Page 47 - IJAMD-1-1
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International Journal of AI
            for Material and Design                                                ML for quality improvement in L-PBF



               2023;1274:012010.                               41.  Bland S, Aboulkhair NT. Reducing porosity in additive
                                                                  manufacturing. Metal Powder Report. 2015;70(2):79-81.
               doi: 10.1088/1757-899X/1274/1/012010
                                                                  doi: 10.1016/j.mprp.2015.01.005
            31.  Liu  Y,  Sing  SL. Review  on the use of  machine  learning
               techniques to optimize the processing of copper alloys   42.  Gao B, Zhao H, Peng L, Sun Z. A review of research progress
               in additive manufacturing. In:  Asian Society for Precision   in  selective  laser  melting  (SLM).  Micromachines  (Basel).
               Engineering and Nanotechnology (ASPEN 2022). Singapore:   2023;14:57.
               Research Publishing Services; 2022. p. 107-109.
                                                                  doi: 10.3390/mi14010057
               doi: 10.3850/978-981-18-6021-8_OR-01-0297.html
                                                               43.  Solberg K, Guan S, Razavi N,  et al. Fatigue of additively
            32.  Sabuj MR, Afshari SS, Liang X. Selective LASER melting part   manufactured 316L stainless steel: The influence of porosity
               quality prediction and energy consumption optimization.   and surface roughness.  Fatigue Fract Eng Mater Struct.
               Meas Sci Technol. 2023;34(7):075902.               2019;42:2043-52.
               doi: 10.1088/1361-6501/acc5a4                      doi: 10.1111/ffe.13004
            33.  Uy M,  Telford JK.  Optimization by Design of Experiment   44.  Tapia G, Elwany AH, Sang H. Prediction of porosity in
               Techniques. Big Sky, MT, USA, IEEE Aerospace Conference   metal-based additive manufacturing using spatial Gaussian
               2009, p1-10.                                       process models. Addit. Manuf. 2016;12:282-290.
               doi: 10.1109/AERO.2009.4839625                     doi: 10.1016/j.addma.2016.04.002
            34.  Goh GD, Huang X, Huang S, Thong JLJ, Seah JJ, Yeong WY.   45.  Imani F, Gaikwad A, Montazeri M, Rao P, Yang H, Reutzel E.
               Data imputation strategies for process optimization of laser   Process mapping and in-process monitoring of porosity in
               powder bed fusion of Ti6Al4V using machine learning.   laser  powder  bed  fusion  using  layerwise  optical  imaging.
               MSAM. 2023;2(1):50.                                ASME J Manuf Sci Eng. 2018;140:101009.
               doi: 10.36922/msam.50                              doi: 10.1115/1.4039501

            35.  Wang  C,  Tan  XP,  Tor  SB,  Lim  CS.  Machine  learning  in   46.  Hanzl H, Zetek M, Baka T, Kroupa T. The influence of
               additive manufacturing: State-of-the-art and perspectives.   processing parameters on the mechanical properties of SLM
               Addit Manuf. 2020;36:101538.                       parts. Proc Eng. 2015;100:1405-1413.
               doi: 10.1016/j.addma.2020.101538                   doi: 10.1016/j.proeng.2015.01.456
            36.  Forien  J, Calta  N, DePond P,  Guss  G, Roehling T,   47.  Maitra V, Shi J. Predictability assessment of as-built hardness
               Matthews M. Detecting keyhole porosity and monitoring   of Ti-6Al-4V alloy fabricated via laser powder bed fusion.
               process signatures in additive manufacturing: An  in situ   Manufact Lett. 2023;35:785-796.
               pyrometry and ex situ X-ray radiography correlation. Addit
               Manuf. 2019;35.                                    doi: 10.1016/j.mfglet.2022.12.001
               doi: 10.1016/j.addma.2020.101336                48.  Ravichander BB, Rahimzadeh A, Farhang B, Moghaddam NS,
                                                                  Amerinatanzi  A,  Mehrpouya  M.  A  prediction  model  for
            37.  Akbari P, Ogoke F, Kao NY, et al. MeltpoolNet: Melt pool   additive manufacturing of inconel 718 superalloy. Appl Sci.
               characteristic prediction in metal additive manufacturing   2021;11:8010.
               using machine learning. Addit Manuf. 2022;55:102817.
                                                                  doi: 10.3390/app11188010
               doi: 10.1016/j.addma.2021.102817
                                                               49.  Zhang T, Zhou X, Zhang P, et al. Hardness prediction of laser
            38.  Lee S, Peng J, Shin D, Choi YS. Data analytics approach for   powder bed fusion product based on melt pool radiation
               melt-pool geometries in metal additive manufacturing. Sci   intensity. Materials (Basel). 2022;15(13):4674.
               Technol Adv Mater. 2019;20(1):972-978.
                                                                  doi: 10.3390/ma15134674
               doi: 10.1080/14686996.2019.1669114
                                                               50.  Fang Q, Tan Z, Li H, et al. In-situ capture of melt pool signature
            39.  Yang Z, Lu Y, Yeung H, Krishnamurty S. From scan strategy   in selective laser melting using U-Net-based convolutional
               to melt pool prediction: A  Neighboring-effect modeling   neural network. J Manuf Process. 2021;68:347-355.
               method. ASME J Comput Inf Sci Eng. 2020;20(5):051001.
                                                                  doi: 10.1016/j.jmapro.2021.05.052
               doi: 10.1115/1.4047926
                                                               51.  Taherkhani K, Eischer C, Toyserkani E. An unsupervised
            40.  Saunders R, Rawlings A, Birnbaum A,  et al. Additive   machine learning algorithm for in-situ defect-detection in
               manufacturing melt pool prediction and classification via
               multifidelity Gaussian process surrogates.  Integr  Mater   laser powder-bed fusion. J Manuf Process. 2022;81:476-489.
               Manuf Innov. 2022;11:497-515.                      doi: 10.1016/j.jmapro.2022.06.074
               doi: 10.1007/s40192-022-00204-3                 52.  Huang DJ, Li H. A machine learning guided investigation


            Volume 1 Issue 1 (2024)                         41                      https://doi.org/10.36922/ijamd.2301
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