Smart Connected Factory Automation Engineering Intern
Waterloo, Iowa, United States
- Implemented CVML Cameras via Python2 and Ignition at the end of the engine manufacturing process that automatically inspect engine parts to mitigate errors through my created AI supervised-learning model, thus reducing manual inspection, diminishing warranty claims, and setting the foundation for replacing salary workers.- Engineered an all-in-one Engine Process Tracking dashboard with SQL that consists of a variety of engine data that informs quality engineers of the "engine's footprint" through the manufacturing line, which saves each quality engineer at least 43 hours and $280,000 annually due to reducing walkarounds, heavy repair, and warranty.- Performed 1,100+ scans of the factory floor with live tags so remote workers can view the factory floor's developments along with assisting in factory mastery planning,- Established a troubleshooting tool for the engine line that develops 13.5L and 13.6L engines so quality engineers and mechanical/manufacturing engineers can effectively troubleshoot errors without having to contact factory automation, thusly resulting in less downtime.- Presented in front of 100+ John Deere engineers and management about my long-term impact of the four mini projects on the corporation.