Lack-of-fusion porosity due to insufficient melting of the adjacent tracks and successive layers is a persistent problem in laser powder bed fusion (LPBF). A novel functional relation is proposed here following dimensional analysis for rapid estimation of the lack-of-fusion porosity fraction as a function of important process variables and alloy properties. The estimated values of the porosity fractions are found to be in fair agreement with the corresponding independent experimentally measured results for LPBF of five commonly used alloys for a wide range of process conditions. The proposed functional relation is used further to construct a set of process maps that can aid in the selection of important LPBF conditions to avoid lack-of-fusion porosity on the shop floor without doing any calculations.
SnowZ, NassarAR, ReutzelEW.Invited review article: review of the formation and impact of flaws in powder bed fusion additive manufacturing. Addit Manuf. 2020;36:101457. DOI: 10.1016/j.addma.2020.101457
2.
DebRoyT, WeiHL, ZubackJS, . Additive manufacturing of metallic components – process, structure and properties. Prog Mater Sci. 2018;92:112–224. DOI: 10.1016/j.pmatsci.2017.10.001
3.
Macías JgS, ElangeswaranC, ZhaoL, . Fatigue crack nucleation and growth in laser powder bed fusion AlSi10Mg under as built and post-treated conditions. Mater Des. 2021;210:110084. DOI: 10.1016/j.matdes.2021.110084
4.
KasperovichG, HaubrichJ, GussoneJ, . Correlation between porosity and processing parameters in TiAl6V4 produced by selective laser melting. Mater Des. 2016;105:160–170.
5.
HastieJC, KartalME, CarterLN, . Classifying shape of internal pores within AlSi10Mg alloy manufactured by laser powder bed fusion using 3D X-ray micro computed tomography: influence of processing parameters and heat treatment. Mater Charact. 2020;163:110225. DOI: 10.1016/j.matchar.2020.110225
6.
WangZ, LiuM.Dimensionless analysis on selective laser melting to predict porosity and track morphology. J Mater Process Technol. 2019;273:116238. DOI: 10.1016/j.jmatprotec.2019.05.019
7.
GanZ, KafkaOL, ParabN, . Universal scaling laws of keyhole stability and porosity in 3D printing of metals. Nat Commun. 2021;12:2379. DOI: 10.1038/s41467-021-22704-0
8.
RankouhiB, AgrawalAK, PfefferkornFE, . A dimensionless number for predicting universal processing parameter boundaries in metal powder bed additive manufacturing. Manuf Lett. 2021;27:13–17. DOI: 10.1016/j.mfglet.2020.12.002
9.
TangM, PistoriusPC, BeuthJL.Prediction of lack-of-fusion porosity for powder bed fusion. Addit Manuf. 2017;14:39–48. DOI: 10.1016/j.addma.2016.12.001
10.
BayatM, MohantyS, HattelJH.Multiphysics modelling of lack-of-fusion voids formation and evolution in IN718 made by multi-track/multi-layer L-PBF. Int J Heat Mass Transf. 2019;139:95–114. DOI: 10.1016/j.ijheatmasstransfer.2019.05.003
11.
Bruna-RossoC, DemirAG, PrevitaliB.Selective laser melting finite element modeling: validation with high-speed imaging and lack of fusion defects prediction. Mater Des. 2018;156:143–153. DOI: 10.1016/j.matdes.2018.06.037
12.
PanwisawasC, QiuC, AndersonMJ, . Mesoscale modelling of selective laser melting: thermal fluid dynamics and microstructural evolution. Comput Mater Sci. 2017;126:479–490. DOI: 10.1016/j.commatsci.2016.10.011
13.
QiuC, PanwisawasC, WardM, . On the role of melt flow into the surface structure and porosity development during selective laser melting. Acta Mater. 2015;96:72–79. DOI: 10.1016/j.actamat.2015.06.004
14.
PeiW, ZhengyingW, ZhenC, . Numerical simulation and parametric analysis of selective laser melting process of AlSi10Mg powder. Appl Phys A. 2017;123. DOI: 10.1007/s00339-017-1143-7
15.
WeiHL, MukherjeeT, ZhangW, . Mechanistic models for additive manufacturing of metallic components. Prog Mater Sci. 2020: 100703. DOI: 10.1016/j.pmatsci.2020.100703
16.
MukherjeeT, DebRoyT.Mitigation of lack of fusion defects in powder bed fusion additive manufacturing. J Manuf Process. 2018;36:442–449. DOI: 10.1016/j.jmapro.2018.10.028
17.
PromoppatumP, YaoS-C.Analytical evaluation of defect generation for selective laser melting of metals. Int J Adv Manuf Technol. 2019;103(1–4):1185–1198. DOI: 10.1007/s00170-019-03500-z
18.
WangW, NingJ, LiangSY.Prediction of lack-of-fusion porosity in laser powder-bed fusion considering boundary conditions and sensitivity to laser power absorption. Int J Adv Manuf Technol. 2021;112(1–2):61–70. DOI: 10.1007/s00170-020-06224-7
19.
