Prompt uncertainty estimation with GUM framework for on-machine tool coordinate metrology

Abstract

This paper validates the uncertainty evaluated following the Guide to the Expression of Uncertainty in Measurement (GUM) for on-machine probing with a five-axis machine tool. A partly synthetic input covariance matrix is assembled for Monte Carlo and GUM frameworks, which separately estimate the uncertainty of on-machine probed point sets and obtained geometric features. The differences between the GUM and Monte Carlo results lie within the stipulated tolerances with comparable coverage regions and marginal distributions. This validates the GUM framework, which is on average 24 and 249 times faster for on-machine measurement of a gauge block and a precision sphere, respectively.