The world’s population is projected to increase to 10 billion by the year 2050. Traditional agriculture is costly and inefficient compared to Controlled Environment Agriculture (CEA). New technologies have reduced the operational costs of CEA, but humans still perform the harvesting operations, which is costly. The team created a machine that reduces the need for manual labor when harvesting produce from greenhouses or vertical farms.

The machine can cut the leafy greens or microgreens, cut the roots, remove the growing media from the foam growing board and direct these items to the next step in the process. The team focused on using readily available commercial components to minimize cost and improve availability of replacement parts. The design includes hedge trimmer cutting blades, stepper motors and conveyor belts, as well as an aluminum frame with a platform that accepts foam growing rafts. The operator uses a touchscreen linked to a Raspberry Pi single-board computer and an Arduino microcontroller to indicate which product is being harvested.

This product met all of its system requirements and greatly reduced the time and labor costs associated with the harvesting operation in CEAs.

The Smart Lysimeter automates the process of collecting data on the nutrient solution, pH, electrical conductivity (EC), and drainage rate in a greenhouse, eliminating the need for manual labor. These metrics are crucial for maintaining the proper environment for crop growth and resource allocation. This lysimeter fills a market niche by automatically collecting all the necessary data to make real-time greenhouse operation decisions, without requiring high-tech, expensive equipment. It can also be transferred between greenhouses and crops with just a few simple steps.

The design consists of a divided tank, each side equipped with a pH probe, EC probe, fluid level sensor and pump. The two-section tank allows for monitoring of both the solution that drains out of the plants, and the solution being given to the plants. After data is collected, the solutions are pumped out of the system and into a drainage channel so they can be recycled. The team developed software to autonomously collect data from the sensors at appropriate time intervals, process the data and display it in a user-friendly manner. A Raspberry Pi serves as the microcontroller and data storage unit, allowing for a historical log of data.

Optical fibers are used to perform highly accurate laser measurements in photolithography machines. External factors can affect the beam propagation in these fibers, so modifying the laser settings to accommodate the variations leads to higher yield on silicon wafers (semiconductors). This project predicts output field distribution, intensity and power loss from external effects as the light propagates through the fiber.

The team modeled the structure for simulation using MATLAB, which allows integration into the model the sponsor currently supports. The code is modular and derived from preexisting mathematical models. This allows for separate functions to simulate each of the effects independently. The team’s design for the experimental validation component of the project features a supercontinuum laser, and narrow band pass filters to test individual wavelengths. The laser is then coupled into an optical fiber with a bi-convex focusing lens where the fiber is exposed to bending, twisting and thermal effects. A Newport Si power detector records the power before and after coupling.

Traditional manufacturing of aerospace heat exchangers is time-intensive and has a high rate of manufacturing failure. Additive manufacturing heat exchangers would allow for single part construction, reducing potential points of failure. Additive manufacturing also greatly reduces the time needed to create prototypes and allows for a significantly more complex design.

The team designed, tested and produced an additive manufactured heat exchanger prototype to meet sponsor requirements. They also created a material and machine agnostic repeatable workflow package to allow customers to replicate and continue to develop the submitted design. The team also designed and constructed a testing apparatus for verifying that the heat exchanger can meet its requirements. The test equipment can verify the water flow, temperature, pressure and internal integrity. The team also created a verification procedure utilizing the modular testing apparatus in multiple forms.

Parylene coating is a barrier that protects a circuit card from environmental threats, such as contaminants, moisture and temperature, while providing dielectric properties. Its durability makes it an ideal choice as a barrier, but also makes it difficult to remove when the circuit card needs to be repaired. Removal requires a lot of time, money, labor, and even dangerous chemicals, which can pose potential damage risks. In this project, the team tested and provided various methods, such as abrasion, reactive ion etching, and laser ablation, to effectively remove the coating without damaging the board. They tested the laser removal with a device they designed and built, which can be controlled by a user interface and allows for complete board and spot removal of Parylene.

