Unintended consequences: Assessing thermo-mechanical changes in vinyl nitrile foam due to micro-computed X-ray tomographic imaging.

Micro-computed X-ray tomography (µCT) is a volumetric imaging tool used to quantify the internal structure of materials. µCT imaging with mechanical testing (in situ µCT) helps visualize strain-induced structural changes and develop structure-property relationships. However, the effects on thermophysical properties of radiation exposure during in situ µCT imaging are seldom addressed, despite potential radiation sensitivity in elastomers. This work quantifies the radiation dosage effect on thermo-, chemical-, and mechanical-properties for a vinyl nitrile-based foam. Material properties were measured after (0, 1, 2, and 3) days at (8.1 ± 0.9) kGy/d. Morphological characteristics were investigated via scanning electron microscopy. Thermal transitions were assessed using differential scanning calorimetry. Viscoelasticity was measured with dynamic mechanical analysis over a range from -30 °C to 60 °C. Higher dose lead to stiffening and increased dissipation. Chemical structure was assessed with Fourier transform infrared spectroscopy and energy-dispersive X-ray spectroscopy. Soxhlet extraction was used to measure gel content. In summary, substantial changes occur in thermophysical properties, which may confound structure-property measurements. However, this also provides a modification pathway. Quantitation and calibration of the properties changes informed a finite element user material for material designers to explore tunablity and design optimization for impact protection engineers.

A high-fidelity simulation of double-sided incremental forming: Improving the accuracy by incorporating the effects of machine compliance.

Double-Sided Incremental Forming (DSIF) is a technology for the rapid, flexible manufacturing of sheet metal parts. DSIF is highly nonlinear, requiring the use of complex finite element (FE) models to optimize and control the process in order to meet geometric accuracy and sheet thinning design criteria. Current FE models do not properly take into account the effects of machine compliance, which reduces their accuracy and hinders their use for optimization and control. The aim of this work is to create a greatly improved FE model of DSIF by taking a novel approach of modeling the aggregate effects of machine and tool compliance. The accuracy of the new model was extensively validated using the local geometry, thickness distribution, principal strains, and forming forces from a funnel experiment. The validated model was used to accurately predict the spatial distribution and time-histories of the equivalent plastic strain, von Mises equivalent stress, stress triaxiality, and Lode angle parameter across and along the sheet metal. The stress state was found to rapidly change through the sheet thickness, from highly compressive between the tools and the sheet, to a mixture of generalized shear and plane strain elsewhere. Moreover, the compressive regions between the two DSIF tools created a constrained deformation zone, which likely aids in prolonging the onset of excessive thinning. This improved FE model can now be used to quantitatively characterize the nonlinear local deformation mechanisms inherent to the DSIF process, thereby providing a solid foundation for future advances in process control.

AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions.

The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress-strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion. Two different laser scan strategies were considered: an X-only raster and an XY raster, which involved a 90° rotation in the scan direction between subsequent layers. To measure anisotropy, multiple tensile orientations with respect to the build direction were investigated (e.g., parallel, perpendicular, and intervals in between). Benchmark participants were provided grain structure information via electron backscatter diffraction measurements, as well as the stress-strain response for tensile specimens manufactured parallel to the build direction and produced by the XY scan strategy. Then, participants were asked to predict tensile properties, like the ultimate tensile strength, for the remaining specimens and orientations. Interestingly, the measured mechanical properties did not vary linearly as a function of tensile orientation. Moreover, specimens manufactured with the XY scan strategy exhibited greater yield strength than those corresponding to the X-only scan strategy, regardless of orientation. The benchmark data has been made publicly available for anyone that is interested [1]. For the modeling aspect of the challenge, five teams participated in this benchmark. While most of the models incorporated a crystal plasticity framework, one team chose to use a more semi-empirical approach, and to great success. However, no team excelled at all the predictions, and all teams were seemingly challenged with the predictions associated with the X-only scan strategy.

Pore formation driven by particle impact in laser powder-blown directed energy deposition.

