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Lattice metamaterials based on three-period minimum surface (TPMS) are an effective means to achieve lightweight and high-strength materials which are widely used in various fields such as aerospace and ships. However, its vibration and noise reduction, and damping properties have not been fully studied. Therefore, in this study, the TPMS structures with parameterization were designed by the method of surface migration, and the TPMS structures with high forming quality was manufactured by laser powder bed fusion (LPBF). The mechanical properties and energy absorption characteristics of the beam and TPMS structures were studied and compared by quasi-static compression. The modal shapes of the beam lattice structures and TPMS structures were obtained by the free modal analysis, and the damping properties of two structures were obtained by modal tests. For the two structures after heat treatment with the same porosity of 70%, the yield strength of the beam lattice structure reaches 40.76 MPa, elastic modulus is 20.38 GPa, the energy absorption value is 32.23 MJ·m-3, the damping ratio is 0.52%. The yield strength, elastic modulus, energy absorption value, and damping ratio of the TPMS structure are 50.74 MPa, 25.37 GPa, 47.34 MJ·m-3, and 0.99%, respectively. The results show that TPMS structures exhibit more excellent mechanical properties and energy absorption, better damping performance, and obvious advantages in structural load and vibration and noise reduction compared with the beam lattice structures under the same porosity. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. [ABSTRACT FROM AUTHOR]

Nickel-based superalloy lattice sandwich structures present higher stiffness, higher strength and higher temperature resistance in comparison with other metals. In this study, the Kagome unit was adopted to design the lattice sandwich structure and ProCAST software was used to simulate the filling and solidification processes of the nickel-based superalloy. Grain morphology and sizes of the nickel-based superalloy lattice sandwich structures were simulated by using of cellular automaton coupled with finite element model (CAFE), and indirect additive manufacture combining with investment casting were carried out to fabricate the nickel-based superalloy lattice sandwich structures. The calculated grain morphology and sizes are in good agreement with the experimental results. The grains are mainly equiaxed with an average size of about 500 µm. The simulated results also show that the superheat of melting and the mold preheated temperature have significant influence on the grain size of the Kagome lattice sandwich structures, lower superheat of melting and mold preheated temperatures are encouraged to obtain the fine grains while assuring the integrity of the Kagome lattice sandwich structures for industrial application. [ABSTRACT FROM AUTHOR]