Page 32 - IJAMD-2-2
P. 32

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


            99.  Khan ZN, Albalawi HI, Valle-Pérez AU,  et al. From 3D      doi: 10.1088/1758-5090/ad716e
               printed molds to bioprinted scaffolds: A  hybrid material   107. Qiao Q, Zhang X, Yan Z, et al. The use of machine learning
               extrusion and vat polymerization bioprinting approach for   to predict the effects of cryoprotective agents on the GelMA-
               soft matter constructs. Mater Sci Addit Manuf. 2022;1(1):7.  based bioinks used in extrusion cryobioprinting.  Bio-Des
               doi: 10.18063/msam.v1i1.7                          Manuf. 2023;6(4):464-477.
            100. Magennis EP, Hook AL, Davies MC, Alexander C,      doi: 10.1007/s42242-023-00244-4
               Williams P, Alexander MR. Engineering serendipity: High-  108. Ruberu K, Senadeera M, Rana S, et al. Coupling machine
               throughput discovery of materials that resist bacterial   learning with 3D bioprinting to fast track optimisation of
               attachment. Acta Biomater. 2016;34:84-92.          extrusion printing. Appl Mater Today. 2021;22:100914.
               doi: 10.1016/j.actbio.2015.11.008                  doi: 10.1016/j.apmt.2020.100914
            101. Groll J, Burdick JA, Cho DW, et al. A definition of bioinks   109. Hashemi A, Ezati M, Zumberg I,  et al. Characterization
               and their distinction from biomaterial inks. Biofabrication.   and optimization of a biomaterial ink aided by machine
               2018;11(1):013001.                                 learning-assisted parameter suggestion.  Mater Today
                                                                  Commun. 2024;40:109777.
               doi: 10.1088/1758-5090/aaec52
                                                                  doi: 10.1016/j.mtcomm.2024.109777
            102. Chimene D, Lennox KK, Kaunas RR, Gaharwar AK.
               Advanced  bioinks  for  3D  printing:  A  materials  science   110. Huang X, Ng WL, Yeong WY. Predicting the number
               perspective. Ann Biomed Eng. 2016;44(6):2090-2102.  of printed cells during inkjet-based bioprinting process
                                                                  based on droplet velocity profile using machine learning
               doi: 10.1007/s10439-016-1638-y
                                                                  approaches. J Intell Manuf. 2024;35(5):2349-2364.
            103. Zhang Z, Jin Y, Yin J, et al. Evaluation of bioink printability for      doi: 10.1007/s10845-023-02167-4
               bioprinting applications. Appl Phys Rev. 2018;5(4):041304.
                                                               111. Huang X, Wong YX, Goh GL, Gao X, Lee JM, Yeong WY.
               doi: 10.1063/1.5053979                             Machine learning-driven prediction of gel fraction in
            104. Qavi I, Halder S, Tan G. Optimization of printability of   conductive gelatin methacryloyl hydrogels. Int J AI Mater
               bioinks with multi-response optimization (MRO) and   Des. 2024;1(2):61-75.
               artificial neural networks (ANN). Prog Addit Manuf. 2024.     doi: 10.36922/ijamd.3807
               doi: 10.1007/s40964-024-00828-1                 112. Bonatti AF, Vozzi G, Chua CK, Maria CD. A deep learning
            105. Lee J, Oh SJ, An SH, Kim WD, Kim SH. Machine     quality control loop of the extrusion-based bioprinting
               learning-based design strategy for 3D printable bioink:   process. Int J Bioprinting. 2022;8(4):620.
               Elastic modulus and yield stress determine printability.      doi: 10.18063/ijb.v8i4.620
               Biofabrication. 2020;12(3):035018.
                                                               113. Chen  B,  Dong  J,  Ruelas  M,  et al.  Artificial  intelligence-
               doi: 10.1088/1758-5090/ab8707                      assisted  high-throughput  screening  of  printing  conditions
            106. Xu Y, Sarah R, Habib A, Liu Y, Khoda B. Constraint based   of hydrogel architectures for accelerated diabetic wound
               Bayesian optimization of bioink precursor: A  machine   healing. Adv Funct Mater. 2022;32(38):2201843.
               learning framework. Biofabrication. 2024;16(4):045031.     doi: 10.1002/adfm.202201843

























            Volume 2 Issue 2 (2025)                         26                        doi: 10.36922/IJAMD025100007
   27   28   29   30   31   32   33   34   35   36   37