Gaussian Process Model for Touch Probing

Abstract

In this paper, we present an approach to determine probing points for the CMM (coordinate measurement machine) measurement. A surface uncertainty based approach is developed to maximize the amount of information acquired by the touch probing of the surface deviations caused by machining errors. The surface uncertainty is modeled by the Gaussian process model and the probing points are selected to minimize the maximum surface uncertainty (surface variance) conditioned on the touch probings at the selected points. The algorithm has been tested with various numerical examples and has been applied in real machining scenarios. The surface reconstruction error based on the developed algorithm is 50% smaller than uniform sampling. The experiments of on machine probing has validated that the selected points can adequately capture the machining errors.