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).