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Abstract Additive Friction stir deposition (AFSD) has been extensively utilized for processing Al alloys. The properties of the Al depositions under as-fabricated state, including mechanical strength and corrosion resistance, are typically inferior compared to the base material, especially for heat-treatable alloys. In this research, multilayers of Al7075 composites, reinforced by ceramic particles, were processed by AFSD to evaluate the effect of using feedstock materials containing reinforcing particles on the properties of the deposition. For comparison, a bare Al7075 part was also processed by AFSD under the same conditions. The results of mechanical testing revealed a significant reduction in the microhardness, tensile strength and compression stress of the bare alloy after deposition. However, the composite deposition exhibited only a slight decrease in the properties compared to its feedstock material. Additionally, the corrosion resistance of the composite enhanced after AFSD, in contrast to the bare alloy, where the corrosion resistance deteriorated. Microstructural analysis showed a uniform distribution of the reinforcing particles in the matrix for the deposition, closely resembling that of the feedstock composite. This, along with grain refinement and minimal change in precipitates, were the reasons for the minimum changes in mechanical properties, as well as the improvement in corrosion resistance. 

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. 

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.