The key purpose of robust design and tolerancing approaches is the management of uncertainty. Against this background, it is not surprising that there is a large overlap between the basic ideas and concepts in both fields. However, while sharing the same objective, the focus of the corresponding development phases is quite different; that is (i) the determination of solutions that react insensitive, in other words robust, to n oise factors – Robust parametric design; and (ii) the limitation of the effects of manufacturing imprecision by the specification of optimal tolerances – Tolerancing. As a consequence, there also is a significant gap between both concepts. Focusing on the improvement of design solutions, robustness is often related to uncertainty of not known designs or manufacturing processes. Due to the complexity of a largely matured solution, tolerancing tasks are usually based on previously specified, key characteristics or behavior models that are supposed perfect. Therefore, an overview of robust design and tolerancing is used to highlight the deficiencies, and to formalize a new classification of tolerance analysis issues based on the type of uncertainty considered. The proposed framework is based on Dempster-Shafer evidence theory and allows to efficiently perform statistical tolerance analyses under model imprecision.

稳健设计和容差方法的主要目的是管理不确定性。在此背景下，两个领域的基本思想和概念之间存在大量重叠也就不足为奇了。然而，虽然目标相同，但相应的发展阶段的侧重点却大不相同；即（i）确定对噪声因素不敏感的解决方案，换句话说，稳健的解决方案——稳健的参数化设计；(ii) 最佳公差规范对制造不精确性影响的限制 -公差. 因此，这两个概念之间也存在重大差距。专注于设计解决方案的改进，稳健性通常与未知设计或制造过程的不确定性有关。由于很大程度上成熟的解决方案的复杂性，公差任务通常基于先前指定的、被认为是完美的关键特征或行为模型。因此，使用稳健设计和公差的概述来突出缺陷，并根据所考虑的不确定性类型将公差分析问题的新分类形式化。所提出的框架基于 Dempster-Shafer 证据理论，允许在模型不精确的情况下有效地执行统计公差分析。