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The Effect of Geometry on Local Processing State in Additively Manufactured Ti-6Al-4V Lattices

Abstract

Laser-based powder bed fusion is a thermal metal additive manufacturing process suitable for the fabrication of complex structures. Laser-based powder bed fusion induces large localized thermal gradients and cooling rates, which can produce significant variation in mechanical properties by affecting the underlying microstructure and porosity. This research describes how thermal imaging data can be analyzed to establish correlations for geometric factors such as strut diameter, inclination angle, and lattice location with thermal field variables. This research develops a methodology to analyze full-field temporal and spatial data across entire printed objects and systematically classify those data based on local and far-field geometry characteristics. This methodology is applied to generate an experimental time–temperature dataset for: (1) evaluation of the interactions of specific local geometric features and the local processing characteristics, (2) evaluation and calibration of proposed thermal models and (3) providing a methodology to validate other qualitative but higher spatial and temporal resolution in-process monitoring approaches.

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References

  1. 1.

    Berumen S, Bechmann F, Lindner S, Kruth J-P, Craeghs T (2010) Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies. Phys Procedia 5:617–622

    Article  Google Scholar 

  2. 2.

    Bidare P, Bitharas I, Ward RM, Attallah MM, Moore AJ (2018) Fluid and particle dynamics in laser powder bed fusion. Acta Mater 142:107–120

    CAS  Article  Google Scholar 

  3. 3.

    Buchbinder D, Meiners W, Pirch N, Wissenbach K, Schrage J (2014) Investigation on reducing distortion by preheating during manufacture of aluminum components using selective laser melting. J Laser Appl 26(1):012004

    Article  Google Scholar 

  4. 4.

    Chen H, Gu D, Xiong J, Xia M (2017) Improving additive manufacturing processability of hard-to-process overhanging structure by selective laser melting. J Mater Process Technol 250:99–108

    Article  Google Scholar 

  5. 5.

    Craeghs T, Clijsters S, Yasa E, Bechmann F, Berumen S, Kruth J-P (2011) Determination of geometrical factors in Layerwise laser melting using optical process monitoring. Opt Lasers Eng 49(12):1440–1446

    Article  Google Scholar 

  6. 6.

    Criales LE, Arısoy YM, Lane B, Moylan S, Donmez A, Özel T (2017) Laser powder bed fusion of nickel alloy 625: experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. Int J Mach Tools Manuf 121:22–36

    Article  Google Scholar 

  7. 7.

    Downing D (2020) Thermal field data set for additively manufactured lattices and inclined cylinders using selective laser melting. https://figshare.com/s/2527c0b54ed0bddc123a

  8. 8.

    Everton SK, Hirsch M, Stravroulakis P, Leach RK, Clare AT (2016) Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Mater Des 95:431–445

    CAS  Article  Google Scholar 

  9. 9.

    Grasso M, Colosimo BM (2017) Process defects and in-situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol 28(4):044005

    Article  Google Scholar 

  10. 10.

    Gusarov AV, Kovalev EP (2009) Model of thermal conductivity in powder beds. Phys Rev B 80(2):024202

    Article  Google Scholar 

  11. 11.

    Gusarov AV, Yadroitsev I, Bertrand P, Smurov I (2009) Model of Radiation and heat transfer in laser-powder interaction zone at selective laser melting. J Heat Transf 131(7):072101

    Article  Google Scholar 

  12. 12.

    Hooper PA (2018) Melt pool temperature and cooling rates in laser powder bed fusion. Addit Manuf 22:548–559

    CAS  Google Scholar 

  13. 13.

    IRCAM (2017) EQUUS 81k M/SM. EQUUS 81k M/SM Retrieved IRCAM, 2017, from http://www.ircam.eu/en/productnavigation/ir-cameras/equus/equus-81k-msm/.

  14. 14.

    Jamshidinia M, Kovacevic R (2015) The influence of heat accumulation on the surface roughness in powder-bed additive manufacturing. Surf Topogr Metrol Prop 3(1):014003

    Article  Google Scholar 

  15. 15.

    Krauss H, Zeugner T, Zaeh MF (2014) Layerwise monitoring of the selective laser melting process by thermography. Phys Procedia 56:64–71

    Article  Google Scholar 

  16. 16.

    Lane B, Whitenton E, Moylan S (2016) Multiple sensor detection of process phenomena in laser powder bed fusion. International Society for Optics and Photonics, USA

    Google Scholar 

  17. 17.

