Page 355 - IJB-9-4
P. 355

International Journal of Bioprinting                                      Bioprinting with machine learning



            Ethics approval and consent to participate         12.  Ng WL, Chan A, Ong YS, et al., 2020, Deep learning for
                                                                  fabrication and maturation of 3D bioprinted tissues and
            Not applicable.                                       organs. Virtual Phys Prototyp, 15(3):340–358.

            Consent for publication                               https://doi.org/10.1080/17452759.2020.1771741
                                                               13.  He H, Yang Y, Pan Y, 2019, Machine learning for continuous
            Not applicable.                                       liquid interface production: Printing speed modelling.
                                                                  J Manuf Syst, 50:236–246.
            Availability of data
                                                                  https://doi.org/10.1016/j.jmsy.2019.01.004
            Not applicable.
                                                               14.  Yu C, Jiang J, 2020, A perspective on using machine learning
            References                                            in 3D bioprinting. Int J Bioprint, 6(1):1–8.
                                                               15.  Freeman S, Calabro S, Williams R, et al., 2022, Bioink
            1.   Xiaoya Z, Liuchao J,Jingchao J, 2022, A survey of additive   formulation and machine learning-empowered bioprinting
               manufacturing reviews. MSAM, 1(4):21.              optimization. Front Bioeng Biotechnol, 10:913579.
            2.   Jiang J, 2020, A novel fabrication strategy for additive   16.  Jin Z, Zhang Z, Demir K, et al., 2020, Machine learning
               manufacturing processes. J Clean Prod, 272:122916.  for advanced additive manufacturing.  Matter, 3(5):
               https://doi.org/10.1016/j.jclepro.2020.122916      1541–1556.
            3.   Al-Kharusi G, Dunne NJ, Little S, et al., 2022, The role   https://doi.org/10.1016/j.matt.2020.08.023
               of machine learning and design of experiments in the   17.  Omairi A, Ismail Z H, 2021, Towards machine learning for
               advancement of biomaterial and tissue engineering research.   error compensation in additive manufacturing[J]. Appl Sci,
               Bioengineering, 9(10):561.                         11(5):2375.
            4.   Gholami P, Ahmadi-pajouh MA, Abolftahi N, et al., 2018,
               Segmentation and measurement of chronic wounds for   https://doi.org/10.3390/app11052375
               bioprinting. IEEE J Biomed Health, 22(4):1269–1277.  18.  Qin J, Hu F, Liu Y, et al., 2022, Research and application of
                                                                  machine learning for additive manufacturing. Addit Manuf,
               https://doi.org/10.1109/JBHI.2017.2743526
                                                                  52:102691.
            5.   Shin J, Lee Y, Li Z, et al., 2022, Optimized 3D bioprinting
               technology based on machine learning: A review of recent   https://doi.org/10.1016/j.addma.2022.102691
               trends and advances. Micromachines, 13(3):363.  19.  Jiang J, Xu X, Xiong Y, et al., 2020, A novel strategy for
            6.   Ravanbakhsh H, Karamzadeh V, Bao G, et al., 2021,   multi-part production in additive manufacturing. Int J Adv
               Emerging technologies in multi-material bioprinting.  Adv   Manuf Technol, 109(5):1237–1248.
               Mater, 33(49):2104730.                             https://doi.org/10.1007/s00170-020-05734-8
               https://doi.org/10.1002/adma.202104730
                                                               20.  Ko H, Witherell P, Lu Y, et al., 2021, Machine learning and
            7.   Malekpour A, Chen X, 2022, Printability and cell viability   knowledge graph based design rule construction for additive
               in extrusion-based bioprinting from experimental,   manufacturing. Addit Manuf, 37:101620.
               computational, and machine learning views.  J Funct
               Biomater, 13(2):40.                                https://doi.org/10.1016/j.addma.2020.101620
            8.   Ong CS, Yesantharao P, Huang CY,  et al., 2018, 3D   21.  Li Z, Zhang Z, Shi J, et al., 2019, Prediction of surface
               bioprinting using stem cells. Pediatr Res, 83(1):223–231.  roughness in extrusion-based additive manufacturing
                                                                  with machine learning.  Robot Cim-Int Manuf, 57:
               https://doi.org/10.1038/pr.2017.252                488–495.
            9.   Sklare SC, Richey WL, Vinson BT, et al., 2017, Directed self-  https://doi.org/10.1016/j.rcim.2019.01.004
               assembly software for single cell deposition. Int J Bioprint,
               3(2):100–108.                                   22.  Zhu Z, Anwer N, Huang Q, et al., 2018, Machine learning
                                                                  in  tolerancing  for additive  manufacturing.  CIRP Ann,
            10.  Datta  P,  Barui  A,  Wu  Y, et  al.,  2018,  Essential  steps  in   67(1):157–160.
               bioprinting: From pre- to post-bioprinting. Biotechnol Adv,
               36(5):1481–1504.                                   https://doi.org/10.1016/j.cirp.2018.04.119
               https://doi.org/10.1016/j.biotechadv.2018.06.003  23.  Jiang J, Xiong Y, Zhang Z, et al., 2022, Machine learning
            11.  Zolfagharian A, Denk M, Kouzani AZ, et al., 2020, Effects   integrated design for additive manufacturing. J Intell Manuf,
               of topology optimization in multimaterial 3D bioprinting of   33(4):1073–1086.
               soft actuators. Int J Bioprint, 6(2):50–60.        https://doi.org/10.1007/s10845-020-01715-6



            Volume 9 Issue 4 (2023)                        347                         https://doi.org/10.18063/ijb.739
   350   351   352   353   354   355   356   357   358   359   360