Circuit board longevity is a key factor in electrical and electronic equipment. Boards are constantly introduced to varying environmental conditions, including extreme heat, high relative humidity and below freezing temperatures. The specific production processes used along with the differing levels of voltages applied introduce even more variables that may impact longevity. It is necessary to understand why some boards fail. The team designed an experiment which tests key factors to provide data and understand failures of printed circuit boards.

The experiment is based on varying three factors: voltage, flux volume and pad spacing. The team developed a circuit board which optimized the number of tests per board while meeting design of experiment requirements. They used a humidity/temperature chamber to match desired environmental conditions, applied flux to each board at three differing levels, and applied voltage sources ranging from 15V to 50V DC. Pad spacing allowed for further variation. The boards were held under constant environmental conditions for a period sufficient to encourage dendritic growth. The team noted current leakage, recorded the time of failure and presented relevant statistics. Ion chromatography permitted further understanding of the factors causing dendritic growth and current leakage.

Understanding and analyzing the human body’s physiological processes can be useful, but most of them cannot be seen with the naked human eye. Processes that could be of interest include blood content, oxygen levels, and perspiration levels – all of which directly relate to overall human health. This project offers a safe, low-cost and efficient way to image physiological processes.

The system hardware consists of 13 pairs of LEDs of varying wavelengths connected to an imaging system. The camera collects an image with each LED pair turned on, along with a single image with all LEDs turned off, resulting in 14 total images per system cycle. The Rapid Multispectral Imaging System (RMIS) can collect 500 photos per second and provide 10 frames per second of video output. After the photos are captured, the Python code calibrates the image using techniques such as flat field correction and background subtraction. Based on the optical properties of the imaging target, the code then analyzes the calibrated images to obtain physiological data. The resulting images are displayed on a graphical user interface, which also contains options for selecting the imaging target, analysis, wavelength and physiological data of interest. The system offers a wide range of applications in medical research and allows for adjustments to meet the needs of specific scenarios.

Self-driving cars, or autonomous vehicles, have come a long way, but there is still much research to be done and many improvements could be made, particularly when it comes to human safety. The team has partnered with the Institute of Automated Mobility on simulating real world-driving situations to help and train autonomous vehicles for interactions with traditional automobiles. The design uses a preexisting axial radio-controlled car with enhancements of the wheels, suspension and frame to facilitate the sensor package data collection for position tracking. The team designed a visual tracking system and mounted it to the chassis for camera recognition. These enhancements calibrate and validate the visual tracking, while natively storing the position of the vehicle as it navigates its environment.

The manufacturing of safety-critical systems, such as aircraft battery packs, requires a high level of precision and consistency that is difficult to achieve with human labor alone. Factory automation presents an opportunity for manufacturers of such systems to significantly improve the quality and efficiency of their operations. This team designed and built a low-cost, portable fixture to automate the attachment of an adhesive heater blanket to a set of battery cells.

The final design uses a series of stepper motors attached to 3D-printed clamp arms. The motors are controlled by a Raspberry Pi running Python code developed by the team. Attached to the Pi is a touchscreen which displays a graphical user interface (GUI). Users of the system can load a set of battery cells into the fixture and clip a heater blanket to the clamp arms. The GUI can then start the application, which consists of the clamp arms automatically closing around the battery cells and in the process accurately adhering the blanket to the cells. The system also features an emergency stop function, allowing the user to halt the application of the blanket at any point.

Elephants walk extreme distances to forage for food in wild habitats. To simulate a more realistic environment for the elephants at the Reid Park Zoo, the team created a device that uses a programmable control system to distribute food pellets throughout the elephant habitat. The device can operate in harsh Arizona weather conditions and includes a refillable hopper to minimize zookeeper interaction with the animals. The remotely operable device aims to further the realism of the elephant habitat by encouraging more elephant exercise, requiring them to search for food rather than depending on human interaction. The most important design constraint was ensuring that the device does not cause any harm or distress to the elephants.

To allow zookeepers to easily operate the system, the unit includes a touchpad interface to program launch times, distance and angle; a remote to initiate launches from a distance; a large-capacity storage hopper; and a pneumatic pellet launching mechanism.