Senior Design Day 2026

The concrete anchor market is a $2 billion dollar industry with over 100 million concrete anchors sold every year. These anchors are used in a variety of applications, such as construction and manufacturing, to bolt down machinery and other important fixtures.

TerraFix aims to make the installation process for concrete anchors more efficient as there are currently many discrete steps within the overall process, leading to excessive tooling, high labor costs, and inconsistencies in installation quality. By providing a single unified product for technicians while maintaining industry standard strengths for installed anchors, TerraFix addresses these challenges. At its core, TerraFix is an enhancement package consisting of an improved anchor that allows for simultaneous drilling and cleaning, an integrated vacuum, and a hassle free epoxy injection insert. 

The team has currently developed a prototype anchor which has been successfully installed and validated for strength requirements up to 31ksi in 5000 psi concrete, the upper limits of expected usage. Furthermore, prototypes have been developed for the vacuum and epoxy systems, with functionality testing in work.

Team TerraFix is composed of Michelle Lin, Grayson Roberts, and Marissa Teitelbaum and is advised by Jordan Raney, Associate Professor in MEAM.

LoAdsorb is a customized testing chamber designed for the startup Tidal Metals, to characterize adsorption properties of a specialized adsorbent segment. Tidal Metals extracts magnesium from seawater, by electrolyzing magnesium chloride crystallized out of seawater. Their patented adsorbent material enables efficient water removal from brine to isolate the salt. While the adsorbent has already been incorporated into manufacturing workflows following proof of concept, its true efficacy remains unknown. Data collected from further characterization of adsorbent segments is necessary for Tidal Metals to attract more investors and customers, and to further optimize their technology. Existing desiccant characterization techniques do not meet the specific needs and configurations required by the system to accurately simulate operating conditions of the adsorbent segment. LoAdsorb will enable operando measurements by simulating adsorption-desorption cycles within the controlled environment of a vacuum sealed chamber.

LoAdsorb tracks water uptake of the adsorbent segment with time using a mass measurement, while also recording operating conditions such as humidity level, pressure and temperature of chamber atmosphere and segment.

A previous prototype of the testing chamber suffered from ineffective heat exchange, damage to electronic components and condensation. To overcome these issues, Tidal Metals requested redesign of the tester to establish efficient thermal contact between the segment and the heat exchanger and to build reliable electronics for sensing.

Within LoAdsorb a direct contact configuration allows for efficient thermal contact and redesigned electronics bypass previous issues. The load cell has been moved from the bottom of the chamber to the top to allow for easier troubleshooting through the removable top and also to prevent damage due to pooling condensation. We also present data on adsorption performance collected for temperatures in the range X-Y oC.

Team LoAdsorb is composed of Emma Chu, Achala Kankanamge, Peyton Jenkins, and Medha Patel and is advised by Igor Bargatin, Associate Professor in MEAM.

Professional and collegiate sports teams lose out on seasonal revenue and compromise grass health due to reliance on conventional field paints when rendering event-specific graphics on their turf. To address this, our team has developed an autonomous robot that uses lawn striping techniques to create images without paint or any other additives. The product has applications for marketing teams, field maintenance, and audience members for professional and collegiate sports.

GAIA is an autonomous robot that is able to selectively bend regions of grass to produce clear 2-D images, such as logos or advertisements, while maintaining grass health and longevity. GAIA allows multiple cycles of temporary images to be produced on the same surface due to the lack of permanent grass alteration. With a system of rollers and air blowers, GAIA bends grass in one of two directions, producing dark and light “pixels” that produce an image to the viewer using the same principles as lawn striping.

Due to its autonomous nature, the end-user simply uploads an image to GAIA’s software program, where a series of pixels and subsequent robot pathing are generated. The user positions GAIA on the desired field location and starts the printing process. After the printing time of up to three hours, the user will have their image produced cleanly on the grass surface.

Team GAIA is composed of Theodore Kang, Christine Meng, Megan Murray, Riya Nandakumar, Christopher Takoudes and is advised by Bruce Kothmann, Senior Lecturer in MEAM.

AutoField is a semi-autonomous, GPS-guided, field-lining system designed to reduce the time, labor, and physical burden of marking athletic fields in non-professional settings while still meeting governing-body field-marking guidelines. Current professional field-lining solutions are often cost-prohibitive, leaving many schools and community organizations reliant on slow, manual methods. AutoField provides a reliable, affordable, and easy-to-use alternative, targeting athletic coaches, field managers, and athletic directors – particularly in underfunded school districts.

The system aims to reduce field-lining time by approximately 50% compared to manual methods, maintain a total system cost under $2,000, and remain compact and lightweight enough to be transported in a personal vehicle (approximately under 100 Ibs). AutoField features a simple and intuitive user interface requiring minimal technical knowledge: users input field dimensions and the system autonomously navigates the field and applies paint along predefined paths. GPS-based navigation enables precise driving and consistent line placement by calculating a real-time kinematic solution without specialized setup or calibration.

Ongoing development efforts include implementation of GPS-guided navigation for autonomous path following, quality testing of field lines using a wheel-transfer paint system, and integration of motor controllers and drivers to regulate speed, torque, and directional control to ensure precise and repeatable motion. Future work will also focus on integrated system testing and performance evaluation under varying field conditions to ensure reliability and accuracy of our system’s navigation and painted lines, as well as building out a comprehensive logging and watchdog system to ensure constant awareness of driving and ensure no failures slip through.

Team AutoField is composed of Roisin Keenan, Jake Wolfe, Kayla Bleier, David Kukoyi, Rebecca Scheinmann, and Abigail Nibauer and is advised by Nat Trask, Associate Professor in MEAM.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease impairing voluntary muscle control, leading to severe motor limitations and limb weakness. As a result, 40% of ALS patients experience severe malnutrition [1]. Studies show that on average, this increases mortality risk by ~30% [2].

Assistive technologies are critical in preserving autonomy for ALS patients while also alleviating the difficulty of receiving proper nutrition.

Unlike conventional robotic feeders, Allevia Arm restores natural arm motion, with three degrees of shoulder movement and elbow control and a rigid exoskeleton brace that’s easily wearable and enables independent feeding.

The device is designed to operate for 28 minutes, the maximum meal length for ALS patients. Our design features a carbon fiber frame to pass the weight limit on an ALS patient’s shoulders of ≤ 4.3 kg. Four DC motors power shoulder mobility and elbow flexion, with electronics housed in a backpack-style backplate to reduce arm load. Safety is maintained through hardware/software stops and emergency shutoffs.

Currently, the team utilizes a CAN bus to read motor torque, generating speed and position adjustments as a patient attempts to lift their arm. We’ve powered one motor and manufactured final carbon fiber linkages. Next steps include powering the remaining three motors, integrating CAN bus readouts, and applying gravity compensation techniques to filter out arm-weight torque.

Team Allevia Arm is composed of Amar Mohamed, Angelo Sali, Joshua Tiu, Rebecca Wang, and Alvaro Dominguez and advised by Mark Yim, Asa Whitney Professor of Mechanical Engineering.