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Laser powder bed fusion of metals (PBF-LB/M) is a process widely used in additive manufacturing (AM). It is highly sensitive to its process parameters directly determining the quality of the components. Hence, optimal parameters are needed to ensure the highest part quality. However, current approaches such as experimental investigation and the numerical simulation of the process are time-consuming and costly, requiring more efficient ways for parameter optimization. In this work, the use of machine learning (ML) for parameter search is investigated based on the influence of laser power and speed on simulated melt pool dimensions and experimentally determined part density. In total, four machine learning algorithms are considered. The models are trained to predict the melt pool size and part density based on the process parameters. The accuracy is evaluated based on the deviation of the prediction from the actual value. The models are implemented in python using the scikit-learn library. The results show that ML models provide generalized predictions with small errors for both the melt pool dimensions and the part density, demonstrating the potential of ML in AM. The main limitation is data collection, which is still done experimentally or simulatively. However, the results show that ML provides an opportunity for more efficient parameter optimization in PBF-LB/M.

With increasing maturity of the laser powder bed fusion (PBF-LB/M) process, the related products are becoming more complex. The more conventional parts are integrated into one design, the more requirements regarding local material properties arise. This concerns for instance products with high demands regarding temperature management. Here, different thermal conductivities within the part enable the control of the temperature distribution as well as the direction of heat flows. The realization of those local properties poses a challenge, though, as the use of multiple materials in PBF-LB/M is not broadly available. However, the different states of material in PBF-LB/M, i.e. bulk and powder material, provide the opportunity to create thermal metamaterials with locally varied thermal conductivities. To enable part design utilizing the bulk material as well as enclosed powder, this study investigates the respective thermal conductivities of Ti-6Al-4V. Powder and printed samples were measured at RT by the Modified Transient Plane Source method, resulting in an effective thermal conductivity of 0.13 W/mK for powder and 5.4 W/mK for bulk material (compared to 6.5 W/mK in prior experiments). For complete assessment of the powder material, because of the many uncertainties due to the particle size distribution and powder application, a computational model following the network modeling approach is created. The model is used to create a data set of 60 different powder bed configurations, which is then statistically evaluated to provide a description independent from powder packing. Finally, the application of the investigations to achieve thermal metamaterials capable of local temperature management with a single material is presented in a numerical study. Here, the use cases of thermal shielding as well as the concentration of heat flow is demonstrated.