In this paper, a numerical approach to analyze tires based on a multi-objective optimization with special consideration of uncertainties is presented. Within the optimization, which uses evolutionary algorithms, the evaluation of a 3D Finite Element tire model in the steady state rolling situation is performed. In order to obtain a reliable and high quality design, data uncertainty caused e.g. by variation of production conditions of tire components as well as incomplete information concerning loading have to be considered. Modeling epistemic uncertainty, which results from fragmentary or dubious information requires the application of the uncertainty model fuzziness.

Among several design goals, this study is looking at durability as an example. An improvement is achieved by the consideration of two objective functions: one is focusing on the reduction of wear and the other on providing a resistance to fatigue. The wear performance is strongly influenced by the distribution of contact pressure in the contact zone. Therefore, a criterion for obtaining optimal contact pressure ratios in the tire footprint is formulated. Within the second objective function, the occurrence of a fatigue crack is investigated by the evaluation of, for example, the strain energy density as a simple criterion. Additionally, in the proposed optimization concept the robustness measure is implicitly included. The robustness measure is defined as a ratio of the variation of uncertain simulation input parameters versus the variation of uncertain structural responses. Thereby, a high variation of uncertain input parameters leading to a minor variation of uncertain structural responses indicates a high robustness of the tire design. In order to improve the numerical efficiency of the proposed design approach, a response surface approximation based on artificial neural networks is applied.