Additive manufacturing is a modern technique to produce parts with a complex geometry. However, the choice of the printing parameters is a time-consuming and costly process. In this study, the parameter optimization for the laser powder bed fusion process was investigated. Using state-of-the art multi-objective Bayesian optimization, the set of the most-promising process parameters (laser power, scanning speed, hatch distance, etc.), which would yield parts with the desired hardness and porosity, was established. The Gaussian process surrogate model was built on 57 empirical data points, and through efficient sampling in the design space, we were able to obtain three points in the Pareto front in just over six iterations. The produced parts had a hardness ranging from 224–235 HV and a porosity in the range of 0.2–0.37%. The trained model recommended using the following parameters for high-quality parts: 58 W, 257 mm/s, 45 µm, with a scan rotation angle of 131 degrees. The proposed methodology greatly reduces the number of experiments, thus saving time and resources. The candidate process parameters prescribed by the model were experimentally validated and tested.

The particle size distribution significantly affects the material properties of the additively manufactured parts. In this work, the influence of bimodal powder containing nano- and micro-scale particles on microstructure and materials properties is studied. Moreover, to study the effect of the protective atmosphere, the test samples were additively manufactured from 316L stainless steel powder in argon and nitrogen. The samples fabricated from the bimodal powder demonstrate a finer subgrain structure, regardless of protective atmospheres and an increase in the Vickers microhardness, which is in accordance with the Hall-Petch relation. The porosity analysis revealed the deterioration in the quality of as-built parts due to the poor powder flowability. The surface roughness of fabricated samples was the same regardless of the powder feedstock materials used and protective atmospheres. The results suggest that the improvement of mechanical properties is achieved by adding a nano-dispersed fraction, which dramatically increases the total surface area, thereby contributing to the nitrogen absorption by the material.

The efficiency of electronic microchip-based devices increases with advancements in technology, while their size decreases. This miniaturization leads to significant overheating of various electronic components, such as power transistors, processors, and power diodes, leading to a reduction in their lifespan and reliability. To address this issue, researchers are exploring the use of materials that offer efficient heat dissipation. One promising material is a polymer–boron nitride composite. This paper focuses on 3D printing using digital light processing of a model of a composite radiator with different boron nitride fillings. The measured absolute values of the thermal conductivity of such a composite in the temperature range of 3–300 K strongly depend on the concentration of boron nitride. Filling the photopolymer with boron nitride leads to a change in the behavior of the volt–current curves, which may be associated with the occurrence of percolation currents during the deposition of boron nitride. The ab initio calculations show the behavior and spatial orientation of BN flakes under the influence of an external electric field at the atomic level. These results demonstrate the potential use of photopolymer-based composite materials filled with boron nitride, which are manufactured using additive techniques, in modern electronics.

High residual stresses (RS) in laser powder bed fusion (PBF-LB) components present significant structural challenges, yet traditional characterization techniques are cost- and time-intensive. This study introduces three rapid mechanical testing methodologies to estimate longitudinal RS in PBF-LB 316L stainless steel by using the premature micro-yielding of pre-tensioned near-surface regions in cylindrical geometries. The proposed methods evaluate RS through: (I) plastic work density calculated from cyclic loading-unloading, (II) virtual unloading mathematically projected from standard monotonic tensile data, and (III) the detection of a macroscopic micro-yielding threshold (0.01% strain). Estimations were quantitatively validated against neutron diffraction (ND) measurements across multiple heat treatment conditions (as-built to 1000 °C). The energy-based approaches (Proposed Methods I and II) demonstrated linear proportionality with relative ND stress reductions (R2= 0.82 and 0.77, respectively). Furthermore, by subtracting the empirical micro-yielding threshold from condition-specific macroscopic yield strengths, Proposed Method III tracked absolute RS relaxation. Electron backscatter diffraction (EBSD) analysis physically validated these techniques, confirming that the initial non-linear macroscopic mechanical response is governed by localized geometrically necessary dislocation (GND) accumulation prior to bulk yielding. These findings establish the proposed mechanical methods as highly accessible alternatives for evaluating stress mitigation strategies in additive manufacturing.

High residual stresses (RS) in laser powder bed fusion (PBF-LB) components present significant structural challenges, yet traditional characterization techniques are cost- and time-intensive. This study introduces three rapid mechanical testing methodologies to estimate longitudinal RS in PBF-LB 316 L stainless steel by using the premature micro-yielding of pre-tensioned near-surface regions in cylindrical geometries. The proposed methods evaluate RS through: (I) plastic work density calculated from cyclic loading-unloading, (II) virtual unloading mathematically projected from standard monotonic tensile data, which also allows the analysis of existing tensile test results, and (III) the detection of a macroscopic micro-yielding threshold (0.01% strain). The estimates were benchmarked against neutron diffraction (ND) measurements across multiple heat-treatment conditions (as-built to 1000 °C). The energy-based approaches (Proposed Methods I and II) demonstrated linear correlations with relative ND stress reductions (R ² = 0.75 and 0.71, respectively). Furthermore, by subtracting the empirical micro-yielding threshold from condition-specific macroscopic yield strengths, Proposed Method III captured the relative RS relaxation trend. Electron backscatter diffraction (EBSD) analysis supported the proposed mechanistic interpretation by showing localized geometrically necessary dislocation (GND) accumulation prior to bulk yielding. These findings show that the proposed mechanical methods can serve as accessible tools for evaluating stress mitigation strategies in additive manufacturing.