Adaptive inspection plans in coordinate metrology based on Gaussian Process models

The paper describes a successful technology transfer of Gaussian Process (GP) modelling, also known as kriging, to the field of industrial metrology. Product compliance to geometrical specifications requires an inspection cycle in which some points are sequentially probed on the part surface. Although the inspection plan can be naturally regarded as a statistical experiment, industrial practice generally relies on a deterministic logic both to choose the sample and to compute the geometric error. Opposed to this, in this work the inspection plan is built as an adaptive experiment where the next probing location is selected by criteria based on predictions obtained from a GP model estimated at each step of the procedure. Results show that the good predictive capability of GP models assures an improvement over the current state of the art both in terms of quality of the estimated error and cost of the inspection.