SUMMARY

This research will investigate the process modeling and control of microgrinding with micro-machine tools and flexible workpieces. A microgrinding force model will be constructed to characterize the probability nature of the grinding force frequency in wheels by creating an analytical stochastic model. Numerical simulation will be used to show model accuracy. The ability to use this stochastic model to determine process operating parameters a priori to allow for stable, chatter-free grinding on highly compliant micro-machine tools and varyingly compliant high-aspect ratio workpieces will be investigated. This model will also be used to study the impact of microgrinding scale effects associated with the limited number of grits on a single wheel on the process. A machine vision system will be used to measure wheel surface topography online in order to overcome challenges associated with this scale effect. A process controller will be designed to allow for in situ parameter optimization and system compliance compensation with the goal of improving final ground part dimensional accuracy. The technique will limit added machine costs, increased machine footprint, and increased process setup time. This will be achieved using online model modification based on in situ wheel topography measurement and inexpensive dynamic sensors.