Doctor of Philosophy (Ph.D.), Bowling Green State University, 2012, Biological Sciences

Many hairpin and internal RNA 3D motif structures are recurrent, occurring in various types of RNA molecules, not necessarily homologs. Although usually drawn as single-strand “loops” in RNA 2D diagrams, recurrent motifs share a common 3D structure, but can vary in sequence. It is essential to understand the sequence variability of RNA 3D motifs in order to advance the RNA 2D and 3D structure prediction and ncRNA discovery methods, to interpret mutations that affect ncRNAs, and to guide experimental functional studies. The dissertation is organized into two parts as follows. First, the development of a new online resource called RNA 3D Hub is described, which is intended to provide a useful resource for structure modeling and prediction. It houses non-redundant sets of RNA-containing 3D structures, RNA 3D motifs extracted from all RNA 3D structures, and the RNA 3D Motif Atlas, a representative collection of RNA 3D motifs. Unique and stable ids are assigned to all non-redundant equivalence classes of structure files, to all motifs, and to all motif instances. RNA 3D Hub is updated automatically on a regular schedule and is available at http://rna.bgsu.edu/rna3dhub. In the second part of the dissertation, the development of WebFR3D (http://rna.bgsu.edu/webfr3d), a new webserver for finding and aligning RNA 3D motifs, is described and its use in a biologically relevant context is then illustrated using two RNA 3D motifs. The first motif was predicted in Potato Spindle Tuber Viroid (PSTVd), and the prediction was supported by functional evidence. The second motif had previously been undescribed, although it is found in multiple 3D structures. RNA 3D Hub, RNA 3D Motif Atlas, and the bioinformatic techniques discussed in this dissertation lay the groundwork for further research into RNA 3D motif prediction starting from sequence and provide useful online resources for the scientific community worldwide.

PHD, Kent State University, 2022, College of Arts and Sciences / Materials Science Graduate Program

Three-dimensional (3-D) tissue scaffolds produce suitable environments for cell growth and proliferation for longer periods of time compared to traditional two-dimensional (2-D) tissue culture and, act as appropriate models for the study of cell-cell/cell-scaffold interactions. 3-D models also allow to study cell activities and their functions as well as to evaluate diseases or tissue damage. Liquid crystal elastomers (LCEs) have intrinsic anisotropy and have shown to promote cell alignment and orientation and the presence of liquid crystals (LCs) at a molecular level with and without the use of external stimuli. The work presented in this thesis is the synthesis, design, and creation of 3-D LCEs scaffolds that support several cell lines to promote tissue regeneration. LCEs scaffolds have shown to meet all tissue scaffold requirements to support cell proliferation and growth since they can potentially be biocompatible, biodegradable and their properties, such as porosity and mechanical properties, can be tuned to adapt and match to different cell lines to obtain a suitable tissue. To tune porosity size and density, a salt leaching method was used. Cellulose nanocrystals (a biocompatible additive) were used as an additive to tune the mechanical properties of LCEs to match the young modulus of specific tissues. Once 3-D LCE scaffolds were produced to specifically match neural-like cell lines, we proceed to co-culture neuroblastoma and a glial cell lines (oligodendrocytes). When neuroblastomas are in presence of oligodendrocytes, myelin sheet is formed around the axon of neurons. We observed myelination of neurons during our co-culturing efforts allowing us to study its formation well over several weeks. Our findings will lead researchers on brain degenerative diseases such as Multiple Sclerosis (MS) to have a more appropriate model to quantify, monitor, and find treatments for demyelination and myelination of neurons. Last but not least, in this thesis we will sho (open full item for complete abstract)

