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International Journal of AI for
            Materials and Design                                                  AI-driven material development for AM


               doi: 10.1038/s41524-020-0308-7                     doi: 10.1016/j.mtcomm.2023.106147
            78.  Liu S, Kappes BB, Amin-Ahmadi B, Benafan O, Zhang X,   88.  Butt J, Mohaghegh V. Combining digital twin and machine
               Stebner AP. Physics-informed machine learning for   learning for the fused filament fabrication process. Metals.
               composition-process-property design: Shape memory alloy   2022;13(1):24.
               demonstration. Appl Mater Today. 2021;22:100898.
                                                                  doi: 10.3390/met13010024
               doi: 10.1016/j.apmt.2020.100898
                                                               89.  Zhang J, Wang P, Gao RX. Deep learning-based tensile
            79.  Li Z, Pradeep KG, Deng Y, Raabe D, Tasan CC. Metastable   strength prediction in fused deposition modeling. Comput
               high-entropy  dual-phase  alloys  overcome  the  strength-  Ind. 2019;107:11-21.
               ductility trade-off. Nature. 2016;534(7606):227-230.     doi: 10.1016/j.compind.2019.01.011
               doi: 10.1038/nature17981                        90.  Lee S, Zhang Z, Gu GX. Generative machine learning
            80.  Tapia G, Khairallah S, Matthews M, King WE, Elwany A.   algorithm for lattice structures with superior mechanical
               Gaussian process-based surrogate modeling framework   properties. Mater Horiz. 2022;9(3):952-960.
               for process planning in laser powder-bed fusion additive      doi: 10.1039/D1MH01792F
               manufacturing of 316L stainless steel.  Int J Adv Manuf
               Technol. 2018;94(9-12):3591-3603.               91.  He Y, Abdi M, Trindade GF,  et al. Exploiting generative
                                                                  design for 3D printing of bacterial biofilm resistant
               doi: 10.1007/s00170-017-1045-z                     composite devices. Adv Sci. 2021;8(15):2100249.
            81.  Chen D, Skouras M, Zhu B, Matusik W. Computational      doi: 10.1002/advs.202100249
               discovery of extremal microstructure families.  Sci Adv.
               2018;4(1):eaao7005.                             92.  Goh GD, Sing SL, Lim YF, et al. Machine learning for 3D
                                                                  printed multi-materials tissue-mimicking anatomical
               doi: 10.1126/sciadv.aao7005                        models. Mater Des. 2021;211:110125.
            82.  Xue T, Wallin TJ, Menguc Y, Adriaenssens S, Chiaramonte M.      doi: 10.1016/j.matdes.2021.110125
               Machine learning generative models for automatic design of
               multi-material 3D printed composite solids. Extreme Mech   93.  Veerabagu U, Palza H, Quero F. Review: Auxetic polymer-
               Lett. 2020;41:100992.                              based mechanical metamaterials for biomedical applications.
                                                                  ACS Biomater Sci Eng. 2022;8(7):2798-2824.
               doi: 10.1016/j.eml.2020.100992
                                                                  doi: 10.1021/acsbiomaterials.2c00109
            83.  Fleisch M, Thalhamer A, Meier G,  et  al. Functional
               mechanical metamaterial with independently  tunable   94.  Sinha P, Mukhopadhyay T. Programmable multi-physical
               stiffness in the three spatial directions.  Mater Today Adv.   mechanics of mechanical metamaterials.  Mater Sci Eng R
               2021;11:100155.                                    Rep. 2023;155:100745.
               doi: 10.1016/j.mtadv.2021.100155                   doi: 10.1016/j.mser.2023.100745
            84.  Bessa MA, Glowacki P, Houlder M. Bayesian machine   95.  Jiao P, Chen T, Xie Y. Self-adaptive mechanical metamaterials
               learning in metamaterial design: Fragile becomes   (SMM) using shape memory polymers for programmable
               supercompressible. Adv Mater. 2019;31(48):1904845.  postbuckling  under  thermal  excitations.  Compos Struct.
                                                                  2021;256:113053.
               doi: 10.1002/adma.201904845
                                                                  doi: 10.1016/j.compstruct.2020.113053
            85.  Sharma S, Gupta V, Mudgal D, Srivastava V. Predicting
               biomechanical properties  of  additively manufactured   96.  Zhang Z, Krushynska AO. Programmable shape-morphing
               polydopamine coated poly lactic acid bone plates using deep   of rose-shaped mechanical metamaterials.  APL Mater.
               learning. Eng Appl Artif Intell. 2023;124:106587.  2022;10(8):080701.
               doi: 10.1016/j.engappai.2023.106587                doi: 10.1063/5.0099323
            86.  Nasrin T, Pourali M, Pourkamali-Anaraki F, Peterson AM.   97.  Chen Y, Wang L. Periodic co-continuous acoustic
               Active learning for prediction of tensile properties   metamaterials with overlapping locally resonant and Bragg
               for material extrusion additive manufacturing.  Sci   band gaps. Appl Phys Lett. 2014;105(19):191907.
               Rep. 2023;13(1):11460.                             doi: 10.1063/1.4902129
               doi: 10.1038/s41598-023-38527-6                 98.  Belei C, Pommer R, Amancio-Filho ST. Optimization of
                                                                  additive manufacturing for the production of short carbon
            87.  Veeman D, Sudharsan S, Surendhar GJ, Shanmugam R,
               Guo L. Machine learning model for predicting the hardness   fiber-reinforced polyamide/Ti-6Al-4V hybrid parts. Mater
               of additively manufactured acrylonitrile butadiene styrene.   Des. 2022;219:110776.
               Mater Today Commun. 2023;35:106147.                doi: 10.1016/j.matdes.2022.110776


            Volume 2 Issue 2 (2025)                         25                        doi: 10.36922/IJAMD025100007
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