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Abstract Additive friction stir deposition (AFSD) is a novel additive manufacturing technique that enables the fabrication of components in the solid state. Given the benefits of AFSD, understanding the behavior of various feedstock materials after undergoing the AFSD process is crucial for optimizing their performance in structural applications. This study aims to evaluate the effects of AFSD on an Al–Mg alloy, Al5086, comparing it to its initial H32 condition to assess the changes in mechanical properties, microstructure, corrosion resistance, microhardness, and electrical conductivity. Tensile testing showed a 23% reduction in yield strength for as-deposited samples, while ultimate tensile strength remained comparable to the feedstock. Ductility improved significantly, with elongation to failure increasing by 77%, attributed to grain refinement and dynamic recovery. Microhardness decreased by 16% in lower layers due to thermal exposure, but electrical conductivity remained stable, indicating minimal solute atom redistribution. The Nitric Acid Mass Loss Test (NAMLT) revealed a 245% increase in corrosion rate for the AFSD material, linked to the higher density of grain boundaries acting as pathways for corrosion. These findings highlight AFSD’s potential for improving ductility and formability. However, they underscore the need for optimization to reduce corrosion susceptibility and address mechanical strength trade-offs. Future work should focus on fine-tuning process parameters or implementing post-treatment methods to enhance corrosion and mechanical performance. 

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. 

Additive friction stir deposition (AFS-D) is considered a productive method of additive manufacturing (AM) due to its ability to produce dense mechanical parts at a faster deposition rate compared to other AM methods. Al6061 alloy finds extensive application in aerospace and nuclear engineering; nevertheless, exposure to radiation or high-energy particles over time tends to deteriorate their mechanical performance. However, the effect of radiation on the components manufactured using the AFS-D method is still unexamined. In this work, samples from the as-fabricated Al6061 alloy, by AFS-D, and the Al6061 feedstock rod were irradiated with He+ ions to 10 dpa at ambient temperature. The microstructural and mechanical changes induced by irradiation of He+ were examined using a scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), and nanoindentation. This study demonstrates that, at 10 dpa of irradiation damage, the feedstock Al6061 produced a bigger size of He bubbles than the AFS-D Al6061. Nanoindentation analysis revealed that both the feedstock Al6061 and AFS-D Al6061 samples have experienced radiation-induced hardening. These studies provide a valuable understanding of the microstructural and mechanical performance of AFS-D materials in radiation environments, offering essential data for the selection of materials and processing methods for potential application in aerospace and nuclear engineering. 

Solid-state additive friction stir deposition (AFSD) is a thermomechanical-based additive manufacturing technique. For this study, AFSD was utilized to produce aluminum alloy 6061 (AA6061) blocks with varying layer thicknesses (1 mm, 2 mm, and 3 mm). The mechanical properties were assessed through uniaxial tensile tests and Vickers microhardness measurement, and statistical analysis was employed to investigate differences among data groups. The results revealed that the deposition layer thickness influences tensile properties in the building (Z) direction, while the properties in the X and Y directions showed minor differences across the three AFSD blocks. Furthermore, variations in tensile properties were observed depending on the sample orientation in the AFSD blocks and its depth-wise position in the part in the building direction. The microhardness values decreased non-linearly along the building direction, spread across the width of the part’s cross-section, and highlighted that the deposition layer thickness significantly affects this property. The 1 mm block exhibited lower average microhardness values than the 2 mm and 3 mm blocks. The temperature histories and dynamic heat treatment are influenced by the deposition layer thickness and depend on the location of the point being studied in the part, resulting in variations in the microstructure and mechanical properties along the building direction and across the part’s width. 

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.