Since their invention in the 19th century, polymers have played an essential role, yet their full potential in biomedicine remains largely untapped. Biocompatible polymers, known for their flexibility, accessibility, and modifiability, hold promise in creating complex biomimetic structures for bioscaffolds and biosensors. 3D printing, an emerging manufacturing technique, enables on-demand production of intricate structures, offering significant potential for personalized medicine and advanced biomedical engineering. This thesis focuses on designing and developing polymer-based bioscaffolds and biosensors using 3D printing. Chapter 1 provides an all-round introduction to common 3D printing techniques and polymeric biomaterials, especially biodegradable polymers. In Chapter 2, a gill-mimicking thermoelectric generator (TEG) was created to harvest body temperature and monitor bio-signals without external power. The out-of-plane geometry is obtained with fused deposition modeling (FDM), which is crucial for effective contact with various curved surfaces. Further improvements in biocompatibility enable the material to be implanted in vivo. Chapter 3 discusses UV-facilitated DIW printing for pelvic organ prolapse (POP) tissue scaffolds, featuring crosslink strategies for native tissue-like mechanical behavior. The double network comprises thiol-ene UV-initiated chemical bonds and alkaline-induced crystal regions as physical crosslink nodes. The crosslink density affects the degradation rate of the scaffold, enabling a slow degradation behavior beneficial to the recovery of the injured tissue. Chapter 4 presents a novel artificial artery design with varying moduli and natural polymers for bypass surgeries. The inner and outer layers of the conduit were stretched successively under different strains, endowing the vessel with varying moduli. Natural polymers were utilized to achieve low cytotoxicity and promote adequate cell adhesion. Additionally, the gelation behavior and the ink composition suitable for extrusion with a DIW platform were thoroughly studied. Image analysis, finite element analysis, and machine learning were employed to substantiate the findings regarding mechanical properties, extrusion quality, and printing fidelity in Chapters 3 and 4. This combination of computer-assisted analysis with experimental results enhances the robustness of the studies. Lastly, Chapter 5 provides an outlook and perspectives on the applications of biocompatible polymeric materials manufactured by 3D printing in the field of health applications.

The field of unmanned aerial vehicle, or UAV, navigation has been moving towards collision inclusive path planning, yet work has not been done to consider what a UAV is colliding with, and if it should or not. Therefore, there is a need for a framework that allows a UAV to consider what is around it and find the best collision candidate. The following work presents a framework that allows UAVs to do so, by considering what an object is and the properties associated with it. Specifically, it considers an object’s material and monetary value to decide if it is good to collide with or not. This information is then published on a binary occupancy map that contains the objects’ size and location with respect to the current position of the UAV. The intent is that the generated binary occupancy map can be used with a path planner to decide what the UAV should collide with. The framework was designed to be as modular as possible and to work with conventional UAV's that have some degree of crash resistance incorporated into their design. The framework was tested by using it to identify various objects that could be collision candidates or not, and then carrying out collisions with some of the objects to test the framework’s accuracy. The purpose of this research was to further the field of collision inclusive path planning by allowing UAVs to know, in a way, what they are intending to collide with and decide if they should or not in order to make safer and more efficient collisions.

A key aspect of understanding the behavior of materials and structures is the analysis of how they fail. A key aspect of failure analysis is the discipline of fractography, which identifies features of interest on fracture surfaces with the goal of revealing insights on the nature of defects and microstructure, and their interactions with the environment such as loading conditions. While fractography itself is a decades-old science, two aspects drive the need for this research: (i) Fractography remains a specialized domain of materials science where human subjectivity and experience play a large role in accurate determination of fracture modes and their relationship to the loading environment. (ii) Secondly, Additive Manufacturing (AM) is increasingly being used to create critical functional parts, where our understanding of failure mechanisms and how they relate to process and post-process conditions is nascent. Given these two challenges, this thesis conducted work to train convolutional neural network (CNN) models to analyze fracture surfaces in place of human experts and applies this to Inconel 718 specimens fabricated with the Laser Powder Bed Fusion (LPBF) process, as well as to traditional sheet metal specimens of the same alloy. This work intends to expand on previous work utilizing clustering methods through comparison of models developed using both manufacturing processes to demonstrate the effectiveness of the CNN approach, as well as elucidate insights into the nature of fracture modes in additively and traditionally manufactured thin-wall Inconel 718 specimens.