@misc{UbbenBestleHaug, author = {Ubben, Paul Tobe and Bestle, Dieter and Haug, J{\"u}rgen}, title = {Robust Automotive Suspension Design Using Adaptive Response Surface Based Multi-objektive Optimization}, series = {RDO-Journal}, journal = {RDO-Journal}, number = {2}, pages = {10 -- 15}, language = {en} } @inproceedings{UbbenBestleHaug, author = {Ubben, Paul Tobe and Bestle, Dieter and Haug, J{\"u}rgen}, title = {Robust Automotive Suspension Design Using Adaptive Response Surface Based Multi-objektive Optimization}, series = {11. Weimarer Optimierungs- und Stochastiktage - 06. - 07. November 2014}, booktitle = {11. Weimarer Optimierungs- und Stochastiktage - 06. - 07. November 2014}, publisher = {DYNARDO GmbH}, address = {Weimar}, pages = {19}, abstract = {Deterministic optimization is used in all fields of engineering, especially in early design processes based on digital prototypes and simulation. A major disadvantage of deterministic optimization is the unknown robustness of the found solution against uncertainties of system parameters. Therefore, the Robust Design Optimization (RDO) concept as combination of Robustness Analysis (RA) and deterministic optimization was developed. In this paper, such an approach is applied to the optimization of a suspension for passenger cars w.r.t. typical driving maneuvers. The suspension behavior has to be robust against uncertainties without defining strict limits or safety margins. The coupled multi-objective RDO procedure will find a Pareto-front w.r.t. mean value and variance of chosen objectives. As a result, a specific compromise regarding system robustness and mean performance may be chosen from this Pareto-set. To overcome the vast amount of CPU-time, required for expensive direct function evaluations, an adaptive response surface method (aRSM) is integrated. The overall process then consists of an inner loop involving a multi-objective evolutionary algorithm based on response surfaces and an outer loop, where metamodeling is performed on a set of support points. This set is initialized in the first iteration step and updated afterwards by picking promising designs from the Pareto-fronts of the surrogate model, which are then evaluated exactly.}, language = {en} }