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.

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.

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.

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

PURPOSES: This study aims to suggest improvements to the management implementation plan for privately-financed roads. METHODS: We reviewed the establishment procedures and contents of the management implementation plan for expired businesses and analyzed the limits of detailed instructions. Additionally, we created a checklist including the needs of competent authorities to solve practical matters. Furthermore, we introduced the analytic hierarchy process (AHP) method to reflect aspects that were not covered in the detailed instructions. RESULTS: We suggested criteria for the decision-making structure system using the AHP method for efficient decision-making between the operation under government management and contracting-out. CONCLUSIONS: The proposed AHP method can consider other alternatives that are not proposed in the previous detailed instructions. However, further study is required with regard to the redundancy verification and appropriateness of weights of the evaluation items.