ZagadeP, GauthamBP, DeA, . Analytical estimation of fusion zone dimensions and cooling rates in part scale laser powder bed fusion. Addit Manuf. 2021;46:102222. DOI: 10.1016/j.addma.2021.102222
20.
GongH, RafiK, GuH, . Analysis of defect generation in Ti–6Al–4 V parts made using powder bed fusion additive manufacturing processes. Addit Manuf. 2014;1–4:87–98. DOI: 10.1016/j.addma.2014.08.002
21.
ChoiJ-P, ShinG-H, YangS, . Densification and microstructural investigation of Inconel 718 parts fabricated by selective laser melting. Powder Technol. 2017;310:60–66. DOI: 10.1016/j.powtec.2017.01.030
22.
SimmonsJC, ChenX, AziziA, . Influence of processing and microstructure on the local and bulk thermal conductivity of selective laser melted 316L stainless steel. Addit Manuf. 2020;32:100996. DOI: 10.1016/j.addma.2019.100996
23.
VallejoDN, LucasC, AyersN, . Process optimization and microstructure analysis to understand laser powder bed fusion of 316L stainless steel. Metals. 2021;11(5):832. DOI: 10.3390/met11050832
24.
DilipJJS, ZhangS, TengC, . Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4 V alloy parts fabricated by selective laser melting. Prog Addit Manuf. 2017;2(3):157–167. DOI: 10.1007/s40964-017-0030-2
25.
ChoiJ-P, ShinG-H, BrochuM, . Densification behavior of 316L stainless steel parts fabricated by selective laser melting by variation in laser energy density. Mater Trans. 2016;57(11):1952–1959. DOI: 10.2320/matertrans.M2016284
26.
YakoutM, ElbestawiMA, VeldhuisSC.Density and mechanical properties in selective laser melting of invar 36 and stainless steel 316L. J Mater Process Technol. 2019;266:397–420. DOI: 10.1016/j.jmatprotec.2018.11.006
27.
HyerH, ZhouL, ParkS, . Understanding the laser powder bed fusion of AlSi10Mg alloy. Metallogr Microstruct Anal. 2020;9(4):484–502. DOI: 10.1007/s13632-020-00659-w
28.
NarvanM, Al-RubaieKS, ElbestawiM.Process-structure-property relationships of AISI H13 tool steel processed with selective laser melting. Materials. 2019;12(14):2284. DOI: 10.3390/ma12142284
29.
NarvanM, GhasemiA, FereiduniE, . Part deflection and residual stresses in laser powder bed fusion of H13 tool steel. Mater Des. 2021;204:109659. DOI: 10.1016/j.matdes.2021.109659
30.
BalbaaM, MekhielS, ElbestawiM, . On selective laser melting of Inconel 718: densification, surface roughness, and residual stresses. Mater Des. 2020;193:108818. DOI: 10.1016/j.matdes.2020.108818
31.
RaiR, ElmerJW, PalmerTA, . Heat transfer and fluid flow during keyhole mode laser welding of tantalum, Ti–6Al–4 V, 304L stainless steel and vanadium. J Phys Appl Phys. 2007;40(18):5753–5766. DOI: 10.1088/0022-3727/40/18/037
32.
MillsKC.Recommended values of thermophysical properties for selected commercial alloys. Cambridge: Woodhead Publishing; 2002.
33.
KabirIR, YinD, TamannaN, . Thermomechanical modelling of laser surface glazing for H13 tool steel. Appl Phys A. 2018;124(3):260. DOI: 10.1007/s00339-018-1671-9
34.
FreedmanDR, PisaniR, PurvesR.Statistics. 4th edNew York: W. W. Norton & Company; 2007.
35.
TaylorR.Interpretation of the correlation coefficient: a basic review. J Diagn Med Sonogr. 1990;6(1):35–39.
36.
JamnikarND, LiuS, BriceC, . In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing. J Manuf Process. 2022;79:803–814. DOI: 10.1016/j.jmapro.2022.05.013
37.
WenC, ZhangY, WangC, . Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109–117. DOI: 10.1016/j.actamat.2019.03.010
38.
DaiD, XuT, WeiX, . Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Comput Mater Sci. 2020;175:109618. DOI: 10.1016/j.commatsci.2020.109618
39.
GuzmánJ, NobreR, NunesER, . Laser powder bed fusion parameters to produce high-density Ti–53%Nb alloy using irregularly shaped powder from hydride-dehydride (HDH) process. J Mater Res Technol. 2021;10:1372–1381. DOI: 10.1016/j.jmrt.2020.12.084
40.
EschnerN, WeiserL, HäfnerB, . Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions. Addit Manuf. 2020;34:101324. DOI: 10.1016/j.addma.2020.101324
41.
KumarP, FarahJ, AkramJ, . Influence of laser processing parameters on porosity in Inconel 718 during additive manufacturing. Int J Adv Manuf Technol. 2019;103(1–4):1497–1507. DOI: 10.1007/s00170-019-03655-9
42.
VéleF, AckermannM, BittnerV, . Influence of selective laser melting technology process parameters on porosity and hardness of AISI H13 tool steel: statistical approach. Materials (Basel). 2021;14(20):6052. DOI: 10.3390/ma1420605
