Traditional methods of shielding fragile goods and human tissues from impact energy rely on isotropic foam materials. The mechanical properties of these foams are inferior to an emerging class of metamaterials called plate lattices, which have predominantly been fabricated in simple 2.5-dimensional geometries using conventional methods that constrain the feasible design space. In this work, additive manufacturing is used to relax these constraints and realize plate lattice metamaterials with nontrivial, locally varying geometry. The limitations of traditional computer-aided design tools are circumvented and allow the simulation of complex buckling and collapse behaviors without a manual meshing step. By validating these simulations against experimental data from tests on fabricated samples, sweeping exploration of the plate lattice design space is enabled. Numerical and experimental tests demonstrate plate lattices absorb up to six times more impact energy at equivalent densities relative to foams and shield objects from impacts ten times more energetic while transmitting equivalent peak stresses. In contrast to previous investigations of plate lattice metamaterials, designs with nonuniform geometric prebuckling in the out-of-plane direction is explored and showed that these designs exhibit 10% higher energy absorption efficiency on average and 25% higher in the highest-performing design.
Mechanical strength of a 94 wt% debased alumina was measured using ASTM-C1161 specimens fabricated via conventional and lithography-based ceramic manufacturing (LCM) methods. The effects of build orientation and a 1500°C wet hydrogen fire added to the LCM firing sequence on strength were evaluated. A Weibull fit to the conventional flexural specimen data yielded 20 and 356 MPa for the modulus and characteristic strength, respectively. Weibull fits of the data from the LCM specimens yielded moduli between 7.5 and 11.3 and characteristics strengths between 333 and 339 MPa. A Weibull fit to data from LCM specimens subjected to the wet hydrogen fire yielded 14.2 and 376 MPa for the modulus and characteristic strength, respectively. The 95% confidence intervals for all Weibull parameters are reported. Average Archimedes bulk densities of LCM and conventional specimens were 3.732 and 3.730 g/cm3, respectively. Process dependent differences in surface morphology were observed in scanning electron microscope (SEM) images of specimen surfaces. SEM images of LCM specimen cross-sections showed alumina grain texture dependent on build direction, but no evidence of porosity concentrated in planes between printed layers. Fracture surfaces of LCM and conventionally processed specimens revealed hackle lines and mirror regions indicative of fracture initiation at the sample surface rather than the interior.
Simulation of additive manufacturing processes can provide essential insight into material behavior, residual stress, and ultimately, the performance of additively manufactured parts. In this work, we describe a new simulation based workflow utilizing both solid mechanics and fluid mechanics based formulations within the finite element software package SIERRA (Sierra Solid Mechanics Team in Sierra/SolidMechanics 4.52 User’s Guide SAND2019-2715. Technical report, Sandia National Laboratories, 2011) to enable integrated simulations of directed energy deposition (DED) additive manufacturing processes. In this methodology, a high-fidelity fluid mechanics based model of additive manufacturing is employed as the first step in a simulation workflow. This fluid model uses a level set field to track the location of the boundary between the solid material and background gas and precisely predicts temperatures and material deposition shapes from additive manufacturing process parameters. The resulting deposition shape and temperature field from the fluid model are then mapped into a solid mechanics formulation to provide a more accurate surface topology for radiation and convection boundary conditions and a prescribed temperature field. Solid mechanics simulations are then conducted to predict the evolution of material stresses and microstructure within a part. By combining thermal history and deposition shape from fluid mechanics with residual stress and material property evolutions from solid mechanics, additional fidelity and precision are incorporated into additive manufacturing process simulations providing new insight into complex DED builds.
Ceramic to metal brazing is a common bonding process usedin many advanced systems such as automotive engines, aircraftengines, and electronics. In this study, we use optimizationtechniques and finite element analysis utilizing viscoplastic andthermo-elastic material models to find an optimum thermalprofile for a Kovar® washer bonded to an alumina button that istypical of a tension pull test. Several active braze filler materialsare included in this work. Cooling rates, annealing times, aging,and thermal profile shapes are related to specific materialbehaviors. Viscoplastic material models are used to represent thecreep and plasticity behavior in the Kovar® and braze materialswhile a thermo-elastic material model is used on the alumina.The Kovar® is particularly interesting because it has a Curiepoint at 435°C that creates a nonlinearity in its thermal strain andstiffness profiles. This complex behavior incentivizes theoptimizer to maximize the stress above the Curie point with afast cooling rate and then favors slow cooling rates below theCurie point to anneal the material. It is assumed that if failureoccurs in these joints, it will occur in the ceramic material.Consequently, the maximum principle stress of the ceramic isminimized in the objective function. Specific details of the stressstate are considered and discussed.
