Aluminum alloys, known for their high strength-to-weight ratios and impressive electrical and thermal conductivities, are extensively used in numerous engineering sectors, such as aerospace, automotive, and construction. Recently, significant efforts have been made to develop novel aluminum alloys specifically tailored for additive manufacturing. These new alloys aim to provide an optimal balance between mechanical properties and thermal/ electrical conductivities. In this study, nine combinatorial samples with various alloy compositions were fabricated using direct energy deposition (DED) additive manufacturing by adjusting the feeding speeds of Al6061 alloy and Al-12Si alloy powders. The effects of the alloying elements on the microstructure, electrical conductivity, and hardness were investigated. Generally, as the Si and Cu contents decreased, electrical conductivity increased and hardness decreased, exhibiting trade-off characteristics. However, electrical conductivity and hardness showed an optimal combination when the Si content was adjusted to below 4.5 wt%, which can sufficiently suppress the grain boundary segregation of the α- Si precipitates, and the Cu content was controlled to induce the formation of Al₂Cu precipitates.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.

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• Effect of Heat Treatment on Microstructure and Mechanical Properties of Al–Zn–Mg–Cu–Si Sintered Alloys with and Without High-energy Ball Milling
  Junho Lee, Seonghyun Park, Sang-Hwa Lee, Seung Bae Son, Seok-Jae Lee, Jae-Gil Jung
  journal of Korean Powder Metallurgy Institute.2023; 30(6): 470.     CrossRef

Aluminum alloys are widely utilized in diverse industries, such as automobiles, aerospace, and architecture, owing to their high specific strength and resistance to oxidation. However, to meet the increasing demands of the industry, it is necessary to design new aluminum alloys with excellent properties. Thus, a new method is required to efficiently test additively manufactured aluminum alloys with various compositions within a short period during the alloy design process. In this study, a combinatory approach using a direct energy deposition system for metal 3D printing process with a dual feeder was employed. Two types of aluminum alloy powders, namely Al6061 and Al-12Cu, were utilized for the combinatory test conducted through 3D printing. Twelve types of Al-Si-Cu-Mg alloys were manufactured during this combinatory test, and the relationship between their microstructures and properties was investigated.

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• Combinatorial Experiment for Al-6061 and Al-12Si alloy Based on Directed Energy Deposition (DED) Process
  Seoyeon Jeon, Suwon Park, Yongwook Song, Jiwon Park, Hyunyoung Park, Boram Lee, Hyunjoo Choi
  journal of Korean Powder Metallurgy Institute.2023; 30(6): 463.     CrossRef

Aluminum alloys are extensively employed in several industries, such as automobile, aerospace, and architecture, owing to their high specific strength and electrical and thermal conductivities. However, to meet the rising industrial demands, aluminum alloys must be designed with both excellent mechanical and thermal properties. Computer-aided alloy design is emerging as a technique for developing novel alloys to overcome these trade-off properties. Thus, the development of a new experimental method for designing alloys with high-throughput confirmation is gaining focus. A new approach that rapidly manufactures aluminum alloys with different compositions is required in the alloy design process. This study proposes a combined approach to rapidly investigate the relationship between the microstructure and properties of aluminum alloys using a direct energy deposition system with a dual-nozzle metal 3D printing process. Two types of aluminum alloy powders (Al-4.99Si-1.05Cu-0.47Mg and Al-7Mg) are employed for the 3D printing-based combined method. Nine types of Al-Si-Cu-Mg alloys are manufactured using the combined method, and the relationship between their microstructures and properties is examined.