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Optimization of time-variant laser power in a cladding process

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Abstract

This work presents an optimization algorithm for time-variant additive manufacturing process parameters. The algorithm uses a finite-element model of the AM process to determine a sequence of laser powers (i.e., laser power as a function of time) that will result in the least variation in molten pool temperature. The algorithm was applied to a single-layer DED process made up of 36 unidirectional passes. The build material was Inconel® 625. A 15.1 °K (23.5%) reduction in standard deviation of molten pool temperature was achieved, and the upward trend of molten pool temperatures toward the later stages of the process was considerably reduced. Cooling rate was reduced by up to 67 °K/s (10.8%) at certain locations, although these improvements appeared largely at the later stages of the process. Another model was developed to facilitate comparison between the optimization algorithm and in situ feedback control. In this model, laser power was allowed to change in 5% increments whenever molten pool temperature dropped below 105% or rose above 120% of Inconel® 625’s melting temperature. This simulated control model yielded a 26.05 °K (44.1%) reduction in standard deviation of molten pool temperature and was able to reduce cooling rates across the entire print geometry by up to 86 °K/s (11.4%). These results indicate that the optimization algorithm can be used to achieve improved results in DED processes without the need for in situ interference. The algorithm may yield greater benefits during longer build times. In situ feedback control, if available, is still expected to perform best.

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References

  1. Thompson SM, Bian L, Shamsaei N, Yadollahi A (2015) An overview of direct laser deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics. Addit Manuf 8:36–62. https://doi.org/10.1016/j.addma.2015.07.001

    Article  Google Scholar 

  2. Denlinger ER, Irwin J, Michaleris P (2018) Thermomechanical modeling of additive manufacturing. Butterworth-Heinemann, Oxford

    Google Scholar 

  3. Francois MM et al (2017) Modeling of additive manufacturing processes for metals: Challenges and opportunities. Curr Opin Solid State Mater Sci 21(4):198–206. https://doi.org/10.1016/j.cossms.2016.12.001

    Article  Google Scholar 

  4. Gibson I, Rosen D, Stucker B (2015) Additive manufacturing technologies: 3d printing, rapid prototyping, and direct digital manufacturing. Springer Science+Business Media, New York

    Book  Google Scholar 

  5. Nadammal N et al (2017) Effect of hatch length on the development of microstructure, texture and residual stresses in selective laser melted superalloy Inconel 718. Mater Des 134:139–150. https://doi.org/10.1016/j.matdes.2017.08.049

    Article  Google Scholar 

  6. Zhang Y et al (2018) Additive manufacturing of metallic materials: a review. J Mater Eng Perform 27(1):1–13. https://doi.org/10.1007/s11665-017-2747-y

    Article  Google Scholar 

  7. Shunmugavel M, Polishetty A, Littlefair G (2015) Microstructure and mechanical properties of wrought and sdditive manufactured Ti-6Al-4V cylindrical bars. Procedia Technol 20:231–236. https://doi.org/10.1016/j.protcy.2015.07.037

    Article  Google Scholar 

  8. Trosch T, Strößner J, Völkl R, Glatzel U (2016) Microstructure and mechanical properties of selective laser melted Inconel 718 compared to forging and casting. Mater Lett 164:428–431. https://doi.org/10.1016/j.matlet.2015.10.136

    Article  Google Scholar 

  9. Carroll BE, Palmer TA, Beese AM (2015) Anisotropic tensile behavior of Ti-6Al-4V components fabricated with directed energy deposition additive manufacturing. Acta Mater 87:309–320. https://doi.org/10.1016/j.actamat.2014.12.054

    Article  Google Scholar 

  10. Akerfeldt P, Antti M-L, Pederson R (2016) Influence of microstructure on mechanical properties of laser metal wire-deposited Ti-6Al-4V. Mater Sci Eng A 674:428–437. https://doi.org/10.1016/j.msea.2016.07.038

    Article  Google Scholar 

  11. Zhu Y, Tian X, Li J, Wang H (2015) The anisotropy of laser melting deposition additive manufacturing Ti-6.5Al-3.5Mo-1.5Zr-0.3Si titanium alloy. Mater Des 67:538–542. https://doi.org/10.1016/j.matdes.2014.11.001

    Article  Google Scholar 

  12. Ni M et al (2017) Anisotropic tensile behavior of in situ precipitation strengthened Inconel 718 fabricated by additive manufacturing. Mater Sci Eng A 701:344–351. https://doi.org/10.1016/j.msea.2017.06.098

    Article  Google Scholar 

  13. Lavery NP, Brown SGR, Sienz J, Cherry J (2014) A review of computational modelling of additive layer manufacturing—multi-scale and multi-physics. Sustain Des Manuf, pp. 651–673. https://doi.org/10.13140/RG.2.1.3103.0884.

