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.
Highlights
Additive manufacturing (AM) enables production of parts with complex geometries such as lattice structures [1]
We propose to investigate and demonstrate the capability of Resonant ultrasound spectroscopy (RUS) methods, named resonant acoustic method (RAM) [13], and combine it with machine learning for the detection of missing struts in metallic lattice structures produced by AM
To try to find an explanation why the parts with 6/10 outer missing struts could not be separated from other lattices with a different number of missing struts, we identified their location on the AM platform (Fig. 11)
Summary
Additive manufacturing (AM) enables production of parts with complex geometries such as lattice structures [1]. Reliable and reproducible volumetric non-destructive testing (NDT) methods are essential to ensure the internal integrity of lattice structures, especially for safety-critical applications. Applying RUS methods to lattice structures requires new research to evaluate its capability to distinguish different types of internal defects, as opposed to simple pass/fail decision. We propose to investigate and demonstrate the capability of RUS methods, named resonant acoustic method (RAM) [13], and combine it with machine learning for the detection of missing struts in metallic lattice structures produced by AM.
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