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Engineering Science in
            Additive Manufacturing                                                   Gen-AI for lattice structure design



               GLU3D. Adv Funct Mater. 2024;34(41):2404165.       doi: 10.1007/s00170-024-14264-6
               doi: 10.1002/adfm.202404165                     46.  Liu J, Xu M, Zhang R, Zhang X, Xi W. Progress of porous/
            36.  Peng C, Tran P, Rutz E. Accelerating hybrid lattice structures   lattice structures applied in thermal management technology
               design with machine learning.  Mater Sci Addit Manuf.   of aerospace applications. Aerospace. 2022;9(12):827.
               2024;3(2):3430.                                    doi: 10.3390/aerospace9120827
               doi: 10.36922/msam.3430                         47.  Banh L, Strobel G. Generative artificial intelligence.  Elect
            37.  Su J, Ng WL, An J, Yeong WY, Chua CK, Sing SL.   Mark. 2023;33(1):63.
               Achieving sustainability by additive manufacturing:      doi: 10.1007/s12525-023-00680-1
               A state-of-the-art review and perspectives.  Virt Phys
               Prototyp. 2024;19(1):2438899.                   48.  Ooi KB, Tan GW, Al-Emran M,  et al. The Potential
                                                                  of generative artificial intelligence across disciplines:
               doi: 10.1080/17452759.2024.2438899                 Perspectives and future directions. J Comput Inform Syst.
            38.  Verma S, Kumar A, Lin SC, Jeng JY. A  bio-inspired   2023;65(1):76-107.
               design strategy for easy powder removal in powder-bed      doi: 10.1080/08874417.2023.2261010
               based additive manufactured lattice structure.  Virt Phys
               Prototyp. 2022;17(3):468-488.                   49.  Peng XL, Xu BX. Data-driven inverse design of composite
                                                                  triangular lattice structures. Int J Mech Sci. 2024;265:108900.
               doi: 10.1080/17452759.2022.2039071
                                                                  doi: 10.1016/j.ijmecsci.2023.108900
            39.  Lu P, Shi X, Ye X, et al. Mechanical performance and flow heat
               transfer characteristics of topologically optimised Ti-6Al-4   50.  Zhang K, Guo Y, Liu X, Hong F, Hou X, Deng Z. Deep
               V lattice structures. Virt Phys Prototyp. 2024;19(1):2429526.  learning-based inverse design of lattice metamaterials for
                                                                  tuning bandgap. Extreme Mech Lett. 2024;69:102165.
               doi: 10.1080/17452759.2024.2429526
                                                                  doi: 10.1016/j.eml.2024.102165
            40.  Zhang Y, Aiyiti W, Du S, Jia R, Jiang H. Design and
               mechanical behaviours of a novel tantalum lattice structure   51.  Yang Z, Guo Y, Sun Z, et al. GraphDGM: A Generative data-
               fabricated by SLM. Virt Phys Prototyp. 2023;18(1):2192702.  driven  design  approach  for  frame  and  lattice  structures.
                                                                  J Mech Des. 2025;147:101701.
               doi: 10.1080/17452759.2023.2192702
                                                                  doi: 10.1115/1.4068106
            41.  Chua C, Sing SL, Chua CK. Characterisation of in-situ
               alloyed titanium-tantalum lattice structures by laser   52.  Li Z, Li J, Tian J, et al. Inverse design of cellular structures
               powder bed fusion using finite element analysis. Virt Phys   with the geometry of triply periodic minimal surfaces using
               Prototyp. 2022;18(1):2138463.                      generative artificial  intelligence algorithms.  Eng Struct.
                                                                  2024;321:118988.
               doi: 10.1080/17452759.2022.2138463
                                                                  doi: 10.1016/j.engstruct.2024.118988
            42.  Ren Y, Li Y, Yang L, et al. Compressive properties and fatigue
               performance of NiTi lattice structures optimized by TPMS.   53.  Challapalli A, Patel D, Li G. Inverse machine learning
               Mater Sci Addit Manuf. 2024;3(2):e3380.            framework for optimizing lightweight metamaterials. Mater
                                                                  Des. 2021;208:109937.
               doi: 10.36922/msam.3380
                                                                  doi: 10.1016/j.matdes.2021.109937
            43.  Liu Y, Sing SL. A  review of advances in additive
               manufacturing and the integration of high-performance   54.  Eren O, Yüksel N, Börklü HR, Sezer HK, Canyurt OE. Deep
               polymers, alloys, and their composites.  Mater Sci Addit   learning-enabled design for tailored mechanical properties
               Manuf. 2023;2(3):1587.                             of SLM-manufactured metallic lattice structures. Eng Appl
                                                                  Artif Intell. 2024;130:107685.
               doi: 10.36922/msam.1587
                                                                  doi: 10.1016/j.engappai.2023.107685
            44.  Ahmed Qureshi Z, Addin Burhan Al-Omari S, Elnajjar E,
               Al-Ketan O, Abu Al-Rub R. Architected lattices embedded   55.  Yüksel N, Eren O, Börklü HR, Sezer HK. Mechanical
               with phase change materials for thermal management of   properties of additively manufactured lattice structures
               high-power electronics: A numerical study. Appl Therm Eng.   designed by deep learning.  Thin Walled Struct.
               2023;219:119420.                                   2024;196:111475.
               doi: 10.1016/j.applthermaleng.2022.119420          doi: 10.1016/j.tws.2023.111475
            45.  Balthazar M, Baudin N, Soto J, Edelin D, Guéroult S,   56.  Duan C,  Wu D. Inverse design of lattice structures with
               Sobotka V. Improvement of thermal management of    target  mechanical  performance  via  generative  adversarial
               composites forming process tooling using lattice structures.   networks considering the effect of process parameters. Adv
               Int J Adv Manuf Technol. 2024;134(5-6):2705-2723.  Eng Inform. 2025;65:103221.


            Volume 1 Issue 1 (2025)                         13                         doi: 10.36922/ESAM025110006
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