The mechanical properties in laser-based powder bed fusion (PBF-LB/M) manufactured parts are anisotropic in nature due to the layer-wise build-up but also change due to different solidification conditions in dependence on the process strategy and the geometry. In this work, the latter effect is examined by means of simulating the thermal history on a part scale (macro temperature) and correlating the critical temperature and holding time with the local hardness and microstructure. A macro temperature model is introduced and validated with vector-based thermal simulations and thermo couple measurements from the build-up process. Two cone-shaped geometries are investigated, namely, an upright and an inverted cone. The examinations are performed and validated with Inconel 718. An outlook to further investigations and more complex, real-life applicable geometries is given.

Reliably manufacturing defect free products is still an open challenge for Laser Powder Bed Fusion processes. Particularly, pores that occur frequently have a negative impact on mechanical properties like fatigue performance. Therefore, an accurate localisation of pores is mandatory for quality assurance, but requires time-consuming post-processing steps like computer tomography scans. Although existing solutions using in-situ monitoring data can detect pore occurrence within a layer, they are limited in their localisation precision. Therefore, we propose a pore localisation approach that estimates their position within a single layer using a Gaussian kernel density estimation. This allows segmentation models to learn the correlation between in-situ monitoring data and the derived probability distribution of pore occurrence. Within our experiments, we compare the prediction performance of different segmentation models depending on machine parameter configuration and geometry features. From our results, we conclude that our approach allows a precise localisation of pores that requires minimal data preprocessing. Our research extends the literature by providing a foundation for more precise pore detection systems.