Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian Optimization Algorithm.

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.

Thermal and Electrical Properties of Additively Manufactured Polymer-Boron Nitride Composite.

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.

Hardening of Additive Manufactured 316L Stainless Steel by Using Bimodal Powder Containing Nanoscale Fraction.

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.