No description available.

This dissertation provides a novel methodology to assess the impact of additive manufacturing (AM) on the aerospace supply chain. The focus is serialized production of structural parts for the aeroengine. This methodology has three fundamental steps. First, a screening heuristic is used to identify which parts and assemblies would be candidates for AM displacement. Secondly, the production line is characterized and evaluated to understand how these changes in the bill of material might impact plant workflow, and ultimately, part and assembly cost. Finally, the third step employs an integer linear program (ILP) to predict the impact on the supply chain network. The network nodes represent the various companies – and depending upon their tier – each tier has a dedicated function. The output of the ILP is the quantity and connectivity of these nodes between the tiers.<br><br>It was determined that additive manufacturing can be used to displace certain conventional manufacturing parts and assemblies as additive manufacturing’s technology matures sufficiently. Additive manufacturing is particularly powerful if adopted by the artifact’s design authority (usually the original equipment manufacturer – OEM) since it can then print its own parts on demand. Given this sourcing flexibility, these entities can in turn apply pricing pressure on its suppliers. This phenomena increasing has been seen within the industry.

<div>Laser-to-composite interactions are becoming increasingly common in diverse applications such as diagnostics, fabrication and machining, and weapons systems. Lasers are capable of not only performing non-contact diagnostics, but also inducing seemingly imperceptible structural damage to materials. In safety-critical venues like aerospace, automotive, and civil infrastructure where composites are playing an increasingly prominent role, it is desirable to have means of sensing laser exposure on a composite material. Self-sensing materials may be a powerful method of addressing this need. Herein, we present an exploratory study on the potential of using changes in electrical measurements as a way of detecting laser exposure to a carbon nanofiber (CNF)-modified glass fiber/epoxy laminate. CNFs were dispersed in liquid epoxy resin prior to laminate fabrication via hand layup. The dispersed CNFs form a three-dimensional conductive network which allows for electrical measurements to be taken from the traditionally insulating glass fiber/epoxy material system. It is expected that damage to the network will disrupt the electrical pathways, thereby causing the material to exhibit slightly higher resistance. To test laser sensing capabilities, a resistance baseline of the CNF-modified glass fiber/epoxy specimens was first established before laser exposure. These specimens were then exposed to an infra-red laser operating at 1064 nm, 35 kHz, and pulse duration of 8 ns. The specimens were irradiated for a total of 20 seconds (4 exposures each at 5 seconds). The resistances of the specimens were then measured again post-ablation. In this study, it was found that for 1.0 wt.% CNF by weight the average resistance increased by about 18 percent. However, this values varied for specimens with different weight fractions. This established that the laser was indeed causing damage to the specimen sufficient to evoke a change in electrical properties. In order to expand on this result, electrical impedance tomography (EIT) was employed for localization of laser exposures of 1, 3, and 5 seconds on a larger specimen, a 3.25” square plate. EIT was used to measure the changes in conductivity after each exposure. EIT was not only successful in detecting damage that was virtually imperceptible to the human-eye, but it also accurately localized the exposure sites. The post-ablation conductivity of the exposure sites decreased in a manner that was comparable to the resistance increase obtained during prior testing. Based on this preliminary study, this research could lead to the development of a real-time exposure detection and tracking system for the measurement, fabrication, and defense industries.</div>

<p>Raman spectroscopy is a molecular spectroscopy technique that uses monochromatic light to provide a fingerprint to identify structural components and chemical composition. Depending on the changes in the unit-cell parameters and volume under the application of stress and temperature, the Raman spectrum undergoes changes in the wavenumber of Raman-active modes that allow identification of sample characteristics. Due to the various advantage of mechanical Raman spectroscopy (MRS), the use of this technique in the characterization and modeling of chemical changes under stress and temperature have gained popularity. </p> <p> Quantitative information regarding the local behavior of interfaces in an inhomogeneous material during shock loading is limited due to challenges associated with time and spatial resolution. Recently, we have extended the use of MRS to high-strain rate experiments to capture the local thermomechanical response of mock energetic material and obtain material properties during shock wave propagation. This was achieved by developing a novel method for <i>in‑situ</i> measurement of the thermo‑mechanical response from mock energetic materials in a time‑resolved manner with 5 ns resolution providing an estimation on local pressure, temperature, strain rate, and local shock viscosity. The results show the solid to liquid phase transition of sucrose under shock compression. The viscous behavior of the binder was also characterized through measurement of shock viscosity at strain rates higher than 10⁶/s using microsphere impact experiments.</p> <p> This technique was further extended to perform Raman spectral imaging over a microscale domain of the sample with a nano-second resolution. This was achieved by developing a laser-array Raman spectral imaging technique where simultaneous deconvolution of Raman spectra over the sample domain was achieved and Raman spectral image was reconstructed on post-processing. We developed a Raman spectral imaging system using a laser array and analysis was performed over the interface of sucrose crystals bonded using an epoxy binder. This study provides the Raman spectra over the microstructure domain which enabled the detection of localized melting under shock compression. The distribution of shock pressure and temperature over the microstructure was obtained using mechanical Raman analysis. The study shows the effects of an actual interface on the propagation of shock waves where a higher dissipation of shock energy was observed compared to an ideal interface. This increase in shock dissipation is accompanied by a decrease in both the maximum temperature, as well as the maximum pressure within the microstructure during shock wave propagation.</p>

<p>Ni-based superalloys continue to exert themselves as the industry standards in high stress and highly corrosive/oxidizing environments, such as are present in a gas turbine engine, due to their excellent high temperature strengths, thermal and microstructural stabilities, and oxidation and creep resistances. Gas turbine engines are essential components for energy generation and propulsion in the modern age. However, Ni-based superalloys are reaching their limits in the operating conditions of these engines due to their melting onset temperatures, which is approximately 1300 °C. Therefore, a new class of materials must be formulated to surpass the capabilities Ni-based superalloys, as increasing the operating temperature leads to increased efficiency and reductions in fuel consumption and greenhouse gas emissions. One of the proposed classes of materials is termed refractory complex concentrated alloys, or RCCAs, which consist of 4 or more refractory elements (in this study, selected from: Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) in equimolar or near-equimolar proportions. So far, there have been highly promising results with these alloys, including far higher melting points than Ni-based superalloys and outstanding high-temperature strengths in non-oxidizing environments. However, improvements in room temperature ductility and high-temperature oxidation resistance are still needed for RCCAs. Also, given the millions of possible alloy compositions spanning various combinations and concentrations of refractory elements, more efficient methods than just serial experimental trials are needed for identifying RCCAs with desired properties. A coupled computational and experimental approach for exploring a wide range of alloy systems and compositions is crucial for accelerating the discovery of RCCAs that may be capable of replacing Ni-based superalloys. </p> <p>In this thesis, the CALPHAD method was utilized to generate basic thermodynamic properties of approximately 67,000 Al-bearing RCCAs. The alloys were then down-selected on the basis of certain criteria, including solidus temperature, volume percent BCC phase, and aluminum activity. Machine learning models with physics-based descriptors were used to select several BCC-based alloys for fabrication and characterization, and an active learning loop was employed to aid in rapid alloy discovery for high hardness and strength. This method resulted in rapid identification of 15 BCC-based, four component, Al-bearing RCCAs exhibiting room-temperature Vickers hardness from 1% to 35% above previously reported alloys. This work exemplifies the advantages of utilizing Integrated Computational Materials Engineering- and Materials Genome Initiative-driven approaches for the discovery and design of new materials with attractive properties.</p> <p> </p> <p><br></p>

No description available.

No description available.

No description available.

No description available.

No description available.