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The purpose of this paper is to determine the best dimple core design for metal sandwich panels by investigating the various critical criteria and core design parameters using the fuzzy-hybrid multi-criteria decision-making tool. The structural integrity of a sandwich panel depends on the core design and significantly affects the bonding strength. The continuous design and testing of a sandwich panel is a very lengthy process that increases the design time. The simulation analysis output was segregated into nine critical failure criteria. All the critical criteria weightages were evaluated using the Fuzzy-Analytical Hierarchical Process, while the Fuzzy-Technique for Order Preference by Similarity to Ideal Solution was used to evaluate the Closeness Coefficient value to determine the best core design configuration. The results indicate that the core configuration with a diameter of 6.0 mm and a depth of 3.0 mm obtained the highest closeness coefficient values, 0.9937 and 0.9294, under cyclic loading conditions of 50% and 70%. It was shown that using average sizes in the dimple configuration tends to provide better delamination resistance and structural integrity. This study contributes to the selection of the optimum core design configuration based on the various design criteria and using non-complex and competent analysis.
RESUMO
Additive Manufacturing of Polymer-Fiber Composites is a newly open Special Issue of Materials, which aims to publish original and review papers on new scientific and applied research, and make great contributions to the finding and understanding of the fabrication of fiber-reinforced polymer composites using current advanced additive manufacturing techniques [...].
RESUMO
Vibration fatigue characteristics are critical for rotating machinery components such as turbine rotor blades. Lattice structures are gaining popularity in engineering applications due to their unique ability to reduce weight and improve the mechanical properties. This study is an experimental investigation of octet-truss lattice structure utilization in turbine rotor blades for weight reduction and to improve vibration fatigue characteristics. One completely solid and three lattice infilled blades with variable strut thickness were manufactured via additive manufacturing. Both free and forced experimental vibration analyses were performed on the blades to investigate their modal and vibration fatigue characteristics. The blades were subjected to random vibration using a vibration shaker. The response was measured using a triaxial accelerometer in terms of vibration acceleration time histories in the X, Y, and Z directions. Results indicate a weight reduction of up to 24.91% and enhancement in the first natural frequency of up to 5.29% were achieved using lattice infilled blades. The fatigue life of the blades was investigated using three frequency domain approaches, namely, Lalanne, Dirlik and narrow band. The fatigue life results indicate that the 0.25 mm lattice blade exhibits the highest fatigue life, while the solid blade exhibits the lowest fatigue life of all four blades. The fatigue life of the 0.25 mm lattice blade was 1822-, 1802-, and 1819- fold higher compared to that of the solid blade, using the Lalanne, Dirlik, and narrow-band approaches, respectively. These results can serve as the first step towards the utilization of lattice structures in turbine blades, with thermal analysis as the next step. Therefore, apart from being light weight, the octet-truss lattice infilled blades exhibited superior vibration fatigue characteristics to vibration loads, thereby making them a potential replacement for solid blades in turbine rotors.
RESUMO
The conventional component repair in remanufacturing involves human decision making that is influenced by several factors such as conditions of incoming cores, modes of failure, severity of damage, features and geometric complexities of cores and types of reparation required. Repair can be enhanced through automation using additive manufacturing (AM) technology. Advancements in AM have led to the development of directed energy deposition and laser cladding technology for repair of damaged parts and components. The objective of this systematic literature review is to ascertain how intelligent systems can be integrated into AM-based repair, through artificial intelligence (AI) approaches capable of supporting the nature and process of decision making during repair. The integration of intelligent systems in AM repair is expected to enhance resource utilization and repair efficiency during remanufacturing. Based on a systematic literature review of articles published during 2005-2021, the study analyses the activities of conventional repair in remanufacturing, trends in the applications of AM for repair using the current state-of-the-art technology and how AI has been deployed to facilitate repair. The study concludes with suggestions on research areas and opportunities that will further enhance the automation of component repair during remanufacturing using intelligent AM systems.