The purpose of this project is to assess how well today’s youth is able to learn new skills<br/>in the realm of engineering through online video-conferencing resources. Each semester of this<br/>last year, a class of students in both 3rd and 6th grade learned about computer-aided design (CAD)<br/>and 3D printing through their laptops at school. This was done by conducting online lessons of<br/>TinkerCAD via Zoom and Google Meet. TinkerCAD is a simple website that incorporates easy-to-learn skills and gives students an introduction to some of the basic operations that are used in<br/>everyday CAD endeavors. In each lesson, the students would learn new skills by creating<br/>increasingly difficult objects that would test both their ability to learn new skills and their overall<br/>enjoyment with the subject matter. The findings of this project reflect that students are able to<br/>quickly learn and retain new information relating to CAD. The group of 6th graders was able to<br/>learn much faster, which was expected, but the class of 3rd graders still maintained the<br/>knowledge gained from previous lessons and were able to construct increasingly complicated<br/>objects without much struggle. Overall, the students in both classes enjoyed the lessons and did<br/>not find them too difficult, despite the online environment that we were required to use. Some<br/>students found the material more interesting than others, but in general, the students found it<br/>enjoyable to learn about a new skill that has significant real-world applications

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.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.