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Detecting geometrical edges magnitude of the surface of the point clouds is still an important problem that has a wide range of applications such as object recognition and detection, and simultaneous localization and mapping. The objectives of this dissertation is to use the high-pass spatial filter of the first-order (the Gradient) derivative of a field of vectors and the second-order (the Laplacian) derivative of a field of vectors, that handle the vectors as one solid quantity instead of separate layers of axes, to detect the 3-D geometrical edges magnitudes and directions, of the surface of the three-dimensional images (point clouds), in a way that is analogous to the typical using of the Gradient of a field of scalars and the Laplacian of a field of scalars to detect intensity edges magnitude and directions in gray-scale images; The problem statement is that the state of the art high-pass spatial filter of the Gradient and Laplacian operators are not applicable to the vector quantities because they are based on the component-wise Cartesian vector subtraction operation. The contributions of the first part of this dissertations are that: (1) a novel mathematical Solid Vector subtraction operation is proposed that handle the vectors as one solid quantity, instead of separate layers of axes; (2) novel definitions of the geometrical Step edge, Plane and Ramp geometrical areas are proposed; (3) based on the proposed Absolute Solid Vector subtraction operation, a novel algorithm is proposed for the first-order (the Gradient) derivative of a field of vectors to detect geometrical edges magnitude, and to classify Plane segment, and Step and Ramp segment. (4) behavioral analyses is done on the geometrical Step, Plane, and Ramp areas; and (5) performance analyses is done on TUM data set; and comparison study is done on NYUD data set that shows the less complex Gradient geometrical edge detector is efficient. While the contributions of the second part of this d (open full item for complete abstract)

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Mathematical operations that are applicable to the three and hyper-dimensional space remain a key factor that act as a foundation for a wide range of applications of 3-d image processing and computer vision. The recently proposed Solid Vector Subtraction opened the way to propose the high pass spatial filters of a field of vectors geometrical edge detectors. Geometrical edge magnitude and direction detection has a wide spectrum of applications including object recognition and detection, autonomous navigation systems, and three-dimensional localization and mapping. This dissertation proposed three research topics: (1) the first is the Solid Vector Addition, Multiplication, Division, Dot Product and Cross Product operations and their coordinate system definitions; (2) the second is the Gradient-based Laplacian of a field of vectors; and (3) the third is the Unsharp Masking of a field of vectors. The problem statement of the first research proposal is to define the Solid Vector: (1) coordinate system (like whether it is Spherical or Polar or something else); (2) hyper dimensional space; (3) whole complementary set of addition, multiplication, division, dot product and cross Product operations. The problem statement of the second research is that the definition of the Gradient-Based Laplacian of a field of vectors (used for detecting 3-D surface geometrical edges) depends on the definition of the Gradient of a field of vectors which was only just recently proposed. Also, the Gradient-Based Laplacian must comply with the typical rules for the Laplacian operator. The problem statement of the third research is that the Cartesian Components-Wise mathematical subtraction operation used in the Unsharp Masking of a field of scalars is not useful to develop the Unsharp Masking of a field of vectors for geometrical edge magnitude and direction detection in point cloud surfaces. The contributions of the first research are proposing novel definitions of: (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2014, Geodetic Science and Surveying

Knowing the position of an object in real-time has tremendous meaning. The most widely used and well-known positioning system is GPS (Global Positioning System), which is now used widely as invisible infrastructure. However, GPS is only available for outdoor uses. GPS signals are not available for most indoor scenarios. Although much research has focused on vision-based indoor positioning, it is still a challenging problem because of limitations in both the vision sensor itself and processing power. This dissertation focuses on real-time 3D positioning of a moving object using multiple static cameras. A real-time, multiple static camera system for object detection, tracking, and 3D positioning that is run on a single laptop computer was designed and implemented. The system successfully shows less than ±5 mm in real-time 3D positioning accuracy at an update rate of 6 Hz to 10 Hz in a room measuring 8×5×2.5 meters. Implementation and experimental analysis has demonstrated that this system can be used for real-time indoor object positioning. In addition, `collinearity condition equations of motion' were derived that represent the geometric relationship between 2D motions and 3D motion. From these equations, a `tracking from geometry' method was developed that combines these collinearity condition equations of motion with an existing tracking method to simultaneously estimate 3D motion as well as 2D motions directly from the stereo camera system. A stereo camera system was built to test the proposed methods. Experiments with real-time image sequences showed that the proposed method provides accurate 3D motion results. The calculated 3D positions were compared with the results from an existing 2D tracking method that uses space intersection. The differences between results of the two methods were less than ±0.01 mm in all X, Y, and Z directions. The advantage of the tracking from geometry method is that this method calculates 2D motions and 3D motion simultaneously, w (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 2025, EECS - Computer and Information Sciences

