This is the drawing I submitted to be machined:

The photo grid below contains renders of the rear drivetrain subassembly for UAlberta Formula SAE's 2024 electric racecar. It contains three of my designs: the pillow block bearing mount for the motor shaft, the rear sprocket, and the sprocket adapter [link to the latter two: https://briannatolsma.myportfolio.com/fsae-rear-sprocket-adapter]

Below is the mesh for an ANSYS Workbench Mechanical static structural simulation I ran. The results beside it show determine the Equivalent Stress (von-Mises) in the part, the Factor of Safety (FOS) of these stresses relative to the material yield strength, and the total plastic deformation under the maximum possible loading conditions—the maximum possible chain tension before the chain is predicted to snap. 

Additionally, the geometry of this part was derived after running several topology optimization simulations to reduce the part's mass in ANSYS Workbench Mechanical, based on the results from the structural analysis above. This was a less documented process because the results were used only as a gross-inspiration for where material could be removed (prior to designing the lightening holes). This was decided based on three considerations:

1. The loading conditions are highly variable and transient, so they are difficult to estimate with high confidence with only a simple structural study. 

2. Topology optimization results in parts with "organic surfaces" which are difficult and expensive to conventionally machine, which was beyond our team's budget. 

3. I was still learning the basics of this tool so there was likely a too much error to blindly rely on the results.

Despite not directly using any of these results, I still have included a few pictures from when I was learning how to use topology optimization below. I want to highlight how I took initiative to learn a tool beyond the scope of my coursework—in fact, these optimized parts took me several days over my winter break to learn how to generate! I recognize that I still have much to learn before I can use this method reliably, but I hope that I will have many more opportunities to continue learning new design tools like this throughout my career.