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The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%. [ABSTRACT FROM AUTHOR]

The competition between epitaxial vs. equiaxed solidification has been investigated in CMSX-4 single crystal superalloy during laser melting as practiced in additive manufacturing. Single-track laser scans were performed on a powder-free surface of directionally solidified CMSX-4 alloy with several combinations of laser power and scanning velocity. Electron backscattered diffraction (EBSD) mapping facilitated identification of new orientations, i.e., "stray grains" that nucleated within the fusion zone along with their area fraction and spatial distribution. Using high-fidelity computational fluid dynamics simulations, both the temperature and fluid velocity fields within the melt pool were estimated. This information was combined with a nucleation model to determine locations where nucleation has the highest probability to occur in melt pools. In conformance with general experience in metals additive manufacturing, the as-solidified microstructure of the laser-melted tracks is dominated by epitaxial grain growth; nevertheless, stray grains were evident in elongated melt pools. It was found that, though a higher laser scanning velocity and lower power are generally helpful in the reduction of stray grains, the combination of a stable keyhole and minimal fluid velocity further mitigates stray grains in laser single tracks. [ABSTRACT FROM AUTHOR]

Binder jet 3D printing combined with post-deposition sintering is a non-beam additive manufacturing (AM) method for the creation of complex 3-dimensional structures. Binder saturation and particle morphology are two important factors affecting the quality of printed parts. Here, we investigated the effects of binder saturation on dimension accuracy, porosity, microstructure, and microhardness of nickel-based alloy 625 samples made of differently atomized powders. Argon gas atomized (GA) and water atomized (WA) nickel-based alloy 625 powders were used to binder jet samples for a detailed comparative study. The optimal binder saturation for WA system is 60 to 70%, whereas for GA system the optimal is about 80%. Generally, GA samples achieved better overall quality than WA samples in terms of packing density, dimensional accuracy, sintered density, and microhardness. This difference is attributed mainly to the particle morphology including sphericity and roundness. The critical threshold for visible binder bleeding phenomenon in WA and GA systems is determined to be 120% and 140% binder saturation, respectively. Mechanisms for binder bleeding phenomenon at different saturation levels for WA and GA systems are discussed in detail. A pore evolution model is proposed to better understand the printing and sintering processes. [ABSTRACT FROM AUTHOR]