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<div>The new technique of Vibration-Assisted 3D Printing (VAP) offers significant potential for leveraging the geometric flexibility of additive manufacturing (AM) into the realm of solid energetics. The first part of this work compares the print capabilities of a custom-made VAP printer to those of an established commercial direct-write printer using a polymer clay. Characterization tests were conducted and a variety of other shapes were printed comparing the two methods in their turning quality, feature resolution, unsupported overhang angle, negative space feature construction, and less-than-fully-dense self-supported 3D lattices. The porosity and regularity of the printed lattices were characterized using X-ray microtomography (MicroCT) scans. The quality of the shapes was compared using statistical methods and a MATLAB edge-finding code. The results show that the VAP printer can manufacture parts of superior resolution than the commercial printer, due to its ability to extrude highly viscous material through a smaller nozzle diameter. The VAP print speeds were also found to be as high as twenty times higher than those of the direct write printer.</div><div>Following up on this work, a second study explored the possibility of modifying grain geometry through variation of printed infill design using an ammonium perchlorate composite propellant (APCP). In the propellant formulation, a polymer that cures under ultra-violet (UV) light was used instead of the more common hydroxyl-terminated polybutadiene (HTPB). Although this formulation is a less-effective fuel than HTPB, its use enables layer-by-layer curing for improved structural strength during printing. Using VAP, cylindrical propellant charges were prepared using a gyroidal infill design with a range of internal porosities (infill amounts). Some additional propellant grains were prepared with both vertical and concentric layering of different infill amounts. These grains were then burned beginning at atmospheric pressure in a constant-volume Parr cell to measure the resulting pressure output. Analysis of the pressure trace data shows that a less-dense infill increases the maximum pressurization rate, due to the presence of small voids spaced roughly uniformly throughout the grain that increase the burning surface area. We show that additive manufacturing-based propellant grain modification can be used to tailor the pressure-time trace through adjustment of the number and size of small voids. Specifically, this study shows that, using a graded functional geometry, the duration of gas generation can be controlled. This work represents a preliminary effort to explore the possibilities to propellant</div><div>12</div><div>manufacture offered by additive manufacturing and to begin to address the challenges inherent in making it practical.</div>

<div>Self-sensing materials are engineered to transduce mechanical effects like deformations and damages into detectable electrical changes. As such, they have received immense research attention in areas including aerospace, civil infrastructure, robotic skin, and biomedical devices. In structural health monitoring (SHM) and nondestructive evaluation (NDE) applications, damages in the material cause breakage in the conductive filler networks, resulting in changes in the material's conductivity. Most SHM and NDE applications of self-sensing materials have used direct current (DC) measurements. DC-based methods have shown advantages with regard to sensitivity to microscale damages compared to other SHM methods. Comparatively, alternating current (AC) measurement techniques have shown potential for improvement over existent DC methods. For example, using AC in conjunction with self-sensing materials has potential for benefits such as greater data density, higher sensitivity through electrodynamics effects (e.g., coupling the material with resonant circuitry), and lower power requirements. Despite these potential advantages, AC techniques have been vastly understudied compared to DC techniques. </div><div><br></div><div>To overcome this gap in the state of the art, this thesis presents two contributions: First, an experimental study is conducted to elucidate the effect of different damage types, numbers, and sizes on AC transport in a representative self-sensing composite. And second, experimental data is used to inform a computational study on using AC methods to improve damage detection via electrical impedance tomography (EIT) – a conductivity imaging modality commonly paired with self-sensing materials for damage localization. For the first contribution, uniaxial glass fiber specimens containing 0.75 wt.% of carbon nanofiber (CNF) are induced with five types of damage (varying the number of holes, size of holes, number of notches, size of notches, and number of impacts). Impedance magnitude and phase angle were measured after each permutation of damage to study the effect of the new damage on AC transport. It was observed that permutations of hole and notch damages show clear trends of increasing impedance magnitude with the increasing damage, particularly at low frequencies. These damages had little-to-no effect on phase angle, however. Increasing numbers of impacts on the specimens did not show any discernable trend in either impedance magnitude or phase angle, except at high frequencies. This shows that different AC frequencies can be more or less useful for finding particular damage types.</div><div><br></div><div>Regarding the second contribution, AC methods were also explored to improve damage detection in self-sensing materials via EIT. More specifically, the EIT technique could benefit from developing a baseline-free (i.e., not requiring a ‘healthy’ reference) formulations enabled by frequency-difference (fd) imaging. For this, AC conductivity measurements ranging from 100 Hz to 10 MHz were collected from various weight fractions of CNF-modified glass fiber/epoxy laminates. This experimental data was used to inform fdEIT simulations. In the fdEIT simulations, damage was simulated as a simple through-hole. Simulations used 16 electrodes with four equally spaced electrodes on each side of the domain. The EIT forward problem was used to predict voltage-current response on the damaged mesh, and a fdEIT inverse problem was formulated to reconstructs the damage state on an undamaged mesh. The reconstruction images showed the simulated damage clearly. Based on this preliminary study, this research shows that fdEIT does have potential to eliminate the need for a healthy baseline in NDE applications, which can potentially help proliferate the use of this technique in practice.</div>

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