Page 49 - ESAM-1-1
P. 49

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



               doi: 10.1016/j.aei.2025.103221                     2023;1(2):e14.
            57.  Pawlowski JM, Urban JM. Reducing autocorrelation times      doi: 10.1002/mgea.14
               in lattice simulations with generative adversarial networks.
               Mach Learn Sci Technol. 2020;1(4):045011.       66.  Gou W, Shi ZZ, Zhu Y, Gu XF,  et al. Multi‐objective
                                                                  optimization of three mechanical properties  of Mg alloys
               doi: 10.1088/2632-2153/abae73                      through  machine learning.  Mater  Genome  Eng  Adv.
            58.  Bernardi ML, Casciani A, Cimitile M, Marrella A. Conversing   2024;2(3):e54.
               with  business  process-aware  large  language  models:  The      doi: 10.1002/mgea.54
               BPLLM framework. J Intell Inform Syst. 2024;62(6):1607-1629.
                                                               67.  Raj R, Tsai JT, Prajapati MJ, Jeng JY. Lattice-based
               doi: 10.1007/s10844-024-00898-1                    interpenetrating  phase  composite  metamaterial
            59.  Parsa-Pajouh A. Application of generative AI to automate   fabricated with hybrid material extrusion process for
               numerical analysis and synthetic data generation in   tunable mechanical properties. J Mater Res Technol. 2025;35:
               geotechnical engineering.  Mach  Learn  Data  Sci  Geotechn.   2955-2972.
               2025;1(1):46-55.                                   doi: 10.1016/j.jmrt.2025.01.217
               doi: 10.1108/mlag-09-2024-0008                  68.  Leclercq A, Brailovski V. Improving laser powder bed
            60.  Gebreab S, Musamih A, Salah K, Jayaraman R, Boscovic D.   fusion  printability of  tungsten powders  using simulation-
               Accelerating digital twin development with generative AI:   driven process optimization algorithms.  Materials  (Basel).
               A framework for 3D modeling and data integration. IEEE   2024;17(8):1865.
               Access. 2024;12:185918-185936.
                                                                  doi: 10.3390/ma17081865
               doi: 10.1109/access.2024.3514175
                                                               69.  Venugopal V, McConaha M, Anand S. Integration of design
            61.  Wei X, Kumar N, Zhang H. Addressing bias in generative   for additive manufacturing constraints with multimaterial
               AI:  Challenges  and research opportunities  in  information   topology optimization of lattice structures for optimized
               management. Inform Manag. 2025;62(2):104103.       thermal and mechanical properties.  J  Manuf Sci Eng.
               doi: 10.1016/j.im.2025.104103                      2022;144(4):041003.
            62.  Satyadhar J. Review of Gen AI models for financial risk      doi: 10.1115/1.4052193
               management. Int J Sci Res Comput Sci Eng Inform Technol.   70.  Jam A, du Plessis A, Lora C, Raghavendra S, Pellizzari M,
               2025;11(1):709-723.                                Benedetti  M.  Manufacturability  of  lattice  structures
               doi: 10.32628/cseit2511114                         fabricated by laser powder bed fusion: A novel biomedical
                                                                  application of the beta Ti-21S alloy.  Addit  Manuf.
            63.  Yu H, Guo Y. Generative artificial intelligence empowers   2022;50:102556.
               educational reform: Current Status, issues, and prospects.
               Front Educ. 2023;8:1183162.                        doi: 10.1016/j.addma.2021.102556
               doi: 10.3389/feduc.2023.1183162                 71.  Tyagi SA, Manjaiah M. Additive manufacturing of titanium-
                                                                  based lattice structures for medical applications – A review.
            64.  Gong Y, Zhang Q, Ren Y, Liu Z, Abu Seman MT. Research   Bioprinting. 2023;30:e00267.
               on Fuzzy Control of methanol distillation based on SHAP
               (SHapley Additive exPlanations) interpretability and      doi: 10.1016/j.bprint.2023.e00267
               generative artificial intelligence. Sensors. 2025;25(5):1308.  72.  Khan N, Riccio A. A systematic review of design for additive
               doi: 10.3390/s25051308                             manufacturing of aerospace lattice structures: Current trends
                                                                  and future directions. Prog Aerospace Sci. 2024;149:101021.
            65.  Shi B, Lookman T, Xue D. Multi‐objective optimization and
               its application in materials science. Mater Genom Eng Adv.      doi: 10.1016/j.paerosci.2024.101021


















            Volume 1 Issue 1 (2025)                         14                         doi: 10.36922/ESAM025110006
   44   45   46   47   48   49   50   51   52   53   54