Page 21 - IJB-8-1
P. 21

Sing
           42.  Wang Q, Han C, Choma T, et al., 2017. Effect of Nb Content      https://doi.org/10.1016/j.scriptamat.2018.05.010
               on Microstructure,  Property and  In Vitro Apatite-forming   52.  Li Y, Ding Y, Munir K, et al., 2019. Novel β-Ti35Zr28Nb
               Capability  of  Ti-Nb Alloys Fabricated  via  Selective  Laser   Alloy Scaffolds Manufactured Using Selective Laser Melting
               Melting. Mater Des, 126:268–77.                     for Bone Implant Applications. Acta Biomater, 87:273–84.
               https://doi.org/10.1016/j.matdes.2017.04.026        https://doi.org/10.1016/j.actbio.2019.01.051
           43.  Zhao D, Han C, Li J, et al., 2020. In Situ Fabrication of a   53.  Liu  YJ, Zhang  JS, Liu  XC,  et  al., 2021. Non-layer-wise
               Titanium-niobium  Alloy  with  Tailored  Microstructures,   Fracture  and  Deformation  Mechanism  in  Beta  Titanium
               Enhanced  Mechanical  Properties and  Biocompatibility   Cubic  Lattice  Structure  Manufactured by Selective  Laser
               by  Using  Selective  Laser  Melting.  Mater  Sci  Eng  C,   Melting. Mater Sci Eng A, 822:141696.
               2020:110784.                                        https://doi.org/10.1016/j.msea.2021.141696
               https://doi.org/10.1016/j.msec.2020.110784      54.  Qiu  C,  Liu  Q,  Ding  R,  2021.  Significant  Enhancement  in
           44.  Surmeneva MA, Koptyug A, Khrapov D, et al., 2020. In Situ   Yield  Strength  for a  Metastable  Beta  Titanium  Alloy  by
               Synthesis of a Binary Ti-10at% Nb Alloy by Electron Beam   Selective Laser Melting. Mater Sci Eng A, 816:141291.
               Melting Using a Mixture of Elemental Niobium and Titanium      https://doi.org/10.1016/j.msea.2021.141291
               Powders. J Mater Proc Technol, 282:116646.      55.  Liu YJ, Wang HL, Li SJ, et al., 2017. Compressive and Fatigue
               https://doi.org/10.1016/j.jmatprotec.2020.116646    Behavior of Beta-type Titanium Porous Structures Fabricated
           45.  Mosallanejad  MH,  Niroumand B,  Aversa  A,  et al., 2021.   by Electron Beam Melting. Acta Mater, 126:58–66.
               In-Situ  Alloying  in Laser-based  Additive  Manufacturing      https://doi.org/10.1016/j.actamat.2016.12.052
               Processes: A Critical Review. J Alloys Comp, 872:159567.  56.  Goh GD, Sing SL, Yeong WY, 2020. A Review on Machine
               https://doi.org/10.1016/j.jallcom.2021.159567       Learning  in  3D Printing:  Applications,  Potential,  and
           46.  Sing SL,  Wiria FE,  Yeong  WY, 2018. Selective  Laser   Challenges. Artif Intell Rev, 54:63–94.
               Melting  of Lattice Structures:  A  Statistical  Approach  to      https://doi.org/10.1007/s10462-020-09876-9
               Manufacturability and Mechanical Behavior. Robot Comput   57.  Özel T, Altay A, Kaftanoğlu B, et al., 2020. Focus Variation
               Integr Manuf, 49:170–80.                            Measurement and Prediction of Surface Texture Parameters
               https://doi.org/10.1016/j.rcim.2017.06.006          Using  Machine Learning in Laser Powder  Bed Fusion.
           47.  Sing SL, Wiria FE, Yeong WY, 2018. Selective Laser Melting   J Manuf Sci Eng, 12:011008.
               of  Titanium Alloy  with  50 wt%  Tantalum:  Effect  of  Laser      https://doi.org/10.1115/1.4045415
               Process Parameters on Part Quality. Int J Refract Metals Hard   58.  Kwon O, Kim HG, Ham MJ, et al., 2020. A Deep Neural
               Mater, 77:120–7.                                    Network  for  Classification  of  Melt-pool  Images  in  Metal
               https://doi.org/10.1016/j.ijrmhm.2018.08.006        Additive Manufacturing. J Intell Manuf, 31:375–86.
           48.  Yang Y, Wang G, Liang H, et al., 2019. Additive Manufacturing      https://doi.org/10.1007/s10845-018-1451-6
               of Bone Scaffolds. Int J Bioprint, 5:148.       59.  Kunkel MH, Gebhardt A, Mpofu K,  et al., 2019. Quality
               https://doi.org/10.18063/IJB.v5i1.148               Assurance in Metal Powder Bed Fusion Via Deep-learning-
           49.  Hafeez N, Liu J, Wang L, et al., 2020. Superelastic Response   Based Image Classification. Rapid Prototyp J, 26:259–66.
               of Low-modulus Porous Beta-type Ti-35Nb-2Ta-3Zr Alloy      https://doi.org/10.1108/RPJ-03-2019-0066
               Fabricated by Laser Powder Bed Fusion.  Addit Manuf,   60.  Shin DS, Lee CH, Kuhn U, et al., 2021. Optimizing Laser
               34:101264.                                          Powder  Bed  Fusion  of  Ti-5Al-5V-5Mo-3Cr  by  Artificial
               https://doi.org/10.1016/j.addma.2020.101264         Intelligence. J Alloys Comp, 862:158018.
           50.  Liu YJ, Li SJ, Wang HL, et al., 2016. Microstructure, Defects      https://doi.org/10.1016/j.jallcom.2020.158018
               and Mechanical  Behavior  of Beta-type  Titanium  Porous   61.  Meng L, McWilliams B, Jarosinski W, et al., 2020. Machine
               Structures Manufactured  by Electron Beam  Melting  and   Learning  in  Additive Manufacturing:  A  Review.  JOM,
               Selective Laser Melting. Acta Mater, 113:56–67.     72:2363–77.
               https://doi.org/10.1016/j.actamat.2016.04.029       https://doi.org/10.1007/s11837-020-04155-y
           51.  Liu  YJ, Li SJ, Zhang LC,  et al., 2018. Early  Plastic   62.  Qi X, Chen G, Li Y, et al., 2019. Applying Neural-Network-
               Deformation  Behaviour  and  Energy  Absorption in  Porous   Based Machine Learning to  Additive Manufacturing:
               β-type  Biomedical  Titanium  Produced by Selective  Laser   Current Applications,  Challenges,  and Future Perspectives.
               Melting. Script Mater, 153:99–103.                  Engineering. 5:721–9.

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