Uncovering acoustic signatures of pore formation in laser powder bed fusion

Journal Article · 27 December 2023 · International Journal of Advanced Manufacturing Technology

DOI: https://doi.org/10.1007/s00170-023-12771-6 · OSTI ID:2263522

Tempelman, Joshua R.; Mudunuru, Maruti K.; Karra, Satish; Wachtor, Adam J.; Ahmmed, Bulbul; Flynn, Eric B.; Forien, Jean-Baptiste; Guss, Gabe M.; Calta, Nicholas P.; DePond, Phillip J.; Matthews, Manyalibo J.

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$$$\varvec{k}$$$$ $k$ -means clustering (NMF $$$$\varvec{k}$$$$ $k$ ) to learn the underlying spectral patterns associated with pore formation. NMF $$$$\varvec{k}$$$$ $k$ returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$$$\varvec{k}$$$$ $k$ decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

• Sponsoring Organization: USDOE
• OSTI ID: 2263522
• Journal Information: International Journal of Advanced Manufacturing Technology, Journal Name: International Journal of Advanced Manufacturing Technology Journal Issue: 5-6 Vol. 130; ISSN 0268-3768
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: United Kingdom
• Language: English