A virtual CMM to estimate uncertainties

Coordinates measuring machines (CMMs) have become classical measuring instruments. Nevertheless, the uncertainties associated to measurements results obtained by CMMs, are often a global estimation. This work focuses on the development of a virtual CMM with Python, in order to estimate uncertainties of all measured dimensions, on basic parts. The originality of the approach consists in randomizing only the nine linear parameters of the CMM compensation matrix, which allows calculating the twelve other ones, and avoiding complex covariance calculations. The model takes into account CMM defects, probing uncertainties and some parameters of the part and the environment. Method uncertainty, difficult to be modeled, is treated as a set of recommendations. This model can be used as a pre-processing evaluation of uncertainty, or post-processing of a real measurement.