A non-destructive resonant acoustic testing and defect classification of additively manufactured lattice structures

Abstract 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.