Although mass production parts look the same, every manufactured part is unique, at least on a closer inspection. The reason for this is that every manufactured part is inevitable subjected to different scattering influencing factors and variation in the manufacturing process, such as varying temperatures or tool wear. All these factors inevitably lead to parts, which deviate from their ideal shape. Products, which are built from these deviation-afflicted parts consequently show deviations from their ideal properties. To ensure that every single product nevertheless meets its technical requirements, it is necessary to specify the permitted deviations. Furthermore it is necessary to estimate the consequences of the permitted deviations, which is done via tolerance analysis. During this process the imperfect parts are assembled virtually and the effects of the geometric deviations can be calculated during a variation simulation.

Since the tolerance analysis is to enable engineers to identify weak points in an early design stage it is important to know which contribution every single tolerance has on a certain quality-relevant characteristic, to restrict or increase the correct tolerances. In this paper two different approaches are shown and compared to represent the statistical behavior and the strongly connected sensitivity analyses. In particular a newly developed approach, which is based on fuzzy arithmetic, is compared to the established EFAST-method. The exemplary application of both methods and the comparison of the results are illustrated on a case study.

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