Abstract In situ measurement and monitoring techniques have attracted an increased interest in the additive manufacturing (AM) field as they enable an early detection of anomalies and defects, reducing the waste of material and making qualification procedures more efficient. Both the geometry and the surface topography of the solidified layer have been pointed out to be quality characteristics of interest since both deviations from the nominal shape and surface irregularities may indicate departures from the desired process stability and product quality. In laser powder bed fusion (L-PBF), various in situ sensing methods have been proposed to reconstruct either the 2D geometry of the solidified layer or its 3D height map. This study is devoted to a different approach known as polarized imaging, which is suitable to aid both reconstructions with one single sensor and one single image acquisition. It relies on a camera equipped with a polarized sensor, combined with a polarized light source. Its potential to enhance the reconstruction of 2D features in L-PBF has been investigated in a few seminal studies. However, its possible use to aid in the reconstruction of the 3D topography of the layer has not yet been explored in the AM literature. This study analyzes the feasibility of polarized imaging to this aim. An ex situ experimental characterization is first presented to compare the surface topography characterization accuracy against a ground truth reference. A preliminary investigation on the in situ applicability of the method is then presented and discussed, opening to a new adoption of polarized imaging for in situ measurements and monitoring in L-PBF.

The paper at hand evaluates the validity of the transient heat equation with phase change and temperature-dependent coefficients as a model to predict the evolution of melt pools for rapid turnaround scan strategies in a laser powder bed fusion (PBF-LB) additive manufacturing (AM) process. In such scenarios, residual heat effects due to short laser-off times between tracks significantly impact the melt pool morphology. Results show that the conduction model accurately captures the rapidly evolving melt pool morphologies under these conditions. This is a fundamental discovery, as the ability to predict such dynamic scenarios has previously only been attributed to much more involved models which include fluid-like effects that otherwise require computational fluid dynamics (CFD). However, the presented validation (e.g., validated comparison to surface topography measurements) clearly demonstrates that a large portion of the observed effects is actually captured by the heat diffusion equation, including a common model of phase change. As a proof of concept for an application of this insight, feedforward control is investigated. Therein, the validated model predicts the melt pool morphology for a modified scan strategy with an increased pause time between the tracks that reduces melt pool distortions observed in the original configuration.