摘要：Abstract. Virtual assembly (VA) is a method for datum definition and quality prediction of assemblies considering local form deviations of relevant geometries. Point clouds of measured objects are registered in order torecreate the objects’ hypothetical physical assembly state. By VA, the geometrical verification becomes moreaccurate and, thus, increasingly function oriented. The VA algorithm is a nonlinear, constrained derivate of theGaussian best fit algorithm, where outlier points strongly influence the registration result. In order to assessthe robustness of the developed algorithm, the propagation of measurement uncertainties through the nonlineartransformation due to VA is studied. The work compares selected propagation methods distinguished from theirlevels of abstraction. The results reveal larger propagated uncertainties by VA compared to the unconstrainedGaussian best fit.
