I always wanted to learn programming. Years after a brief exposure to HTML and CSS in school, I finally picked up Python through a free online course. The first serious project that made the language click was a gearbox calculator for my mechanical engineering class. Every assignment involved re-running the same formulas by hand, often taking an hour and leaving plenty of room for mistakes. I knew a script could do better.
Identifying the Workflow
Each assignment followed an identical pattern: gather inputs, apply a series of equations, check units, and present the final forces. I documented the steps in detail, which later informed both the structure of the program and the validation tests I ran after each code change.
Designing the Application
I chose Tkinter for the interface so classmates without programming experience could use the tool. The GUI collects the required parameters, validates them, and triggers the calculation engine. Separating the interface from the logic means I can reuse the formulas in future projects or switch to a web UI later without rewriting the math.
Taming Bugs and Edge Cases
The first iterations were rough. Unit conversions broke silently, inputs were not fully sanitised, and some gearbox configurations produced divide-by-zero errors. I added structured error handling, defensive defaults, and a suite of regression tests comparing program outputs against my earlier handwritten solutions.
Features That Made a Difference
- Input validation that catches impossible combinations before running calculations.
- Clear messaging that highlights which constraint caused an issue.
- Reusable calculation modules so related assignments could be automated quickly.
Impact and Deployment
Once I trusted the results, I demonstrated the tool to classmates and instructors. Beyond saving time, it standardised our methodology: everyone started from the same assumptions and equations. The project also sparked conversations about how lightweight automation can remove repetitive academic work without undermining understanding.
What I Learned
- Shipping useful tools means pairing algorithms with a friendly interface.
- Logging and regression tests are critical even for small student projects.
- Scripting repetitive engineering tasks frees time for deeper analysis.
The full source code is available on GitHub: GertiebeD.
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