Understanding the influence of process parameters and defect structure on the properties of parts produced via laser powder bed fusion (L-PBF) is a fundamental step towards the broader use of additive manufacturing technologies in critical applications. Furthermore, the ability to predict mechanical properties by simply considering information on the process parameters and defects observed via X-ray computer tomography (XCT) allows one to avoid expensive destructive testing, provide an in-depth understanding of the process quality and represents a viable solution towards process optimisation. Most of the previous works showed that energy density could be used as an excellent synthetic indicator to predict the mechanical properties of parts produced by L-PBF. This paper explores the effect of different energy density levels on the tensile properties of 316L stainless steel parts produced by L-PBF. Different from previous works in the literature, the same level of energy density is obtained considering various combinations of process parameters (speed, power and hatch distance). While energy density is shown to be a good synthetic indicator for predicting ultimate tensile strength (UTS) and yield strength (YS), a different behaviour is observed for elongation. Elongation shows a significant variability even when samples are produced at the same level of energy density, which contrasts with results obtained for UTS and YS. Synthetic indices representing the porosity structure are shown to be quite significant for predicting elongation even when the optimal energy density is considered. By combining process parameters with porosity structure, we show that almost a full prediction of the tensile properties can be achieved, paving the way for a significant reduction in expensive destructive tests.

In this paper the influence of process parameters and fluence on mechanical properties of maraging steel in Selective Laser Melting (SLM) is studied. The results of the present work show that there exists a steady region of process parameters where fluence conveys all the information required to describe and predict density and tensile properties. We also show that in this region of the parameters, the choice of process parameters can be carried out considering other optimality criteria such as productivity, rather than maximization of density. To pursue this objective, a V-Alphabetical optimal design with fixed fluence levels was specifically designed for the experimentation. The used fluence levels and the corresponding process parameter combinations were tested on an industrial SLM system. To compare the informative content of fluence and the process parameters, two regression equation were estimated from experimental results for part density and tensile properties. The comparison of the regression models showed that the predictive ability of fluence and process parameters in the steady region is the same, however fluence allowed to obtain a higher precision. In conclusion, based on the experimental conditions studied, new process parameters are suggested using a productivity-based criteria.

Metal powders for the laser powder bed fusion process are usually produced via gas atomization. However, due to the tight particle size distribution required for this application, the yield of the atomization process is low, resulting in a high-powder cost. In this work, atomization process parameters were varied to increase the gas-to-metal ratio to reduce the particle size distribution produced, and therefore increase the yield of the process. As a result, eight powders were produced starting from scrap AISI 136L material at different gas-to-metal ratio values, and the atomization process yield was successfully increased by 50%. First, the eight powders were characterized in terms of powder size, shape distributions, and flowability. Later, all powders were used to produce tensile specimens. The powders produced at higher yield exhibited a larger number of fine particles but slightly lower circularity, particularly in the coarse fraction. Furthermore, powders produced at a high gas-to-metal ratio demonstrated enhanced flowing properties and higher packing density. Consequently, these powders exhibited superior tensile performance, with ultimate tensile strength (UTS) ranging from 651 to 673 MPa and elongation values between 63 and 66%.

L-PBF is an additive manufacturing process forming parts with complex geometries by adding material layer by layer. The selection of the process parameters in L-PBF has a significant impact on the mechanical properties of the printed parts. Scan speed, laser power, and hatch distance are among the most influential process parameters in L-PBF because, depending on their combination, different solidification mechanisms take place. However, the procedure for selecting these parameters can be expensive from an experimental point of view. Therefore, it is necessary to identify simplified models that allow fast and reliable optimization of the parameters in L-PBF. Furthermore, the choice of parameters cannot be based exclusively on qualitative aspects but must also consider the productivity of the process to obtain a satisfactory compromise. Increasing productivity leads to the formation of lack of fusion porosity which should be avoided. This paper proposes a procedure for selecting parameters based on a semi-analytical thermal model, which, together with a geometric-based defect model, allows identifying an optimality region where good solidification and productivity are considered. The optimization is carried using a properly defined utility function. The procedure is validated through the production of AISI 316L specimens using an industrial L-PBF system.