In computer vision, enabling computers to understand the dynamic content in image sequences has always been a challenging problem. Our research aims to construct an explicit motion representation through rigorous mathematical derivation to describe the motion of surfaces in 3D space. This work can provide a solid theoretical foundation for building more complex systems in the future, ultimately enabling computers to fully comprehend the concept of motion. Starting from the mathematical representation of fundamental motion patterns, we developed a basic time-varying equation. This equation was then extended to fully represent the motion of surfaces in 3D space. Using this extended time-varying equation, we constructed a generative model capable of simulating the motion of finite planes with arbitrary textures and shapes undergoing any single pattern of motion in 3D space. The corresponding motion representation of this model is both disentangled and steerable. Building on this foundation, we further explored how this generative model can be used to perform motion inference on synthetic data.

Master of Science (MS), Ohio University, 2024, Biomedical Engineering (Engineering and Technology)

With current medical practices, biomaterials have provided new avenues to improve life and remedy ailments like cancer and trauma. The ever-expanding library of suitable monomers and polymers that are bio-focused, in conjunction with Additive manufacturing (AM), has allowed the production of complex and tailorable/customizable medical devices. Throughout this thesis, two polymer systems, naturally derived and petroleum-based, will be explored to identify their various material properties to produce tissue scaffolding utilizing AM technologies. The naturally derived polymer system consists of the various ratio copolymers of limonene and β-myrcene. By varying the ratio of the two monomers we can achieve an ultimate tensile strength (UTS) range between nearly 2.5 MPa to exceeding 55 MPa. In addition, the petroleum-based system was investigated yielding a copolymer of norbornene anhydride and styrene oxide with various molecular weights, using a thiol-ene clickable-based resin that can exhibit plasticization-based mechanical changes when exposed to water at the biological temperature of 37 °C. In addition, the material exhibits the capabilities of both direct ink writing (DIW) and digital light processing (DLP). These polymeric systems expand the material library of biomaterials to enable the production of revolutionary tissue scaffolds to be leveraged in future devices.

Master of Fine Arts, The Ohio State University, 2024, Design

This master's thesis used a research-through-design approach to determine how themed elements & interactive microcontroller consoles could improve the sense of presence in immersive experiences. The hypothesis was that removing handheld controllers and allowing people to touch and feel the environment they were seeing in a virtual reality headset would allow them to natively explore and engage with contextual interactive elements. The final product, Project Orbweaver, was an exciting multi-disciplinary immersive experience that transported players to a cosmic environment beyond our solar system. The experience was comprised of four elements interacting in unique ways to deliver an exciting virtual reality attraction. The first element was the virtual environment & VR component, tasked with immersing players in the teleporter and space station scenes. The second element was the microcontroller interaction system featuring three interactive stations with minigames for the player to complete. The third element was the theming and preshow that immersed players in the story. Finally, the fourth element was the live interaction between the player and experience facilitator; Everybody that came through the experience got slightly unique dialogue and conversation based on how they approached the minigames on the interactive stations. This thesis serves as a documentation of the development process while also presenting a framework that can be used to create similar experiences.

Master of Science, The Ohio State University, 2024, Electrical and Computer Engineering

Nowadays, with the advent of an increasing variety of neural networks in the Multi-View Stereo (MVS), machine learning methods have become widely utilized in MVS. However, these networks have achieved considerable success on datasets that are relatively small in nature, such as DTU, BlendMVS, Tanks and Temples and ETH3D, which artificial and scene miniatures such as cups, tanks, LEGO buildings in controlled scenes. We observe that the application of large-scale outdoor aerial datasets in MVS deep neural networks is significantly lacking in prior discussions. After applying MVSFormer (a member of deep learning based MVS dense matching approaches) on a large-scale outdoor aerial dataset, we discovered that the network performed poorly in predicting depth maps. This issue is common for almost all the MVS networks. It is because that MVS networks are not able to detect the occlusions on reference images, and enabling the occlusions to participate the loss calculation can be negative to the convergence of loss function, leading to the awful effective on predicted depth maps. In this thesis, we firstly identify such problems arising from the network structure when such networks are applied to large-scale urban datasets. Then, we propose a simple method of setting a threshold to exclude those occlusions from loss calculation. Ultimately, our approach significantly reduces the mean absolute errors and enhances the completeness of the predicted depth maps.

