  In engineering applications almost all processes are described with the help
of models. Especially forming machines heavily rely on mathematical models for
control and condition monitoring. Inaccuracies during the modeling,
manufacturing and assembly of these machines induce model uncertainty which
impairs the controller's performance. In this paper we propose an approach to
identify model uncertainty using parameter identification, optimal design of
experiments and hypothesis testing. The experimental setup is characterized by
optimal sensor positions such that specific model parameters can be determined
with minimal variance. This allows for the computation of confidence regions in
which the real parameters or the parameter estimates from different test sets
have to lie. We claim that inconsistencies in the estimated parameter values,
considering their approximated confidence ellipsoids as well, cannot be
explained by data uncertainty but are indicators of model uncertainty. The
proposed method is demonstrated using a component of the 3D Servo Press, a
multi-technology forming machine that combines spindles with eccentric servo
drives.
