Uptime set out to create a machine learning algorithm to classify and predict the remaining lifespans of CNC bits. Using machine learning programs to digest the data collected to then give precise and accurate estimates on the parts, being able to tell how certain processes or materials could further degrade or extend lifespan as well. The purpose of this program would be to allow for accurate predictions of needed maintenance before the problem occurs, removing maintenance downtime. Uptime created a variety of programs to achieve this going through several algorithms and iterations, all in the hopes of creating maintenance Uptime

MEK Additive Manufacturing

The objective of this project is to go from a 3D scan of a physical pipe, create a 3D model of the pipe, and complete an FEA analysis and a flow simulation of the pipe's 3D model. With the results of the FEA and the flow simulation, we aim to make a more organic model of the pipe that will be 3D printed, to find potential efficiencies that can be made in both the pipe design and the documentation of the part details. By 3D-printing the pipe, the team is looking allow a reduction in manufacturing time while maintaining part complexity." 

BEEZ KNEEZ

This project is to develop a robotic assistant that can serve food to the disabled. 3D modeling and printing the external components of this robot will be utilized to develop ESP32 microcontroller along with a variety of sensors, including, but not limited to: infrared, inertial measurement unit (IMU), and ultrasonic will be used. These components will be programmed to allow the robot to act autonomously.