MS, Kent State University, 2023, College of Architecture and Environmental Design

Three-dimensional concrete printing (3DCP) is a rapidly growing field within the architecture and construction industries. It has the potential to address many challenges of the present, given its speed of fabrication, design flexibility, and material efficiency. Yet, there has been minimal research investigating the thermal potential of 3DCP. As well, concrete is known for its high embodied energy. With the consequences of Climate Change increasingly present, the need to explore new and sustainable manufacturing methods, materials, and building systems is critical to reduce the environmental impact of the construction industry. Therefore, it is important for new technologies such as 3DCP to consider thermal analysis in the design and development stages of material research. Specifically, through three-dimensional (3D) printing, concrete blocks and wall assemblies are no longer limited to the existing standards of traditional concrete assemblies. Instead, by harnessing the potential tooling path generated during the printing process, this research aims to investigate the thermal potential of a 3DCP block through the optimization of an interior infill pattern. As well, though 3D printed components can be rapidly generated using computer-aided design (CAD) and computer-aided manufacturing (CAM) systems, the thermal analysis of these components has been limited by time consuming and expensive material testing procedures. This research highlights the benefits of simulating a Hot Box apparatus using Finite Element Analysis (FEA) to analyze the thermal resistance of 3D printed concrete blocks. By integrating FEA alongside volumetric optimization, this work generated concrete blocks with lower concrete volumes and higher thermal resistance than standard concrete masonry units of the same width.

Master of Science in Biomedical Sciences (MSBS), University of Toledo, 2023, Biomedical Sciences (Medical Physics: Radiation Oncology)

Breast cancer is the most common type of cancer in women. More than 287,000 cases of aggressive breast cancer were estimated to have been diagnosed in 2022, leading to roughly 43,000 deaths. Breast radiotherapy has been shown through more than 50 years of clinical trials to be as effective as other treatments such as mastectomy. Clinical efficacy combined with frequent breast cancer diagnosis means that a significant amount of time will be spent by clinical staff planning breast radiotherapy treatments. If the process of treatment planning could be automated, then this same clinical staff would have more time to devout to other less routine cases. The aim of study is to design a script protocol to automate the process of beam segment design and machine output weighting, then to test this protocol against datasets of different geometries. A beam segment generation and monitor unit (MU) weighting protocol was developed and designed within the RayStation treatment planning system (TPS) python software IDE. The design of the protocol is to take human designed open fields and create sub-fields, or segments (FiF – or Field-in-Field), to increase the homogeneity of the radiation distribution. The protocol was tested on 10 CT datasets with five right side breast targets and five left side breast targets. Dose-volume statistics, dose distributions, and homogeneity index were calculated along with other plan parameters to test against recent published literature. The study found an average homogeneity index of 0.10 across all 10 plans, and no hot spot above 107% when normalized to 95% of prescription dose to 95% of the target volume (or D95=95%). This normalization to the prescription dose level is the gold standard in clinical trials, including the Fast, Fast-Forward, and RTOG-1005 trials. The study also noted higher than expected out-of-field dose to the contralateral breast. The homogeneity of the radiation distribution of the plan is the only factor which was sign (open full item for complete abstract)

Master of Science, The Ohio State University, 2022, Dentistry

Objectives: The purpose of this in vitro study was to evaluate the fabrication and seating accuracy of surgical guides fabricated by using 3 different types of 3D printing technologies. Methods: Twenty-one identical polyurethane models were divided into 3 groups and used to plan implants and design surgical guides using digital software. Twenty-one surgical guides were fabricated using 3 different 3D printing technologies: digital light processing (DLP), stereolithography (SLA), and continuous liquid interface printing (CLIP)(n=7). A digital scan of the printed surgical guide was made with an intraoral scanner and the scan file was compared to the CAD file to analyze the fabrication accuracy. Accuracy was evaluated on the internal cameo seating surface as well as the overall external surface of the surgical guide. Then, the triple scan protocol was used to evaluate the seating accuracy of the guides on their respective models. A metrology grade superimposition software was used to calculate accuracy. Results: Overall there was a statistically significant interaction between the 3D printer and the accuracy of the guide compared to the CAD file (p<.001). The trueness of the surgical guides was significantly different for the internal cameo surface but not for the overall external surface of the surgical guide. SLA had the lowest mean RMS deviation (59.04μm) for internal surface of the guide while CLIP had the highest mean RMS (117.14μm). CLIP had the lowest mean RMS (82.25μm) for the overall external surface of the guide while DLP had the highest mean RMS (91.00μm). SLA and DLP seating accuracy was not significantly different (p=1.000) but, both had significantly lower mean RMS values than CLIP (p=0.003, p=0.014). All 3D printing technologies had low variability amongst measured deviations and therefore were similarly precise. Conclusions: Overall, the 3D printers tested produced precise surgical guides. However, 3D printing technology effected (open full item for complete abstract)

