Characterization of manufacturing uncertainties with applications to uncertainty quantification and robust design optimization

Abstract

Methodologies to quantify the impact of manufacturing uncertainties in 3D CFD based design strategies have become increasingly available over the past years as well as optimization under uncertainties, aiming at reducing the systems sensitivity to manufacturing uncertainties. This type of non-deterministic simulation depends however strongly on a correct characterization of the manufacturing variability. Experimental data to characterize this variability is not always available or in many cases cannot be sampled in sufficiently high numbers. Principal Component Analysis (PCA) is applied to the sampled geometries and the influence of tolerances classes, sample size and number of retained deformation modes are discussed. It is shown that the geometrical reconstruction accuracy of the deformation modes and reconstruction accuracy of the CFD predictions are not linearly related, which has important implications on the total geometrical variance that needs to be retained. In a second application the characterization of manufacturing uncertainties to a marine propeller is discussed. It is shown that uncertainty quantification and robust design optimization of the marine propeller can successfully be performed on the basis of the derived uncertainties. This leads to a propeller shape that is less sensitive to the manufacturing variability and therefore to a more robust design.