    Le-Quang T, Shevchik SA, Meylan B, Vakili-Farahani F, Olbinado MP, Rack A, Wasmer K (2018) Why is in situ quality control of laser keyhole welding a real challenge? Procedia CIRP 74:649–653

    Article  Google Scholar 

  18. 18.

    Leung CLA, Marussi S, Atwood RC, Towrie M, Withers PJ, Lee PD (2018) In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing. Nat Commun 9(1):1355

    Article  Google Scholar 

  19. 19.

    Li C, Liu ZY, Fang XY, Guo YB (2018) Residual Stress in Metal Additive Manufacturing. Procedia CIRP 71:348–353

    Article  Google Scholar 

  20. 20.

    McMillan M, Leary M, Emmelmann C, Brandt M (2018) SLM lattice thermal fields acquired by wide-field thermal camera. 10th Conference on Photonic Technologies–LANE 2018.

  21. 21.

    Peyre P, Aubry P, Fabbro R, Neveu R, Longuet A (2008) Analytical and numerical modelling of the direct metal deposition laser process. J Phys D: Appl Phys 41(2):025403

    Article  Google Scholar 

  22. 22.

    Repossini G, Laguzza V, Grasso M, Colosimo BM (2017) On the use of spatter signature for in-situ monitoring of Laser Powder Bed Fusion. Addit Manuf 16:35–48

    Google Scholar 

  23. 23.

    Schmeiser F, Krohmer E, Schell N, Uhlmann E, Reimers W (2020) Experimental observation of stress formation during selective laser melting using in situ X-ray diffraction. Addit Manuf 32:101028

    CAS  Google Scholar 

  24. 24.

    Scime L, Beuth J (2019) Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 25:151–165

    CAS  Google Scholar 

  25. 25.

    Tammas-Williams S, Withers PJ, Todd I, Prangnell PB (2017) The influence of porosity on fatigue crack initiation in additively manufactured titanium components. Sci Rep 7(1):7308

    CAS  Article  Google Scholar 

  26. 26.

    Tammas-Williams S, Zhao H, Léonard F, Derguti F, Todd I, Prangnell PB (2015) XCT analysis of the influence of melt strategies on defect population in Ti-6Al-4V components manufactured by Selective Electron Beam Melting. Mater Charact 102:47–61

    CAS  Article  Google Scholar 

  27. 27.

    Tapia G, Elwany A (2014) A review on process monitoring and control in metal-based additive manufacturing. J Manuf Sci Eng 136(6):060801–060801

    Article  Google Scholar 

  28. 28.

    Wang D, Yang Y, Yi Z, Su X (2013) Research on the fabricating quality optimization of the overhanging surface in SLM process. Int J Adv Manuf Technol 65(9–12):1471–1484

    Article  Google Scholar 

  29. 29.

    Wang F, Mao H, Zhang D, Zhao X, Shen Y (2008) Online study of cracks during laser cladding process based on acoustic emission technique and finite element analysis. Appl Surf Sci 255(5):3267–3275

    CAS  Article  Google Scholar 

  30. 30.

    Wasmer K, Le-Quang T, Meylan B, Vakili-Farahani F, Olbinado MP, Rack A, Shevchik SA (2018) Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach. Procedia CIRP 74:654–658

    Article  Google Scholar 

  31. 31.

    Yeung H, Lane B, Fox J (2019) Part geometry and conduction-based laser power control for powder bed fusion additive manufacturing. Addit Manuf 30:100844

    CAS  Google Scholar 

  32. 32.

    Yu G, Gu D, Dai D, Xia M, Ma C, Chang K (2016) Influence of processing parameters on laser penetration depth and melting/re-melting densification during selective laser melting of aluminum alloy. Appl Phys A 122(10):891

    Article  Google Scholar 

  33. 33.

    Zhang Y, Fuh JYH, Ye D, Hong GS (2019) In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches. Addit Manuf 25:263–274

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the facilities, and the scientific and technical assistance of Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT, as well as the use of facilities within the RMIT Advanced Manufacturing Precinct. Funding for this project is through the Australian Defense Science Technology’s Strategic Research Investment program.

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Correspondence to David Downing.

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Downing, D., Miller, J., McMillan, M. et al. The Effect of Geometry on Local Processing State in Additively Manufactured Ti-6Al-4V Lattices. Integr Mater Manuf Innov 10, 508–523 (2021). https://doi.org/10.1007/s40192-021-00225-4

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Keywords

  • Thermal fields
  • Cooling duration
  • Lattice structures
  • Additive manufacturing
  • In-situ monitoring