Master of Science in Engineering, Youngstown State University, 2022, Department of Civil/Environmental and Chemical Engineering

Additive manufacturing (AM) commonly referred to as 3D printing is a method of manufacturing three-dimensional parts in a layer-by-layer fashion. Common materials used in this process are polymers, metals, and ceramics. Nowadays, AM is utilized for more than just traditional structures - it is used to fabricate and create nontraditional designs. Additive manufacturing is associated with various industrial manufacturing processes and innovations including maintenance, repairs, and product design. Among the different applications of this process, the production of 3D printed morphing systems and parts for batteries represents an attractive approach for yielding high-performance structures. Non-metallic morphing components are commonly constituted by shape memory polymers (SMPs), which are actuating materials that can respond to thermal, electrical, or chemical stimuli. Here, SMPs were constructed by incorporating two different blends of photopolymer resins in a Vat Photopolymerization process. The printed SMPs were subsequently electroplated with copper to yield a conductive morphing structure for applications such as sensors, actuating systems, and functional antennas. The present work investigated the adaptability and functionality of the copper-plated 3D printed parts as morphing antennas capable of providing a multi-radio frequency. Additionally, this research program investigated the production and performance of 3D printed LiFePO4 parts via Vat Photo Polymerization to be used as electrodes on additively manufactured energy storage devices. This effort represents a novel approach to further expanding the production of customized batteries.

Master of Science in Engineering, University of Akron, 2022, Mechanical Engineering

Background: While cochlear implant (CI) surgery, for the hearing impaired, is a relatively safe and standard procedure in the hands of experienced surgeons, they need years of experience dealing with different cases, patients, and implants. Studies have shown that the overall complication rate for CIs is around 18%. Typical complications include infection, injury to the facial nerve, CI migration from its osseous bed, electrode array dislodgement from the cochlea and/or suboptimal electrode placement such as incomplete electrode insertion, electrode kinking, and electrode tip fold-over. Although devices such as mastoid fitting templates have been developed to improve electrode insertion trajectory, extensive training is still required. Current methods that use cadavers, virtual training sessions, or 3D-printed models from reconstruction images are still not good enough to train early career surgeons. This thesis presents a novel 3D-printed physical cochlear model that simulates the dimensions, texture, and feel of inserting the electrode into the inner ear canal. The model is 3D-printed from SolidWorks drawings created from snail shell geometry and actual human cochlear measurements. The material is a transparent plastic. The entire insertion process can be observed in real-time using a camera and a specially designed Graphical User Interface (GUI) that not only shows the real video feed, but also provides depth, trajectory, and speed measurements. Best practice benchmarks were developed using trials by a senior surgeon with over twenty years of experience. Trials: Three sets of clinical trials were then conducted on medical residents, fellows, and early career surgeons. Each clinical trial was part of a medical training lab held at Mercy Health Hospital in Youngstown, Ohio. The first trial was used to finalize the physical 3D-printed model prototype and establish benchmarks. The second trial was used to finalize the GUI and demonstrate that training via the mode (open full item for complete abstract)

