Page 87 - MSAM-1-1

Materials Science in Additive Manufacturing A ML model for AM PSP of Ti64

Formal Analysis: Xi Gong, Dongrui Zeng measurement of spatial correlation functions in multiphase
solids. J Appl Phys, 45: 3159–3164.
Validation: Xi Gong
https://doi.org/10.1063/1.1663741
Visualization: Xi Gong, Willem Groeneveld-Meijer, Guha
Manogharan 11. Lu B, Torquato S, 1992, Lineal-path function for random
heterogeneous materials. Phys Rev A, 45: 922–929.
Writing – Original Draft: Xi Gong, Guha Manogharan
https://doi.org/10.1103/PhysRevA.45.922
Writing – Review and Editing: Willem Groeneveld- 12. Gan Z, Li H, Wolff SJ, et al., 2019, Data-driven microstructure
Meijer, Guha Manogharan
and microhardness design in additive manufacturing using
References a self-organizing map. Engineering, 5: 730–735.
https://doi.org/10.1016/j.eng.2019.03.014
1. Frazier WE, 2014, Metal additive manufacturing: A review.
J Mater Eng Perform, 23: 1917–1928. 13. Thijs L, Verhaeghe F, Craeghs T, et al., 2010, A study of the
microstructural evolution during selective laser melting of
https://doi.org/10.1007/s11665-014-0958-z
Ti-6Al-4V. Acta Mater, 58: 3303–3312.
2. Trelewicz JR, Halada GP, Donaldson OK, et al., 2016, https://doi.org/10.1016/j.actamat.2010.02.004
Microstructure and corrosion resistance of laser additively
manufactured 316L stainless steel. JOM, 68: 850–859. 14. Li GC, Li J, Tian XJ, et al., 2017, Microstructure and
properties of a novel titanium alloy Ti-6Al-2V-1.5Mo-
3. Bostanabad R, Bui AT, Xie W, et al., 2016, Stochastic 0.5Zr-0.3Si manufactured by laser additive manufacturing.
microstructure characterization and reconstruction via Mater Sci Eng A, 684: 233–238.
supervised learning. Acta Mater, 103: 89–102.
https://doi.org/10.1016/j.msea.2016.11.084
https://doi.org/10.1016/j.actamat.2015.09.044
15. Paulson NH, Priddy MW, McDowell DL, et al., 2019,
4. Kalidindi SR, 2015, Data science and cyberinfrastructure: Reduced-order microstructure-sensitive protocols to rank-
Critical enablers for accelerated development of hierarchical order the transition fatigue resistance of polycrystalline
materials. Int Mater Rev, 60: 150–168.
microstructures. Int J Fatigue, 119: 1–10.
https://doi.org/10.1179/1743280414Y.0000000043
https://doi.org/10.1016/j.ijfatigue.2018.09.011
5. Popova E, Rodgers TM, Gong X, et al., 2017, Process- 16. Fernandez-Zelaia P, Nguyen V, Zhang H, et al., 2019, The
structure linkages using a data science approach: Application effects of material anisotropy on secondary processing of
to simulated additive manufacturing data. Integr Mater additively manufactured CoCrMo. Addit Manuf, 29: 100764.
Manuf Innov, 6: 54–68.
https://doi.org/10.1016/j.addma.2019.06.015
https://doi.org/10.1007/s40192-017-0088-1
17. Yang Z, Yabansu YC, Al-Bahrani R, et al., 2018, Deep
6. Greitemeier D, Donne CD, Schoberth A, et al., 2015, learning approaches for mining structure-property linkages
Uncertainty of additive manufactured Ti-6Al-4V: in high contrast composites from simulation datasets.
Chemistry, microstructure and mechanical properties. Appl Comput Mater Sci, 151: 278–287.
Mech Mater, 807: 169–180.
https://doi.org/10.1016/j.commatsci.2018.05.014
https://doi.org/10.4028/www.scientific.net/amm.807.169
18. Manogharan G, Wysk R, Harrysson O, et al., 2015, AIMS-a
7. Markl M, Körner C, 2016, Multiscale modeling of powder metal additive-hybrid manufacturing system: System
bed-based additive manufacturing. Annu Rev Mater Res, architecture and attributes. Proc Manuf, 1: 273–286.
46: 93–123.
19. Zhang W, Qin P, Wang Z, et al., 2019, Superior wear
https://doi.org/10.1146/annurev-matsci-070115-032158
resistance in EBM-Processed TC4 alloy compared with SLM
8. Li J, Zhou X, Brochu M, et al., 2020, Solidification and forged samples. Materials (Basel), 12: 2–13.
microstructure simulation of Ti-6Al-4V in metal additive https://doi.org/10.3390/ma12050782
manufacturing: A review. Addit Manuf, 31: 100989.
20. Edwards P, Ramulu M, 2014, Fatigue performance
https://doi.org/10.1016/j.addma.2019.100989
evaluation of selective laser melted Ti-6Al-4V. Mater Sci Eng
9. Liu S, Shin YC, 2019, Additive manufacturing of Ti6Al4V A, 598: 327–337.
alloy: A review. Mater Des, 164: 107552.
https://doi.org/10.1016/j.msea.2014.01.041
https://doi.org/10.1016/j.matdes.2018.107552
21. Matouš K, Geers MG, Kouznetsova VG, et al., 2017, A review
10. Corson PB, 1974, Correlation functions for predicting of predictive nonlinear theories for multiscale modeling of
properties of heterogeneous materials. I. Experimental heterogeneous materials. J Comput Phys, 330: 192–220.

Volume 1 Issue 1 (2022) 15 https://doi.org/10.18063/msam.v1i1.6