The laser powder bed fusion (LPBF) or powder-bed additive layer manufacturing process is now recognized as a high-potential manufacturing process for complex metallic parts. However, many technical issues are still to overcome for making LPBF a fully viable manufacturing process. This is the case of surface finish and the systematic occurrence of porosities, which require postmachining steps. Up till now, the porosity origin remains unclear but is expected to be related to the stability of the process. As a LPBF part is made by the accumulation of hundreds of meters of small weld beads, it also appears to be important to understand all the phenomena that occur during the laser-powder-melt pool (MP) interaction for each single track. For this reason, in the first part of our study, using an instrumented LPBF setup and a fast camera analysis (>10 000 image/s), single tracks were fabricated and analyzed in real time and postmortem. Spatters ejections and powder denudation phenomena were observed together with variations of melt pool dimensions and melt-pool instabilities. In turn, the physical origin of this powder denudation and the dynamics of the MP were investigated and discussed.

The presence of defects like gas bubble in fabricated parts is inherent in the selective laser sintering process and the prediction of bubble shrinkage dynamics is crucial. In this paper, two artificial intelligence (AI) models based on Decision Trees algorithm were constructed in order to predict bubble dissolution time, namely the Ensemble Bagged Trees (EDT Bagged) and Ensemble Boosted Trees (EDT Boosted). A metadata including 68644 data were generated with the help of our previously developed numerical tool. The AI models used the initial bubble size, external domain size, diffusion coefficient, surface tension, viscosity, initial concentration, and chamber pressure as input parameters, whereas bubble dissolution time was considered as output variable. Evaluation of the models’ performance was achieved by criteria such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results showed that EDT Bagged outperformed EDT Boosted. Sensitivity analysis was then conducted thanks to the Monte Carlo approach and it was found that three most important inputs for the problem were the diffusion coefficient, initial concentration, and bubble initial size. This study might help in quick prediction of bubble dissolution time to improve the production quality from industry.

The authors propose an analysis of the effect of various operating parameters on the keyhole depth during laser welding. The authors have developed a model that uses the analysis of the thermal field obtained in 2D geometry, which is mainly defined by the characteristic Peclet number. This allows us to show that the dependence of the aspect ratio R of the keyhole with the operating parameters of the process is a function of two parameters: a normalized aspect ratio R0, controlled by the incident laser power and the spot diameter, and a characteristic speed V0 related to the process of heat diffusion. The resulting general law R = f (R0, V/V0) appears to be very well verified by different experimental data and allows to define mean thermophysical parameters of the used materials. These data can then be used for keyhole depths prediction for any subsequent operating parameters of the same material. This model also allows us to define precisely a criterion for a keyhole threshold generation. The authors will apply the derived procedure to successfully analyze experiments on materials with very different thermophysical properties (such as steel alloys and copper), with various focal spots, incident laser powers, and welding speeds.

The Laser Metal Deposition (LMD) process is an additive manufacturing method, which generates 3D structures through the interaction of a laser beam and a gas/powder stream. The stream diameter, surface density and focal plan position affect the size, efficiency and regularity of the deposit tracks. Therefore, a precise knowledge of the gas/powder streams characteristics is essential to control the process and improve its reliability and reproducibly for industrial applications. This paper proposes multiple experimental techniques, such as gas pressure measurement, optical and weighting methods, to analyze the gas and particle velocity, the powder stream diameter, its focal plan position and density. This was carried out for three nozzle designs and multiple gas and powder flow rates conditions. The results reveal that (1) the particle stream follows a Gaussian distribution while the gas velocity field is closer to a top hat one; (2) axial, carrier and shaping gas flow significantly impact the powder stream’s focal plan position; (3) only shaping gas, powder flow rates and nozzle design impact the powder stream diameter. 2D axisymmetric models of the gas and powder streams with RANS turbulent model are then performed on each of the three nozzles and highlight good agreements with experimental results but an over-estimation of the gas velocity by pressure measurements.