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Materials Science in Additive Manufacturing                           Data imputation strategies of PBF Ti64



               densities in the Selective Laser Melting technique. J Manuf   learning in 3D printing: Applications, potential, and
               Process, 35: 538–546.                              challenges. Artif Intell Rev, 54: 63–94.
               https://doi.org/10.1016/j.jmapro.2018.09.012       https://doi.org/10.1007/s10462-020-09876-9
            8.   Gong  H,  Rafi  K,  Starr  T,  et al.,  2013,  The  Effects  of   19.  Steiner S, Zeng Y, Young TM, et al., 2016, A study of missing
               Processing Parameters on Defect Regularity in Ti-6Al-4V   data imputation in predictive modeling of a wood-composite
               Parts Fabricated by Selective Laser Melting and Electron   manufacturing process. J Qual Technol, 48: 284–296.
               Beam Melting. In: Conference 24   Annual International      https://doi.org/10.1080/00224065.2016.11918167
                                         th
               Solid Freeform Fabrication Symposium.
                                                               20.  Wang Y, Li K, Gan S, et al., 2019, Missing data imputation
            9.   Kasperovich G, Haubrich J, Gussone J,  et al., 2016,   with OLS-based autoencoder for intelligent manufacturing.
               Correlation between porosity and processing parameters   IEEE Trans Ind Appl, 55: 7219–7229.
               in TiAl6V4 produced by selective laser melting. Mater Des,
               105: 160–170.                                      https://doi.org/10.1109/TIA.2019.2940585
               https://doi.org/10.1016/j.matdes.2016.05.070    21.  Andridge RR, Little RJ, 2010, A review of hot deck
                                                                  imputation for survey non‐response. Int Stat Rev, 78: 40–64.
            10.  Ali H, Ma L, Ghadbeigi H, et al., 2017, In-situ residual stress
               reduction, martensitic decomposition and mechanical      https://doi.org/10.1111/j.1751-5823.2010.00103.x
               properties enhancement through high temperature powder   22.  Jadhav A, Pramod D, Ramanathan K, 2019, Comparison
               bed pre-heating of Selective Laser Melted Ti6Al4V. Mater   of  performance  of  data  imputation  methods  for  numeric
               Sci Eng A, 695: 211–220.                           dataset. Appl Artif Intell, 33: 913–933.
            11.  Vilaro T, Colin C, Bartout JD, 2011, As-fabricated and heat-     https://doi.org/10.1080/08839514.2019.1637138
               treated microstructures of the Ti-6Al-4V alloy processed by
               selective laser melting. Metall Mater Trans A, 42: 3190–3199.   23.  Altman NS, 1992, An introduction to Kernel and nearest-
                                                                  neighbor nonparametric regression. Am Stat, 46: 175–185.
               https://doi.org/10.1007/s11661-011-0731-y
                                                                  https://doi.org/10.2307/2685209
            12.  Qiu C, Adkins NJ, Attallah MM, 2013, Microstructure and
               tensile properties of selectively laser-melted and of HIPed   24.  Imandoust SB, Bolandraftar M, 2013, Application of
               laser-melted Ti-6Al-4V. Mater Sci Eng A, 578: 230–239.   K-nearest neighbor (KNN) approach for predicting
                                                                  economic  events:  Theoretical  background.  Int J Eng Res
               https://doi.org/10.1016/j.msea.2013.04.099         Appl, 3: 605–610.
            13.  Xu Y, Zhang D, Guo Y, et al., 2020, Microstructural tailoring   25.  Wilson DR, Martinez TR, 2000, Reduction techniques
               of As-selective Laser melted Ti6Al4V alloy for high   for instance-based learning algorithms.  Mach Learn,
               mechanical properties. J Alloys Compd, 816: 152536.   38: 257–286.
               https://doi.org/10.1016/j.jallcom.2019.152536      https://doi.org/10.1023/A:1007626913721
            14.  Pal S, Gubeljak N, Hudak R, et al., 2019, Tensile   26.  sklearn.impute.KNNImputer-scikit-learn   0.23.2
               properties  of  selective  laser  melting  products  affected  by   documentation. Available from: https://scikit-learn.org/
               building orientation and energy density. Mater Sci Eng A,   stable/modules/generated/sklearn.impute.KNNImputer.
               743: 637–647.                                      html [Last accessed on 2020 Oct 05].
               https://doi.org/10.1016/j.msea.2018.11.130      27.  sklearn.metrics.pairwise.nan_euclidean_distances-scikit-
            15.  Sun J, Yang Y, Wang D, 2013, Parametric optimization of   learn 0.23.2 documentation. Available from: https://
               selective laser melting for forming Ti6Al4V samples by   scikit-learn.org/stable/modules/generated/sklearn.metrics.
               Taguchi method. Opt Laser Technol, 49: 118–124.    pairwise.nan_euclidean_distances.html  [Last  accessed  on
                                                                  2020 Oct 05].
               https://doi.org/10.1016/j.optlastec.2012.12.002
                                                               28.  Van  Buuren  S, Groothuis-Oudshoorn  K,  2010,  Mice:
            16.  Bartolomeu F, Faria S, Pinto E, et al., 2016, Predictive models   Multivariate imputation by chained equations in R. J Stat
               for physical and mechanical properties of Ti6Al4V produced   Softw, 45: 1–67.
               by Selective Laser Melting. Mater Sci Eng A, 663: 181–192.
                                                                  https://doi.org/10.18637/jss.v045.i03
               https://doi.org/10.1016/j.msea.2016.03.113
                                                               29.  Azur MJ, Stuart EA, Frangakis C,  et  al., 2011, Multiple
            17.  Fotovvati B, Namdari N, Dehghanghadikolaei A, 2018,   imputation by chained equations: What is it and how does it
               Fatigue performance of selective laser melted Ti6Al4V   work? Int J Methods Psychiatr Res, 20: 40–49.
               components: State of the art. Mater Res Express, 6: 012002.
                                                                  https://doi.org/10.1002/mpr.329
               https://doi.org/10.1088/2053-1591/aae10e
                                                               30.  Rubin DB, 1987, Multiple Imputation for Nonresponse in
            18.  Goh GD, Sing SL, Yeong WY, 2020, A review on machine   Surveys  (Wiley  Series  in  Probability  and  Statistics).  John


            Volume 2 Issue 1 (2023)                         17                       https://doi.org/10.36922/msam.50
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