distributed heterogeneous unit cells, relatively little focus has been placed on engineering heterogeneity above the unit-cell level. In this work, the energy-absorption performance of lattice metamaterials with a heterogeneous spatial layout of different unit cell architectures was studied. Such multi-morphology lattices can harness the distinct mechanical properties of different unit cells while being composed out of a single base material. A rational design approach was developed to explore the design space of these lattices, inspiring a non-intuitive design which was evaluated alongside designs based on mixture rules. Fabrication was carried out using two different base materials: 316L stainless steel and Vero White photopolymer. Results show that multi-morphology lattices can be used to achieve higher specific energy absorption than homogeneous lattice metamaterials. Additionally, it is shown that a rational design approach can inspire multi-morphology lattices which exceed rule-of-mixtures expectations.

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Residual stress measurements using neutron diffraction and the contour method were performed on a valve housing made from 316 L stainless steel powder with intricate three-dimensional internal features using laser powder-bed fusion additive manufacturing. The measurements captured the evolution of the residual stress fields from a state where the valve housing was attached to the base plate to a state where the housing was cut free from the base plate. Making use of this cut, thus making it a non-destructive measurement in this application, the contour method mapped the residual stress component normal to the cut plane (this stress field is completely relieved by cutting) over the whole cut plane, as well as the change in all stresses in the entire housing due to the cut. The non-destructive nature of the neutron diffraction measurements enabled measurements of residual stress at various points in the build prior to cutting and again after cutting. Good agreement was observed between the two measurement techniques, which showed large, tensile build-direction residual stresses in the outer regions of the housing. The contour results showed large changes in multiple stress components upon removal of the build from the base plate in two distinct regions: near the plane where the build was cut free from the base plate and near the internal features that act as stress concentrators. These observations should be useful in understanding the driving mechanisms for builds cracking near the base plate and to identify regions of concern for structural integrity. Neutron diffraction measurements were also used to show the shear stresses near the base plate were significantly lower than normal stresses, an important assumption for the contour method because of the asymmetric cut.

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Due to the direct relationship between thermal history and mechanical behavior, in situ thermal monitoring is key in gauging quality of parts produced with additive manufacturing (AM). Accurate monitoring of temperatures in an AM process requires knowledge of environment and object parameters including object emissivity. The emissivity is dependent on several variables, including: wavelength, material composition, temperature, and surface topography. Researchers have been concerned with the thermal emissivity dependence on temperature since large ranges are seen in metal powder bed processes, but there is also an extensive range of surfaces produced by AM. This work focused on discovering what roughness characteristics control thermal emissivity through investigation of prototypic 316 stainless steel AM samples produced with a range of build conditions on a laser powder bed fusion machine. Through experimental measurements of emissivity using hemispherical directional reflectance (HDR), guided by simulations using a finite-difference time-domain (FDTD) Maxwell solver, it was found that combinations of existing roughness parameters describing both height and slope of the surface correlate well with emissivity changes. These parameters work well due to their apt description of surface features encouraging internal reflection, which is the phenomenon that increases emissivity when a surface falls under the geometric optical region conditions.

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Recent work in metal additive manufacturing (AM) suggests that mechanical properties may vary with feature size; however, these studies do not provide a statistically robust description of this phenomenon, nor do they provide a clear causal mechanism. Because of the huge design freedom afforded by 3D printing, AM parts typically contain a range of feature sizes, with particular interest in smaller features, so the size effect must be well understood in order to make informed design decisions. This work investigates the effect of feature size on the stochastic mechanical performance of laser powder bed fusion tensile specimens. A high-throughput tensile testing method was used to characterize the effect of specimen size on strength, elastic modulus and elongation in a statistically meaningful way. The effective yield strength, ultimate tensile strength and modulus decreased strongly with decreasing specimen size: all three properties were reduced by nearly a factor of two as feature dimensions were scaled down from 6.25 mm to 0.4 mm. Hardness and microstructural observations indicate that this size dependence was not due to an intrinsic change in material properties, but instead the effects of surface roughness on the geometry of the specimens. Finite element analysis using explicit representations of surface topography shows the critical role surface features play in creating stress concentrations that trigger deformation and subsequent fracture. The experimental and finite element results provide the tools needed to make corrections in the design process to more accurately predict the performance of AM components.

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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.

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Porosity in additively manufactured metals can reduce material strength and is generally undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

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