Early Engineering Project
Semi-Active Vibration Control of an MRE Sandwich Beam
A hands-on controls and experimental systems project that combined mechanical fabrication, sensing, data acquisition, system identification, and active controller implementation for vibration attenuation.
Why This Project
The project studied how a magnetorheological elastomer core could be used to reduce vibration in a sandwich beam. Because the stiffness and damping behavior of the material changes under a magnetic field, the system offered a practical way to explore semi-active vibration control rather than relying only on passive structural design.
Experimental System
The work centered on a fabricated sandwich beam test setup with an MRE core, magnetic-field actuation, vibration excitation, response measurement, and data acquisition. The experimental workflow included free-response, forced-response, and impact-hammer studies so the beam behavior could be characterized under different magnetic-field conditions.
The project connected physical fabrication, measured system behavior, and controller implementation in one complete experimental workflow.
Fabrication, Sensing, and Control
I led the team and took charge of the fabrication, sensing, instrumentation, and LabVIEW coding. That meant turning the physical test concept into a working experimental platform, integrating response measurements, and building the control/data-acquisition workflow needed to evaluate the beam under repeatable conditions.
Modeling and Validation
Because the viscoelastic behavior of the MRE core made purely analytical modeling difficult, the project used experimental system identification to estimate transfer-function models under different magnetic fields. The identified models were validated in MATLAB/Simulink and used to support controller design.
What It Demonstrated
The project demonstrated that the beam response could be reduced by changing the magnetic field and that semi-active control could improve vibration attenuation near resonance. The implemented on-off control logic achieved a substantial reduction in acceleration amplitude, showing how sensing, material response, and control logic could be brought together in a lab-scale smart-structure system.
Why It Still Matters
This project was an early foundation for the way I now approach research software and scientific AI systems: build the experimental or computational workflow, connect sensing and models, validate behavior, and translate technical outputs into decisions that can be acted on.