While many 3D printed structures are rigid and stationary, the potential for complex geometries offers a chance for creative and useful motion. Printing structures larger than the print bed, reducing the need for support materials, maintaining multiple states without actuation, and mimicking origami folding are some of the opportunities offered by 3D printed hinges. Current efforts frequently employ advanced materials and equipment that are not available to all users. The purpose of this project was to develop a parametric, print-in-place, self-locking hinge that could be printed using very basic materials and equipment. Six main designs were developed, printed, and tested for their strength in maintaining a locked position. Two general design types were used: 1) sliding hinges and 2) removable pin hinges. The test results were analyzed to identify and explain the causes of observed trends. The amount of interference between the pin vertex and knuckle hole edge was identified as the main factor in hinge strength. After initial testing, the designs were modified and applied to several structures, with successful results for a collapsible hexagon and a folding table. While the initial goal was to have one CAD model as a final product, the need to evaluate tradeoffs depending on the exact application made this impossible. Instead, a set of design guidelines was created to help users make strategic decisions and create their own design. Future work could explore additional scaling effects, printing factors, or other design types.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.