Process defects currently limit the use of metal additive manufacturing (AM) components in industries due to shorter fatigue life, potential for catastrophic failure, and lower strength. Conditions under which these defects form, and their mechanisms, are starting to be analyzed to improve reliability and structural integrity of these highly customized parts. We use in situ, high-speed X-ray imaging in conjunction with a high throughput laser, powder-blown directed energy deposition setup to observe powder particle impact behavior within the melt pool. Through fundamental observations of the stochastic, violent powder delivery in powder-blown DED, we uncover a unique pore formation mechanism. We find that a pore can form due to air-cushioning, where vapor from the carrier gas or environment is entrapped between the solid powder particle surface and liquid melt pool surface. A critical time constant is established for the mechanism, and X-ray computed tomography is used to further analyze and categorize the new type of "air-cushioning" pores. It is shown that the air-cushioning mechanism can occur under multiple laser processing conditions, and we show that air-cushioning pores are more likely to be formed when powder particles are larger than 70 µm. By quantifying the effect of powder particle impact, we identify new avenues for development of high-quality laser, powder-blown DED products. Furthermore, we deepen knowledge on defect formation in metal additive manufacturing, which is being increasingly utilized in high performance situations such as aerospace, automotive, and biomedical industries.

A materials data framework and dataset for elastomeric foam impact mitigating materials.

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

Additive Manufacturing Benchmark 2022 Subcontinuum Mesoscale Tensile Challenge (CHAL-AMB2022-04-MeTT) and Summary of Predictions.

This additive manufacturing benchmarking challenge asked the modelling community to predict the stress-strain behavior and fracture location and pathway of an individual meso-scale (gauge dimensions of approximately 200 µm thickness, 200 µm width, 1mm length) tension specimen that was excised from a wafer of nickel allow IN625 manufactured by laser powder bed fusion (L-PBF). The data used for the challenge questions and answers are provided in a public dataset (https://data.nist.gov/od/id/mds2-2587). Testing models against the data is still possible, although a good-faith blinded prediction should be attempted before reading this article, as the results are contained herein. The uniaxial tension test was pin loaded, conducted at quasi-static strain rates under displacement control, and strain was measured via non-contact methods (digital image correlation). The predictions are challenging since the number of grains contained in the thickness of the specimen are sub-continuum. In addition, pores can be heterogeneously distributed by the L-PBF process, as opposed to intentionally seeded defects. The challenge provided information on chemical composition, grain and subgrain structure (surface-based measurements via electron backscatter diffraction and scanning electron microscopy) and pore structure (volume-based measurements via X-Ray computed tomography) along the entire gauge length for the tension specimen. During the challenge, prediction responses were collected from six different groups. Prediction accuracy compared to the measurements varied, with elastic modulus and strain at ultimate tensile strength consistently over-predicted, while most other values were a mix of over- and under-predicted. Overall, no one model performed best at all predictions. Failure-related properties proved quite challenging to predict, likely in part due to the data provided as well as the inherent difficulty in predicting fracture. Future directions and areas of improvement are discussed in the context of improving model maturity and measurement uncertainty.

Quantifying Discretization Errors in Electrophoretically-Guided Micro Additive Manufacturing.

This paper presents process models for a new micro additive manufacturing process termed Electrophoretically-guided Micro Additive Manufacturing (EPµAM). In EPµAM, a planar microelectrode array generates the electric potential distributions which cause colloidal particles to agglomerate and deposit in desired regions. The discrete microelectrode array nature and the used pulse width modulation (PWM) technique for microelectrode actuation create unavoidable process errors-space and time discretization errors-that distort particle trajectories. To combat this, we developed finite element method (FEM) models to study trajectory deviations due to these errors. Mean square displacement (MSD) analysis of the computed particle trajectories is used to compare these deviations for several electrode geometries. The two top-performing electrode geometries evaluated by MSD were additionally investigated through separate case studies via geometry variation and MSD recomputation. Furthermore, separate time-discretization error simulations are also studied where electrode actuating waveforms were simulated. The mechanical impulse of the electromechanical force, generated from these waveforms is used as the basis for comparison. The obtained results show a moderate MSDs variability and significant differences in the computed mechanical impulses for the actuating waveforms. The observed limitations of the developed process model and of the error comparison technique are briefly discussed and future steps are recommended.