Additive manufacturing enables the fabrication of lattice structures which are of particular interest to fabricate medical implants and lightweight aerospace parts. Product integrity is critical in these applications. This requests very challenging quality control for such complex geometries, particularly on detecting internal defects. It is important not only to detect whether there are missing struts for a product with a large size of lattices, but also to identify the number of missing struts for safety-critical applications. Resonant ultrasound spectroscopy is a promising method for fast and cost-effective non-destructive testing of complex geometries but data analytics methods are needed to systematically analyze resonant ultrasound signals for defect identification and classification. This study utilizes resonant acoustic method to obtain resonant frequency spectrum of test lattice structures. In addition, regularized linear discriminant analysis, combined with adaptive sampling and normalization, is developed to classify the number of missing struts. The result shows 80.95% testing accuracy on validation study, which suggests that the resonant acoustic method combined with machine learning is a powerful tool to inspect lattices.

增材制造使格状结构的制造成为可能，这对于制造医疗植入物和轻型航空航天零件特别重要。产品完整性在这些应用中至关重要。对于这种复杂的几何形状，特别是在检测内部缺陷方面，这要求非常有挑战性的质量控制。重要的是，不仅要检测具有大尺寸格子的产品是否缺少支杆，而且还必须确定安全关键型应用中支杆的数量。共振超声光谱法是一种快速且经济高效的复杂几何形状无损检测的有前途的方法，但是需要数据分析方法来系统地分析共振超声信号以进行缺陷识别和分类。本研究利用共振声学方法获得测试晶格结构的共振频谱。此外，开发了正则化线性判别分析，并结合了自适应采样和归一化，以对缺失的支撑数量进行分类。结果表明，在验证性研究中，测试精度为80.95％，这表明共振声学方法与机器学习相结合是检查晶格的有力工具。