Complex thermo-kinetic interactions during metal additive manufacturing reduce the homogeneity of the microstructure of the produced samples. Understanding the effect of processing parameters over the resulting mechanical properties is essential for adopting and popularizing this technology. The present work is focused on the effect of laser power, scanning speed, and hatch spacing on the relative density, microhardness, and microstructure of 316L stainless steel processed by laser powder bed fusion. Several characterization techniques were used to study the microstructure and mechanical properties: optical, electron microscopies, and spectrometry. A full-factorial design of experiments was employed for relative density and microhardness evaluation. The results derived from the experimental work were subjected to statistical analysis, including the use of analysis of variance (ANOVA) to determine both the main effects and the interaction between the processing parameters, as well as to observe the contribution of each factor on the mechanical properties. The results show that the scanning speed is the most statistically significant parameter influencing densification and microhardness. Ensuring the amount of volumetric energy density (125 J/mm³) used to melt the powder bed is paramount; maximum densification (99.7%) is achieved with high laser power and low scanning speed, while hatch spacing is not statistically significant.

Metal additive manufacturing is a disruptive technology that is changing how various alloys are processed. Although this technology has several advantages over conventional manufacturing, it is still necessary to standardize its properties, which are dependent on the relative density (RD). In addition, since experimental designs are costly, one solution is using machine learning algorithms that allow the effects of variations in the processing parameters on the resulting density of the additively manufactured components to be anticipated. This work assembled a database based on data from 673 observations and 10 predictors to forecast the relative density of 316L stainless steel and AlSi10Mg components produced by laser powder bed fusion (L-PBF). LazyPredict was employed to select the algorithm that best models the variability of the inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting and optimization advantages. The predictions’ precision for aluminum and stainless steel obtained an R² value greater than 0.86 and 0.83, respectively. The results of the SHAP values indicated that laser power and energy density are the parameters that have the greatest impact on the predictability of the relative density of Al-Si10-Mg and SS 316L materials processed by L-PBF. This study presents a compendium of data for the additive fabrication of stainless steel and aluminum alloys, offering researchers a guide to understanding how processing parameters influence RD.