Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the forming quality in L-PBF because it leads to metallurgical defects and the degradation of mechanical properties. This impact becomes more severe in the fabrication of large-sized parts during the multi-laser L-PBF process. Therefore, investigations of spatter generation and countermeasures have become more urgent. Although much research has provided insights into the melt pool, microstructure, and mechanical property, reviews of spatter in L-PBF are still limited. This work reviews the literature on the in situ detection, generation, effects, and countermeasures of spatter in L-PBF. It is expected to pave the way towards a novel generation of highly efficient and intelligent L-PBF systems.

Size-controlled carbon dots (CDs) were prepared by non-focusing pulsed laser irradiation in toluene and the corresponding mechanism was investigated.

The self-absorption effect seriously affects the accuracy of determination in laser-induced breakdown spectroscopy (LIBS). In this work, we proposed to reduce multielemental self-absorption within a wide spectral range (200-900 nm) by using microwave-assisted excitation in LIBS (MAE-LIBS). Self-absorption reduction of sodium (Na), potassium (K), aluminum (Al), silicon (Si), and calcium (Ca) in potassium feldspar using MAE-LIBS was investigated. The mechanisms of self-absorption reduction in MAE-LIBS were also investigated. The results show that the serious self-absorption of spectral lines (Na and K) was reduced. The full widths at half maximum (FWHMs) of Na I 589.0 nm, Na I 589.6 nm, K I 766.5 nm, and K I 769.9 nm in potassium feldspar were reduced by 43%, 43%, 53%, and 47%, respectively. MAE-LIBS also has a little FWHM reduction for spectral lines with weak self-absorption. The results demonstrate that MAE-LIBS can simultaneously reduce multielemental self-absorption.

Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection. However, due to the spatially inhomogeneity and drastically temporal varying nature of its emission source, the laser-induced plasma, it is difficult to find or hard to generate an appropriate spatiotemporal window for high repeatable signal collection with lower matrix effects. The quantification results of traditional physical principle based calibration model are unsatisfactory since these models were not able to compensate for complicate matrix effects as well as signal fluctuation. Machine learning is an emerging approach, which can intelligently correlated the complex LIBS spectral data with its qualitative or/and quantitative composition by establishing multivariate regression models with greater potential to reduce the impacts of signal fluctuation and matrix effects, therefore achieving relatively better qualitative and quantitative performance. In this review, the progress of machine learning application in LIBS is summarized from two main aspects: i) Pre-processing data for machine learning model, including spectral selection, variable reconstruction, and denoising to improve qualitative/quantitative performance; ii) Machine learning methods for better quantification performance with reduction of the impact of matrix effect as well as LIBS spectra fluctuations. The review also points out the issues that researchers need to address in their future research on improving the performance of LIBS analysis using machine learning algorithms, such as restrictions on training data, the disconnect between physical principles and algorithms, the low generalization ability and massive data processing ability of the model.