Investigator: Yang Fang
Collaborator: Dr. Reza Ghodssi (University of Maryland)
Select publications:
- Y. Fang, X. Tan, "A Dynamic JKR Model with Application to Vibrational Release in Micromanipulation", IROS'06
- X. Tan, A. Modafe, R. Ghodssi, "Measurement and Modeling of Dynamic Rolling Friction in Linear Microball Bearings," Journal of Dynamic Systems, Measurement, and Control (2006)
In the past two decades rapid progresses have been made in MEMS fabrication. Modeling and control techniques are essential for designing microsystems with increasing functionality and complexity, and for their long-term, high-performance reliable operation. Macroscopic models become inadequate when sizes shrink down to the order of microns (or even nanometers), a well-known example of which is that adhesive and frictional forces become dominant in comparison to gravity forces.
Adhesion is one of the most challenging problems in micromanipulation. We pursue effective design and control methods to address this problem. One project involves the study of the optimal strategy for acceleration-based release of a micro object from an active manipulator, taking into account the damping effect in the contact area.
In collaboration with Prof. Reza
Ghodssi and his MEMS research group
at University of Maryland, we are also investigating the tribological behavior of
linear microball-bearings using a non-intrusive, vision-based approach. Such bearings
can potentially provide robust, low-friction support in micromachines such as
micromotors and microgenerators. However, interaction of bearing elements at
the microscale leads to various interesting
phenomena that require physical understanding and proper modeling in order
to design and control the micromachines. The experimental setup to the
left is located in the MEMS Sensors and
Actuators Laboratory. We have successfully measured the steady-state
rolling friction behavior based on snapshots of bearing elements when the
bearing is set to oscillation, and developed both empirical and physical
models to capture the observed phenomenon. The developed rolling friction
model will be used to develop algorithms for controlling a microball-bearing-supported
linear micromotor in micropositioning applications.
Click the pictures below for video clips of the bearing in motion. Left: macro-view; Right: micro-view (showing motion of all bearing elements including stator, slider, and micro balls).