9313834 Wiens The project seeks to use force and position data from avaliable automated deburring devices for burr recognition, and prediction of chamfering depth, tool wear, part alignment, and kinematic and dynamically induced errors. The feasibility of establishing such corrleations for various micromanipulators available for automated deburring will also be investigated. The dependence of such corrlwations on the micromanipulator design will also be determined. The results from the work will provide thenecessary correlation models needed for developing a smart deburring system that is capable of adaptively responding in real time to process and part variations with on line tuning and path planning. The approach should remove or minimize the need for vision feedback, making the system less sensitive to problems such as occlusions and need for prior data processing. Areas that will benefit from the research are finishing of machined surfaces and sand cast surfaces.
