Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press

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

Optimal Placement of Active Bars for Buckling Control in Truss Structures under Bar Failures

Buckling of slender bars subject to axial compressive loads represents a critical design constraint for light-weight truss structures. Active buckling control by actuators provides a possibility to increase the maximum bearable axial load of individual bars and, thus, to stabilize the truss structure.For reasons of cost, it is in general not economically viable to use such actuators in each bar of the truss structure. Hence, it is an important practical question where to place these active bars. Optimized structures, especially when coupled with active elements to further decrease the number of necessary bars, however, lead to designs, which, while cost-efficient, are especially prone to bardamages, caused, e.g., by material failures. Therefore, this paper presents a mathematical optimization approach to optimally place active bars for buckling control in a way that secures both buckling and general stability constraints even after failure of any combination of a certain number of bars. This allows us to increase the resilience of the system and guarantee stable behavior even in case of failures

Validation du modèle de ruissellement intense (IRIP) - Mémoire M2 du parcours Hydroressources de l'Université de Grenoble Alpes

Maste

4.1.4 : Améliorations de la méthode IRIP au niveau national

Connaissance et prévention des risques naturels et hydrauliques Ces travaux bénéficient du soutien du Ministère chargé de l'environnement, de l'énergie et de la mer par décision de subvention n°21367400 DECEMBRE 201

CARTO ALEA (Zones inondables) 4.1.7. : Régionalisation des paramètres IRIP

Connaissance et prévention des risques naturels et hydrauliques Ces travaux bénéficient du soutien du Ministère chargé de l'environnement, de l'énergie et de la mer par décision de subvention n°2136740