Abstract Performing non-destructive testing on metallic components with very complex geometries, such as turbine blades, is very challenging. To inspect such components, powerful and robust non-destructive inspection protocols must be defined. Process Compensated Resonance Testing (PCRT) is a relatively novel approach that records a broadband vibrational fingerprint for each component and employs vibrational features such as resonant frequency, quality factor, and amplitude. These features are used in the Mahalanobis Taguchi System to classify the parts in terms of their quality, i.e. Good/Bad. In the present study, a two-stage MCS classification approach, coupled with Binary Particle Swarm Optimization, is proposed to optimize the process of selecting the most significant features and to search for the optimal decision boundary to discriminate healthy and unhealthy components. Further, the proposed MCS enables the features to be mapped into a higher dimensional Mahalanobis Distance space, thereby enhancing the performance of classification. An experimental case study on equiaxed Nickel alloy first-stage turbine blades, with very complex geometry and various damages, demonstrates the high classification accuracy and robustness of the developed MCS approach.

中文翻译：

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Mahalanobis 分类系统 (MCS) 与二元粒子群优化集成，用于复杂金属涡轮叶片的稳健质量分类

摘要 对具有非常复杂几何形状的金属部件（例如涡轮叶片）进行无损检测非常具有挑战性。要检查此类组件，必须定义强大且稳健的无损检查协议。过程补偿共振测试 (PCRT) 是一种相对新颖的方法，它记录每个组件的宽带振动指纹，并采用共振频率、品质因数和振幅等振动特征。Mahalanobis Taguchi 系统使用这些特征来根据质量对零件进行分类，即好/坏。在本研究中，两阶段 MCS 分类方法，加上二元粒子群优化，建议优化选择最重要特征的过程并搜索最佳决策边界以区分健康和不健康的组件。此外，所提出的 MCS 能够将特征映射到更高维的马氏距离空间，从而提高分类性能。等轴镍合金一级涡轮叶片的实验案例研究，具有非常复杂的几何形状和各种损坏，证明了所开发的 MCS 方法的高分类精度和鲁棒性。