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As high-strength aluminum alloys present several processability issues with additive manufacturing (AM), Scalmalloy®, an Al-Mg-Sc-Zr-based alloy, has been developed. This alloy is age-hardenable, allowing it to precipitate out a strengthening precipitate phase, Al3(Sc,Zr). The manufacturer recommends a single-stage aging treatment at 325 °C for 4 h; however, the majority of the literature studies utilize a powder bed processing known as selective laser melting (SLM) over powder-fed processing directed energy deposition (DED). This study addresses the lack of information on heat treatments for DED fabrication by exploring the application of artificial aging temperatures of 300–400 °C for 2, 4, and 6 h to: 1. determine the impact on the microstructural evolution and mechanical performance and 2. determine whether the recommended treatment for Scalmalloy® is appropriate for DED fabrication. Tensile testing determined that low-temperature treatments exhibited no visible dependence on time (2–6 h); however, time becomes influential at higher temperatures starting at 350 °C. The temperature plays a considerable role in the mechanical and microstructural behaviors of DED Scalmalloy®. The highest tensile strength was noted at 300 °C (384 MPa, 21.6% increase), but all heat-treated cases resulted in an improvement over the as-built case. This investigation established that increasing the treatment temperature resulted in a decreasing trend for the tensile strength that held over time. Elongation at 2 h displayed a near parabolic trend that peaks at 350 °C (20%) and falls with higher temperatures. At the 4 h treatment, a slight decreasing trend was noticed for elongation. No visible change was observed for elongation at 6 h, with elongation values remaining fairly consistent. The microstructural evolution, including micron-sized and nano-sized Al3(Sc,Zr) and grain size, was examined, and coarsening effects were noted with the increase in the temperature. It is recommended that treatment be conducted at 300 °C to achieve the precipitation of the strengthening Al3(Sc,Zr) phase while minimizing coarsening.

 

Scalmalloy® is an Al-Mg-Sc-Zr-based alloy specifically developed for additive manufacturing (AM). This alloy is designed for use with a direct aging treatment, as recommended by the manufacturer, rather than with a multistep treatment, as often seen in conventional manufacturing. Most work with Scalmalloy® is conducted using powder bed rather than powder-fed processes. This investigation seeks to fill this knowledge gap and expand beyond single-step aging to promote an overall balanced AM-fabricated component. For this study, directed energy deposition (DED)-fabricated Scalmalloy® components were subjected to low-temperature treatments to minimize residual stresses inherent in the material due to the layer-by-layer build process. X-ray diffraction (XRD) indicated the possibility of stress minimization while reducing the detriment to mechanical strength through lower temperature treatments. Microstructural analyses consisting of energy dispersion spectroscopy (EDS) and electron backscatter diffraction (EBSD) revealed the presence of grain growth detrimentally affecting the strength and elongation made possible by very small grains inherent to AM and rapid solidification. Tensile testing determined that treatment at 175 °C for 1 h provides the best relief from the existing residual stresses; however, this is accompanied by a diminishment in the yield and tensile strength of 19 and 9.5%, respectively. It is noted that treatment at 175 °C for 2 h did not provide as great of a decrease in residual stresses, theorized to be the result of grain growth and other strengthening mechanisms further stressing the structure; however, the residual stresses are still significantly diminished compared with the as-built condition. Furthermore, a minimal reduction of the tensile strengths indicates the possibility of finding a balance between property diminishment and stress state through the work proposed here.

 

Additive manufacturing (AM) simulations are effective for materials that are well characterized and published; however, for newer or proprietary materials, they cannot provide accurate results due to the lack of knowledge of the material properties. This work demonstrates the process of the application of mathematical search algorithms to develop an optimized material dataset which results in accurate simulations for the laser directed energy deposition (DED) process. This was performed by first using a well-characterized material, Ti-64, to show the error in the predicted melt pool was accurate, and the error was found to be less than two resolution steps. Then, for 7000-series aluminum using a generic material property dataset from sister alloys, the error was found to be over 600%. The Nelder–Mead search algorithm was then applied to the problem and was able to develop an optimized dataset that had a combined width and depth error of just 9.1%, demonstrating that it is possible to develop an optimized material property dataset that facilitates more accurate simulation of an under-characterized material.

 

In additive manufacturing (AM), the surface roughness of the deposited parts remains significantly higher than the admissible range for most applications. Additionally, the surface topography of AM parts exhibits waviness profiles between tracks and layers. Therefore, post-processing is indispensable to improve surface quality. Laser-aided machining and polishing can be effective surface improvement processes that can be used due to their availability as the primary energy sources in many metal AM processes. While the initial roughness and waviness of the surface of most AM parts are very high, to achieve dimensional accuracy and minimize roughness, a high input energy density is required during machining and polishing processes although such high energy density may induce process defects and escalate the phenomenon of wavelength asperities. In this paper, we propose a systematic approach to eliminate waviness and reduce surface roughness with the combination of laser-aided machining, macro-polishing, and micro-polishing processes. While machining reduces the initial waviness, low energy density during polishing can minimize this further. The average roughness (Ra=1.11μm) achieved in this study with optimized process parameters for both machining and polishing demonstrates a greater than 97% reduction in roughness when compared to the as-built part.