Ceramic to metal brazing is a common bonding process usedin many advanced systems such as automotive engines, aircraftengines, and electronics. In this study, we use optimizationtechniques and finite element analysis utilizing viscoplastic andthermo-elastic material models to find an optimum thermalprofile for a Kovar® washer bonded to an alumina button that istypical of a tension pull test. Several active braze filler materialsare included in this work. Cooling rates, annealing times, aging,and thermal profile shapes are related to specific materialbehaviors. Viscoplastic material models are used to represent thecreep and plasticity behavior in the Kovar® and braze materialswhile a thermo-elastic material model is used on the alumina.The Kovar® is particularly interesting because it has a Curiepoint at 435°C that creates a nonlinearity in its thermal strain andstiffness profiles. This complex behavior incentivizes theoptimizer to maximize the stress above the Curie point with afast cooling rate and then favors slow cooling rates below theCurie point to anneal the material. It is assumed that if failureoccurs in these joints, it will occur in the ceramic material.Consequently, the maximum principle stress of the ceramic isminimized in the objective function. Specific details of the stressstate are considered and discussed.
We describe an approach to predict failure in a complex, additively-manufactured stainless steel part as defined by the third Sandia Fracture Challenge. A viscoplastic internal state variable constitutive model was calibrated to fit experimental tension curves in order to capture plasticity, necking, and damage evolution leading to failure. Defects such as gas porosity and lack of fusion voids were represented by overlaying a synthetic porosity distribution onto the finite element mesh and computing the elementwise ratio between pore volume and element volume to initialize the damage internal state variables. These void volume fraction values were then used in a damage formulation accounting for growth of these existing voids, while new voids were allowed to nucleate based on a nucleation rule. Blind predictions of failure are compared to experimental results. The comparisons indicate that crack initiation and propagation were correctly predicted, and that an initial porosity field superimposed as higher initial damage may provide a path forward for capturing material strength uncertainty. The latter conclusion was supported by predicted crack face tortuosity beyond the usual mesh sensitivity and variability in predicted strain to failure; however, it bears further inquiry and a more conclusive result is pending compressive testing of challenge-built coupons to de-convolute materials behavior from the geometric influence of significant porosity.
This report documents proposed improvements to an apparatus for measuring flow rates and aerosol retention in stress corrosion cracks (SCCs). The potential for SCCs in canister walls is a concern for dry cask storage systems for spent nuclear fuel. Some of the canisters in these systems are backfilled to significant pressures to promote heat rejection via internal convection. Pressure differentials covering the upper limit of commercially available dry cask storage systems are the focus of the current test assembly. Initial studies will be conducted using engineered microchannels with characteristic dimensions expected in SCCs that hypothetically could form in dry storage canister walls. In a previous study, an apparatus and procedures were developed and implemented to investigate aerosol retention in a simple microchannel with an SCC-like opening of 28.9 gm (0.00110 in.). The width was 12.7 mm (0.500 in.), and the length was 8.86 mm (0.349 in.). These initial results indicated 44% of the aerosols available for transmission were retained upstream of microchannel However, limitations in the aerosol instruments available at the time of the preliminary study introduced known biases into the measurements. While these biases were identified and quantified, their presence introduced unwanted degrees of freedom into the measurements and reduced accuracy. Because these aerosol particle sizers (APS) were limited to sampling at atmospheric pressure, a mass flow controller was used to supply the sample upstream of the crack to the APS. The average line loss across all particle sizes for this mass flow controller was 50%. The sample downstream of the crack was delivered via a mass flow meter and caused a line loss of 20%. Another source of bias was using separate (but identical) instruments to measure the aerosols upstream and downstream of the microchannel, which could register up to 40% different when measuring the same sample stream. The experience of conducting the preliminary study highlighted the need for improvements in the experimental approach that would eliminate these biases and benefit future studies. An aerosol analyzer has been identified and ordered that is ideally suited for this study and should substantially mitigate these biases. Moving forward in the near term, the same simple microchannel will be further investigated using the improved aerosol instrumentation. Additionally, an offset microchannel with a step in the flow path will be designed and fabricated for similar testing. Looking out further, the capability to produce and test laboratory generated SCCs will be developed.
This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processe s for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.
This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.