Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R² score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.

As the most commercially developed metal additive process, laser powder bed fusion (LPBF) is vital to advancing several industry sectors, enabling high-precision part production across aerospace, biomedical, and manufacturing industries. Al 7075 alloy offers low density and high-specific strength yet faces LPBF challenges such as hot cracking and porosity due to rapid solidification, thermal gradients, and a wide freezing range. To address these challenges, this study proposes an integrated computational and experimental framework to enhance the LPBF processability of Al 7xxx alloys by compositional modification. Using the Calculation of Phase Diagram approach, printable Al 7xxx compositions were designed by adding grain refiners (V and/or Ti) and a eutectic solidification enhancer (Mg) to Al 7075 alloy to enable grain refinement and eutectic solidification. Subsequent LPBF experiments and characterization tests, such as metallography (scanning electron microscopy), energy-dispersive X-ray spectroscopy, X-ray diffraction, and X-ray micro-computed tomography, confirmed the production of refined microstructures with reduced defects. This study contributes to existing approaches for producing high-quality Al 7xxx alloy parts without significant compositional deviations using an integrated computational and experimental approach. Finally, aligning with the Materials Genome Initiative, this study contributes to the development and industrial adoption of advanced materials.

This study investigated the impact of low-temperature heat treatments on the mechanical and thermophysical properties of Cu-10Sn alloys fabricated by a laser powder bed fusion (LPBF) additive manufacturing (AM) process. The microstructure, phase structure, and mechanical and thermal properties of the LPBF Cu-10Sn samples were comparatively investigated under both the as-fabricated (AF) condition and after low-temperature heat treatments at 140, 180, 220, 260, and 300 °C. The results showed that the low-temperature heat treatments did not significantly affect the phase and grain structures of the Cu-10Sn alloys. Both pre- and post-treatment samples displayed consistent grain sizes, with no obvious X-ray diffraction angle shift for the α phase, indicating that atom diffusion of the Sn element is beyond the detection resolution of X-ray diffractometers (XRD). However, the 180 °C heat-treated sample exhibited the highest hardness, while the AF samples had the lowest hardness, which was most likely due to the generation of precipitates according to thermodynamics modeling. Heat-treated samples also displayed higher thermal diffusivity values than their AF counterpart. The AF sample had the longest lifetime of ~0.19 nanoseconds (ns) in the positron annihilation lifetime spectroscopy (PALS) test, indicating the presence of the most atomic-level defects.

In this paper, laser powder bed fusion (L-PBF) prepared GRX-810 samples with different yttria contents (from 0.3 to 1 wt%) were systematically examined. The mechanical properties and thermal properties were measured under as-built and Hot Isostatic Pressing (HIP) heat treated conditions. The results indicate that yttria content significantly affects the thermal properties of as-built L-PBF GRX-810. Higher yttria reduces thermal diffusivity and conductivity by up to 6% in of as-built samples. HIP restores thermal properties to low-yttria levels, indicating that L-PBF-induced microdefects and porosity, rather than composition, primarily cause the thermal degradation. Based on the mechanical testing results, yttria content up to 0.45% provides the most favorable strength–ductility balance in the present dataset. Higher yttria content (from 0.5 to 1%) leads to the reduction of the elongation from 27% to around 4% in the as-built samples. Following HIP, the yield strength decreases from ~ 785 MPa in the as-built state to ~ 685 MPa. The elongation increases from 4% in the as-built state to ~ 42%. HIP samples with yttria content less than 0.45% exhibit a high ductility at approximately 40% maximum elongation, while HIP samples with yttria content higher than 0.5% exhibit a moderate ductility of approximately 18% maximum elongation at failure. These thermal and mechanical performance studies provide valuable guidance for GRX-810 applications and provide a reference for further AM-ODS alloy optimization.