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

Earlier, various attempts to develop graphene structures using chemical and non-chemical routes were reported. Being efficient, scalable, and repeatable, 3D printing of graphene-based polymer inks and aerogels seems attractive, however, the produced structures highly rely on a binder or ice support to stay intact. The presence of binder or graphene oxide hinders the translation of the excellent graphene properties to the 3D structure. In this thesis, we report our efforts to synthesize a 3D-shaped 3D graphene (3D2G) with good quality, desirable shape, and structure control by combining 3D printing with the atmospheric pressure CVD process. Direct Ink Writing has been used in this work as a 3D printing technique to print nickel powder-PLGA slurry into various shapes. The latter has been employed as catalysts for graphene growth via CVD. Porous 3D2G with high purity was obtained after etching out the obtained nickel-graphene composite. The conducted Micro CT and 2D Raman study of pristine 3D2G revealed important features about the internal structure of this new material. The interconnected porous nature of the obtained 3D2G, combined with its good electrical conductivity (about 17 S/cm) and promising electrochemical properties invites applications for energy storage electrodes, where fast electron transfer and intimate contact with the active material and with the electrolyte are critically important. By changing the printing design one can manipulate the electrical, electrochemical, and mechanical properties including the structural porosity, without any requirement for additional doping or chemical post-processing. The obtained binder-free 3D2G showed a very good thermal stability, tested by Thermo-Gravimetric Analysis (TGA) in air up to 500o C. This work brings together two advanced manufacturing approaches, CVD and 3D printing thus enabling the synthesis of high-quality, binder-free 3D graphene structures with a tailored design that appeared to be suitable f (open full item for complete abstract)

Master of Science in Pharmaceutical Science (MSP), University of Toledo, 2021, Pharmaceutical Sciences (Industrial Pharmacy)

Patient-specific medicine is a growing area of treatment in the healthcare sector and additive manufacturing, or 3D printing technology is a recent pharmaceutical approach to confront the challenge of this individualized drug delivery system. The focus of this study was to investigate the feasibility of formulating a 3D printed personalized dosage form using fused deposition modelling (FDM) in combination with hot-melt extrusion (HME) process. Acetaminophen was selected as a model drug and a commercial polyvinyl alcohol (PVA) filament was used to fabricate 3D printed tablets with two different drug loading percentages. After screening several polyvinyl alcohols (PVA), the commercial PVA filament was selected to enhance the extrusion process. 5% and 15% acetaminophen loaded filaments were successfully extruded through a filament extruder and tablets were printed using an FDM 3D printer. Thermal analysis using DSC and TGA confirmed the thermal stability of 3D printed tablets. No endothermic events corresponding to acetaminophen were observed in the DSC thermograms of drug-loaded filaments and tablets indicating that the drug was amorphously dispersed in PVA. With TGA, the drug-loaded filaments and tablets did not show any appreciable weight loss at the printing temperature of 240 ˚C suggesting that the polymer was stabilizing the drug. Molecular interactions of acetaminophen and PVA on drug-loaded tablets were verified through FTIR analysis. SEM micrographs of cross-sectioned drug-loaded filaments appeared to have a rough surface in compare to the commercial PVA filament due to the inclusion of acetaminophen, which was consistent with the drug-loaded tablets as well. Physical and mechanical characterization was performed according to mandated standards. The 3D printed tablets passed the weight variation, friability, thickness, dimensions, and breaking force tests with minimal outliers. Drug content loss was analyzed using a validated HPLC method. HPLC data demonstrat (open full item for complete abstract)

Doctor of Philosophy, University of Akron, 2020, Polymer Science

4D printing is an emerging technology that combines 3D printing and stimuli-responsive materials. The core idea of 4D printing is `3D printing +time', where the geometries, properties and functionalities of 3D printed structures can evolve as a function of time. While simple shape transformation has been achieved in most 4D printing systems, more limited attention has been paid to 4D printing with multi-functionality or complex shape transformation patterns. However, these properties are of critical importance for advanced applications. For example, sequential shapeshifting is useful for consecutive motion and actuation of soft robotics. High flexibility and extensibility are desired properties in soft electronics and some biomedical devices. To address these limitations, we developed functional 4D printing based on rational designs of shape memory polymers at molecular, morphological and geometrical levels. First, we achieved the first triple shape memory polymer by digital light processing (DLP) 3D printing. We prepared DLP printable resins containing an ion-pair comonomer and acrylates, and obtained ampholytic ionomers through photocuring. These ionomers featured microphases-separated morphology, which generated two glass transition temperatures (Tg) associated with the ion-rich and ion-poor domains. The well-separated Tgs produced excellent triple shape memory effect. The influences of neutral comonomers on microphase separation and Tgs were investigated systematically by dynamic mechanical analysis (DMA) and atomic force microscopy (AFM). Finally, sequential shapeshifting through different shape evolution pathways and its application in microfluidics were demonstrated. Second, we 4D printed a highly extensible, self-healing shape memory elastomer based on fused filament fabrication (FFF) 3D printing. The material was made of a thermoplastic elastomer, polystyrene-block-poly(ethylene-co-butylene)-block-polystyrene (SEBS), and a semi-crystalline thermoplastic, (open full item for complete abstract)

