Abstract
Investigators publish new report on Machine Learning. According to news reporting originating in Los Alamos, New Mexico, by NewsRx journalists, research stated, “We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a lowdimensional 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.” Financial support for this research came from Los Alamos National Laboratory. The news reporters obtained a quote from the research from Los Alamos National Laboratory, “Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom k-means clustering (NMFk) to learn the underlying spectral patterns associated with pore formation. NMFk 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 NMFk 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.”
NETL
NSTL
Springer Nature