Quantification of data and production uncertainties for tire design parameters in the frame of robustness evaluation

Abstract

The design process of products and structures such as tires constitutes a multi-objective optimization to enhance the performance for a specific usage. However, the specified geometry and material parameters as well as the boundary conditions, which are basis of the performance prediction resulting from a numerical simulation, cannot be guaranteed to coincide with the real parameters of a final product in use. Most parameters are subjected to variation in production, use and service, which can lead to significant variation in performance.The objective of this contribution is to introduce a scheme of the procedure for design optimization with consideration of uncertainty, including uncertainty modeling, uncertainty analysis and robustness evaluation. Thus, the choice of the design parameters will not be solely based on the predicted value, but also on the reliability of the performance. The focus lies on a detailed description of modeling uncertainties of tire design parameters with regard to production variation as well as to non-precise data, to enable uncertainty analysis and evaluation for tire performances.For choosing appropriate uncertainty models, a distinction is made for the two types aleatoric and epistemic uncertainty with respect to origin as well as consequence. Probabilistic, possibilistic and polymorphic uncertainty modeling approaches are selected for several geometry and material parameters, based on the situation of available data as well as their sensitivity. The utilized approaches include random variables, fuzzy variables and probability-boxes (p-boxes).