Master of Science in Engineering, Youngstown State University, 2019, Department of Mechanical, Industrial and Manufacturing Engineering

Additive manufacturing is one of the more recent advances in manufacturing technology. Additive manufacturing processes allow for the creation of parts in a layer-by-layer fashion. There are several materials that can be used in additive manufacturing processes including metal, ceramic, and polymers which each presenting their own challenges. This work focuses on metal based additive manufacturing parts made out of AlSi10Mg using a process called laser powder bed fusion. Laser powder bed fusion is one of the three major metal additive manufacturing processes with the other two being multi-pass welding and direct energy deposition. One of many challenges that occur with the laser power bed fusion process is minimizing the residual stresses and distortion that are present in the part during and after the build. During the early days of additive manufacturing that was mostly done through a trial-and-error process where multiple version of a part would be printed until a desired outcome was achieved, and this was often very expensive, and time consuming. There has been plenty of research in developing simulation models in order to predict the distortions and stresses that developed during the additive manufacturing process. These simulations allowed engineers to optimize parts before they were printed, and thus reduce the number of wasted prints. This work demonstrates and validates use of a software package call Autodesk Netfabb Simulation in order to find the optimal orientation of a complex part. The optimal orientation was selected for three categories: distortion, stress, and printability. Optimal orientations were selected from a selection of 23 orientations that were simulated. To validate the simulations, two test parts along with three of the aforementioned orientations were printed and measured using 3D scanning while still the build plate. The result of this was that the optimal orientation was different for each of three criteria meaning it is up to the part (open full item for complete abstract)

Master of Science, Miami University, 2019, Computer Science and Software Engineering

Having high resolution Infra-Red (IR) imagery in cluttered environment of battlespace is crucial for capturing intelligence in search and target acquisition tasks such as whether or not a vehicle (or any heat source) has been moved or used and in which direction. While 3D graphic simulation of large scenes helps with retrieving information and training analysts, using traditional 3D rendering techniques are not enough, and an additional parameter needs to be solved due to different concept of visibility in IR scenes. In 3D rendering of IR scenes, the problem of what can currently be seen by a participant of the simulation does not just depend on emitted thermal energy from objects, and the visibility also depends on previous scenes as thermal energy is slowly retained and diffused over time. Therefore, time as an additional factor must be included since the aggregation of heat energy in the scene relates to its past. Our solution uses lightmaps for storing energy that reaches surfaces over time. We modify the lightmaps to solve the problem of lightmap parameterization between 3D surfaces and 2D mapping and add an extra ability to let us periodically update only necessary areas based on dynamic aspects of the scene.

Master of Science in Electrical Engineering (MSEE), Wright State University, 2018, Electrical Engineering

Synthetic aperture ladar (SAL) is similar to synthetic aperture radar (SAR) in that it can create range/cross-range slant plane images of the illuminated scatters; however, SAL has wavelengths 10,000x smaller than SAR enabling a relatively narrow real aperture, diffraction limited beam widths. The relatively narrow real aperture resolutions allow for multiple slant planes to be created for a single target with reasonable range/aperture combinations. These multiple slant planes can be projected into a single slant plane projections (as in SAR). It can also be displayed as a 3-D image with asymmetric resolutions, diffraction limited in the dimension orthogonal to the SAL baseline. Multiple images with diversity in angle orthogonal to SAL baselines can be used to synthesize resolution with tomographic techniques and enhance the diffraction limited resolution. The goal of this research is to explore methods to enhance the diffraction limited resolutions with multiple observations and/or multiple slant plane imaging with SAL systems. Specifically, metrics associated with the information content of the tomographic based 3 dimensional reconstructions of SAL intensity imagery will be investigated to see how it changes as a function of number of slant planes in the SAL images and number of elevation observations are varied. Approved for public release, distribution unlimited (APRS-RY-18-0785)