Every manufacturing procedure is subject to tolerance variations. Over the years, a set of key characteristic features (KCF) that can explain the effect of manufacturing variations on the aero-mechanical performance of a fan blade has been devised and monitored to ensure conformality and good performance. The KCFs are derived from a cloud of coordinate measurement machine (CMM) points and are defined on approved engineering drawings for the manufactured part. In this paper, it is demonstrated that some of the traditional, common wisdom KCFs are not adequate to explain the engine performance deviation behaviour on a test bed at the sea-level condition. On the other hand, good correlation is found by analysing a set of engineering parameters drawn from a new inverse-mapping procedure of the CMM data. It is further demonstrated that a deviation measured via CMM or 3D structured light (GOM) data in cold conditions can be translated to a variation in the hot running shape of the blade. Having identified the key blade features, a cheap alternative to modifying the manufacturing procedure is devised to recover the fan performance by optimising its leading-edge shape.

每个制造过程都会存在公差变化。多年来，我们设计并监控了一组关键特征（KCF），可以解释制造偏差对风扇叶片空气机械性能的影响，以确保保形性和良好的性能。KCF 源自坐标测量机 (CMM) 点云，并在已批准的制造零件工程图纸上定义。本文证明，一些传统的常识 KCF 不足以解释海平面条件下试验台上的发动机性能偏差行为。另一方面，通过分析从 CMM 数据的新逆映射过程中提取的一组工程参数，发现了良好的相关性。进一步证明，在寒冷条件下通过 CMM 或 3D 结构光 (GOM) 数据测量的偏差可以转化为叶片热运行形状的变化。确定了关键叶片特征后，设计了一种修改制造程序的廉价替代方案，通过优化其前缘形状来恢复风扇性能。