Understanding the formation of defects in Laser Powder Bed Fusion (L-PBF) is important for manufacturing parts that perform to desired specifications. The region of molten material underneath the melting laser beam and inside the vapor depression governs the formation of keyhole porosity in the final part as well as the alloy composition due to preferential evaporation of elements. Although this region is important to defect formation, it is not well quantified by thermal imaging methods because light emitted from a specified region of the surface undergoes reflections in the vapor depression that distort the perceived temperature at the camera. We present a method for simulating thermal imaging experiments on difficult concave melt pool surfaces that accounts for high-temperature reflections, reconciling discrepancies between experimental and simulated thermal fields, and addressing a key challenge in understanding defect formation in Laser powder bed fusion. We begin with bare plate computational fluid dynamics (CFD) temperature profiles and use radiative networks to predict local radiosities that incorporate reflections before passing the light through the simulated optical train to the camera sensor. This process enables an apples-to-apples comparison of CFD predictions of temperature and thermal images. For example, without considering reflections, the maximum deviation in the centerline temperature of 316L steel melt pools is up to 15%. However, with this correction, the maximum deviations are reduced to 4%–6%. Our findings indicate that longer imaging wavelengths reduce the impact of reflections on perceived experimental temperatures. By enabling a more rigorous validation of CFD temperature profiles, the method can help to control for keyhole porosity and powder feedstock alloy composition. Due to the method’s material and surface-agnostic nature, it will also find applications in other fields where understanding high surface temperatures is important such as in aerospace when imaging high-temperature turbine blades, or in renewable energy when working with concentrated solar power technologies.
Weeks et al. (Mon,) studied this question.