Ryland Zimany
Mechanical Engineering · Penn State

About

My name is Ryland and I am a Mechanical Engineering student in my junior year at Penn State University. My concentration in the field of engineering is electromechanical systems, mechanical design, and prototyping. With the help of undergraduate research and engineering projects, I have gained experience in CAD, fabrication, Arduino, control systems, MATLAB, and Python among other programming languages. My goal is to pursue a career in electromechanical engineering, developing innovative technologies through hands-on design, testing, and iterative problem-solving.

Education

B.S. Mechanical Engineering, Pennsylvania State University · Expected 2028

Activities

Nittany Motorsports chassis subteam · Stringimeter research development

Experience

Dec 2025 – Present

Colby Research Group

Undergraduate Researcher | Penn State University

Contributed to polymer research through laboratory equipment assembly and testing and collaboration with researchers across technical disciplines. Assisted with developing and maintaining research capabilities for investigating polymer material behavior under stretched conditions.

Jan 2025 – Present

Nittany Motorsports · Chassis Subteam

Chassis CAD and fabrication, including composite components, as part of the Penn State student motorsports team.

Selected work

Projects

Stringimeter components and measurement diagrams
PrototypingArduinoResearch

Low-Cost Stringimeter Assembly

Assembled a modular, low-cost extensional rheometer prototype from printed parts, mechanical components, and electronics.

Open project
Carbon fiber chassis panel fitted inside the Nittany Motorsports vehicle
Nittany MotorsportsCarbon FiberFabrication

Nittany Motorsports — Carbon Fiber Chassis Fabrication

Contributed to chassis research and development, fabricating lightweight composite components to construct the monocoque.

Open project
Motor-driven test rig with electric motor and instrumentation
PythonSensorsConceptPersonal Project

Motor Predictive Maintenance

Developing a machine learning-based condition monitoring system to detect faulty operation in 12 V brushed DC motors.

Open project
Stringimeter components and measurement diagrams
Research · Instrumentation · Prototyping

Low-Cost Stringimeter Assembly

Assembled the components for a low-cost, modular extensional rheometer for polymer research. The system cost approximately $300, compared to commercial systems costing $20,000 or more.

Slicer preview of Stringimeter components arranged on a 3D printer build plate
3D-print preparation for the Stringimeter components

Component assembly

Assembled 3D-printed components, resin-molded pieces, and commercially available parts into an affordable alternative for laboratory testing.

Controls and integration

Integrated Arduino-based controls with mechanical and electronic components.

Research collaboration

Collaborated with Colby Lab Group to support experimental testing.
Carbon fiber chassis panel fitted inside the Nittany Motorsports vehicle
Nittany Motorsports · Composite Structures

Carbon Fiber Chassis Fabrication

Contributed to chassis research and development for Penn State's Nittany Motorsports team, focusing on the design and fabrication of lightweight composite components to construct the monocoque.

Carbon fiber composite specimen positioned in a mechanical test fixture
Composite component test setup
Nittany Motorsports team members applying carbon fiber to a chassis panel
Carbon fiber layup during fabrication

Design and fabrication

Used carbon fiber layup techniques to manufacture chassis plates, applying materials and structural engineering principles to support strength, stiffness, and weight optimization.

Team development

Worked with the chassis subteam to develop and test components for the team's Formula SAE vehicle.
Motor-driven test rig with electric motor and instrumentation

Photo: Southwest Research Institute

Personal project · Sensors · Python

Motor Predictive Maintenance

Development of a predictive maintenance monitoring model using machine learning to detect faulty operation of 12 V brushed DC motors.

Project Objective

Study vibration data and motor behavior across different operating scenarios to establish a baseline and identify operating anomalies that may indicate faults.

Approach to the Problem

Use mechanical engineering principles, sensor technology, signal processing, and machine learning in Python to construct predictive models.

Software and Technology

Python, NumPy, pandas, SciPy, scikit-learn, Matplotlib, accelerometer sensors, and a 12 V brushed DC motor.

Activities

EMS Soccer

Team captain, developing leadership and teamwork skills.

ASME

Member of the American Society of Mechanical Engineers.

Hiking & Nature

Exploring trails and enjoying the outdoors.

Recreational Volleyball

Playing for fun, competition, and teamwork.

Competitive Gaming

Top 200 in Brawlhalla, North America East.

Social Activities

Spending time with friends and making new ones!

Resume

View resume ↗

Contact Me

rylandz899@gmail.com LinkedIn ↗