Thermal spray deposition is an inherently stochastic manufacturing process used for generating thick coatings of metals, ceramics and composites. The generated coatings exhibit hierarchically complex internal structures that affect the overall properties of the coating. The deposition process can be adequately simulated using rules-based process simulations. Nevertheless, in order for the simulation to accurately model particle spreading upon deposition, a set of predefined rules and parameters need to be calibrated to the specific material and processing conditions of interest. The calibration process is not trivial given the fact that many parameters do not correspond directly to experimentally measurable quantities. This work presents a protocol that automatically calibrates the parameters and rules of a given simulation in order to generate the synthetic microstructures with the closest statistics to an experimentally generated coating. Specifically, this work developed a protocol for tantalum coatings prepared using air plasma spray. The protocol starts by quantifying the internal structure using 2-point statistics and then representing it in a low-dimensional space using Principal Component Analysis. Subsequently, our protocol leverages Bayesian optimization to determine the parameters that yield the minimum distance between synthetic microstructure and the experimental coating in the low-dimensional space.
Optical fiber diagnostics are extensively used in pulsed power experiments, such as the Sandia Z machine. However, radiation produced in a pulsed power environment can significantly affect these measurements. Catastrophic fiber darkening may be mitigated with shielding, but no flexible material can stop all radiation produced by the machine and/or target. Radiation-induced refractive index modulations are particularly challenging for optical interferometry. Several approaches for radiation-tolerant photonic Doppler velocimetry are discussed here.
Thermal spray processes involve the repeated impact of millions of discrete particles, whose melting, deformation, and coating-formation dynamics occur at microsecond timescales. The accumulated coating that evolves over minutes is comprised of complex, multiphase microstructures, and the timescale difference between the individual particle solidification and the overall coating formation represents a significant challenge for analysts attempting to simulate microstructure evolution. In order to overcome the computational burden, researchers have created rule-based models (similar to cellular automata methods) that do not directly simulate the physics of the process. Instead, the simulation is governed by a set of predefined rules, which do not capture the fine-details of the evolution, but do provide a useful approximation for the simulation of coating microstructures. Here, we introduce a new rules-based process model for microstructure formation during thermal spray processes. The model is 3D, allows for an arbitrary number of material types, and includes multiple porosity-generation mechanisms. Example results of the model for tantalum coatings are presented along with sensitivity analyses of model parameters and validation against 3D experimental data. The model's computational efficiency allows for investigations into the stochastic variation of coating microstructures, in addition to the typical process-to-structure relationships.
Dynamic compression of materials can induce a variety of microstructural changes. As thermally-sprayed materials have highly complex microstructures, the expected pressure at which changes occur cannot be predicted a priori. In addition, typical in-situ measurements such as velocimetry are unable to adequately diagnose microstructural changes such as failure or pore collapse. Quasi-isentropic compression experiments with sample recovery were conducted to examine microstructural changes in thermally sprayed tantalum and tantalum-niobium blends up to 8 GPa pressure. Spall fracture was observed in all tests, and post-shot pore volume decreased relative to the initial state. The blended material exhibited larger spall planes with fracture occurring at interphase boundaries. An estimate of the pressure at which pore collapse is complete was determined to be ~26 GPa for pure tantalum and ~19 GPa for the tantalumniobium blend under these loading conditions.
Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.
A parallel, adaptive overlay grid procedure is proposed for use in generating all-hex meshes for stochastic (SVE) and representative (RVE) volume elements in computational materials modeling. The mesh generation process is outlined including several new advancements such as data filtering to improve mesh quality from voxelated and 3D image sources, improvements to the primal contouring method for constructing material interfaces and pillowing to improve mesh quality at boundaries. We show specific examples in crystal plasticity and syntactic foam modeling that have benefitted from the proposed mesh generation procedure and illustrate results of the procedure with several practical mesh examples.
The equation of state (EOS) of bulk niobium (Nb) was investigated within the framework of density functional theory, with Mermin's generalization to finite temperatures. The shock Hugoniot for fully-dense and porous Nb was obtained from canonical ab initio molecular dynamics simulations with Erpenbeck's approach based on the Rankine-Hugoniot jump conditions. The phase space was sampled along isotherms between 300 and 4000 K, for densities ranging from ρ=5.5 to 12 g/cm3. Results from simulations compare favorably with room-temperature multianvil and diamond anvil cell data for fully-dense Nb samples and with a recent tabulated SESAME EOS. The results of this study indicate that, for the application of weak and intermediate shocks, the tabular EOS models are expected to give reliable predictions.