High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.

Biomimetics is a field where natural and biological systems are replicated in a lab. The evolved hierarchical designs of the floating leaves of the water fern Salvinia Molesta are taken as inspiration as they reveal excellent dual scale roughness capability which also presents superhydrophobic properties in the nature. The microscale eggbeater-shaped hairs are coated with microscopic granules and nanoscopic wax crystals (dual-scale roughness) and wrinkled hydrophilic patches are coated with wax crystals which are evenly distributed on the terminal of each hair. The combination of features with diverse wettability, such as wrinkled hydrophilic patches atop superhydrophobic eggbeater hairs, makes such structures unique. The hydrophilic patches bind the air-water interface to the tips of the eggbeater hairs and inhibit air bubble formation. Salvinia effect of several Salvinia species has been extensively researched. Superhydrophobicity is attracting increasing attention for various applications. Salvinia exhibit multiscale roughness because of the unique combination of smooth hydrophilic patches on elastic eggbeater structures decorated with nanoscopic wax crystals. However, how to reproduce such hierarchical structures with controllable surface roughness is challenging for current fabrication approaches, which hinders the applications of these superhydrophobic properties as well as multi-scale roughness on surfaces in engineered products.The objective of this research is to fabricate and study the superhydrophobic structures using electrically assisted Vat Photopolymerization. In this project, an electrically assisted Vat Photopolymerization 3D printing (e-VPP-3DP) process was developed to control the surface roughness of printed eggbeater structures with distribution of multi walled carbon nanotubes (MWCNTs) for multi scale roughness. Vat Photopolymerization (VPP) is a Photopolymerization technique where a Photo Curable resin is used to rapidly produce dense photopolymer parts. A fundamental understanding of e-VPP technique to create superhydrophobic structures was studied to identify the relation between geometric morphology and mechanical enhancements of these structures. The correlation between the material properties for different weight percentage mixtures of MWCNT, printing parameters and the mechanical properties like attaching forces, surface roughness and superhydrophobic nature are also identified with this study on bioinspired hierarchical structures.

Ultrasound has become one of the most popular non-destructive characterization tools for soft materials. Compared to conventional ultrasound imaging, quantitative ultrasound has the potential of analyzing detailed microstructural variation through spectral analysis. Because of having a better axial and lateral resolution, and high attenuation coefficient, quantitative high-frequency ultrasound analysis (HFUA) is a very effective tool for small-scale penetration depth application. One of the QUS parameters, peak density had recently shown a promising response with the variation in the soft material microstructure. Acoustic scattering is arguably the most important factor behind different parametric responses in ultrasound spectra. Therefore, to evaluate peak density, acoustic scattering at different frequency levels was investigated. Analytical, computational, and experimental analysis was conducted to observe both single and multiple scattering in different microstructural setups. It was observed that peak density was an effective tool to express different levels of acoustic scattering that occurred through microstructural variation. The feasibility of the peak density parameter was further evaluated in ultrasound C-scan imaging. The study was also extended to detect the relative position of the imaged structure in the direction of wave propagation. For this purpose, a derivative parameter of peak density named mean peak to valley distance (MPVD) was developed to address the limitations of peak density. The study was then focused on detecting soft tissue malignancy. The histology-based computational study of HFUA was conducted to detect various breast tumor (soft tissue) grades. It was observed that both peak density and MPVD parameters could identify tumor grades at a certain level. Finally, the study was focused on evaluating the feasibility of ultrasound parameters to detect asymptotic breast carcinoma i.e., ductal carcinoma in situ (DCIS) in the surgical margin of the breast tumor. In that computational study, breast pathologies were modeled by including all the phases of DCIS. From the similar analysis mentioned above, it was understood that both peak density and MPVD parameters could detect various breast pathologies like ductal hyperplasia, DCIS, and calcification during intraoperative margin analysis. Furthermore, the spectral features of the frequency spectrums from various pathologies also provided significant information to identify them conclusively.

Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.

Titanium has been and continues to be a popular metal across any form of manufacturing and production because of its extremely favorable properties. In important circumstances, it finds itself outclassing many metals by being lighter and less dense than comparably strong metals like steel. Relative to other metals it has a noteworthy corrosion resistance as it is stable when it oxidizes, and due to the inert nature of the metal, it is famously hypoallergenic and as a result used in a great deal of aviation and medical fields, including being used to produce replacement joints, with the notable limitation of the material being its cost of manufacturing. Among the variants of the metal and alloys used, Ti6Al4V alloy is famous for being the most reliable and popular combination for electron beam manufacturing(EBM) as a method of additive manufacturing. <br/>Developed by the Swedish Arcam, AB, EBM is one of the more recent methods of additive manufacturing, and is notable for its lack of waste by combining most of the material into the intended product due to its precision. This method, much like the titanium it is used to print in this case, is limited mostly by time and value of production. <br/>For this thesis, nine different simulations of a dogbone model were generated and analyzed in Ansys APDL using finite element analysis at various temperature and print conditions to create a theoretical model based on experimentally produced values.