  14. Eltaggaz AA, Cloutier J, Deiab I (2021) Thermal post-processing of 4140 alloy steel parts fabricated by selective laser melting (SLM). In: Proc. Can. Soc. Mech. Eng. Int. Congr. https://doi.org/10.32393/csme.2021.35.

  15. Mower TM, Long MJ (2016) Mechanical behavior of additive manufactured, powder-bed laser-fused materials. Mater Sci Eng A 651:198–213. https://doi.org/10.1016/j.msea.2015.10.068

    Article  Google Scholar 

  16. Chen B, Mazumder J (2017) Role of process parameters during additive manufacturing by direct metal deposition of Inconel 718. Rapid Prototyp J 23(5):919–929. https://doi.org/10.1108/RPJ-05-2016-0071

    Article  Google Scholar 

  17. Amine T, Newkirk JW, Liou F (2014) An investigation of the effect of direct metal deposition parameters on the characteristics of the deposited layers. Case Stud Therm Eng 3:21–34. https://doi.org/10.1016/j.csite.2014.02.002

    Article  Google Scholar 

  18. Keller C, Mokhtari M, Vieille B, Briatta H, Bernard P (2021) Influence of a rescanning strategy with different laser powers on the microstructure and mechanical properties of Hastelloy X elaborated by powder bed fusion. Mater. Sci. Eng. A 803:140474. https://doi.org/10.1016/j.msea.2020.140474

    Article  Google Scholar 

  19. Dehoff RR, Kirka MM, List FA III, Unocic KA, Sames WJ (2015) Crystallographic texture engineering through novel melt strategies via electron beam melting: Inconel 718. Mater Sci Technol (UK) 31(8):939–944. https://doi.org/10.1179/1743284714Y.0000000697

    Article  Google Scholar 

  20. Selcuk C (2011) Laser metal deposition for powder metallurgy parts. Powder Metall 54(2):94–99. https://doi.org/10.1179/174329011X12977874589924

    Article  Google Scholar 

  21. Shamsaei N, Yadollahi A, Bian L, Thompson SM (2015) An overview of direct laser deposition for additive manufacturing; part II: mechanical behavior, process parameter optimization and control. Addit Manuf 8:12–35. https://doi.org/10.1016/j.addma.2015.07.002

    Article  Google Scholar 

  22. Wang L, Felicelli S, Gooroochurn Y, Wang PT, Horstemeyer MF (2008) Optimization of the LENS® process for steady molten pool size. Mater Sci Eng A 474(1–2):148–156. https://doi.org/10.1016/j.msea.2007.04.119

    Article  Google Scholar 

  23. Cline HE, Anthony TR (1977) Heat treating and melting material with a scanning laser or electron beam. J Appl Phys 48(9):3895–3900. https://doi.org/10.1063/1.324261

    Article  Google Scholar 

  24. Han L, Phatak KM, Liou FW (2004) Modeling of laser cladding with powder injection. Metall Mater Trans B 35(6):1139–1150. https://doi.org/10.1007/s11663-004-0070-0

    Article  Google Scholar 

  25. Hu D, Kovacevic R (2017) Modelling and measuring the thermal behaviour of the molten pool in closed-loop controlled laser-based additive manufacturing. Proc Inst Mech Eng Part B J Eng Manuf 217(4):441–452. https://doi.org/10.1243/095440503321628125

    Article  Google Scholar 

  26. Hua T, Jing C, Xin L, Fengying Z, Weidong H (2008) Research on molten pool temperature in the process of laser rapid forming. J Mater Process Technol 198(1–3):454–462. https://doi.org/10.1016/j.jmatprotec.2007.06.090

    Article  Google Scholar 

  27. Tang L, Landers RG (2010) Melt pool temperature control for laser metal deposition processes-part I: Online temperature control. J Manuf Sci Eng Trans ASME. https://doi.org/10.1115/1.4000882

    Article  Google Scholar 

  28. Tang L, Landers RG (2010) Melt pool temperature control for laser metal deposition processes-part II: layer-to-layer temperature control. J Manuf Sci Eng Trans ASME. https://doi.org/10.1115/14000883

    Article  Google Scholar 

  29. Garanger K, Khamvilai T, Feron E (2020) Additive manufacturing for high precision structural properties via feedback control. IEEE Trans Control Syst Technol 28(5):2053–2060

    Article  Google Scholar 

  30. Michaleris P (2014) Modeling metal deposition in heat transfer analyses of additive manufacturing processes. Finite Elem Anal Des 86:51–60. https://doi.org/10.1016/j.finel.2014.04.003

    Article  Google Scholar 

  31. Goldak J, Chakravarti A, Bibby M (1984) A new finite element model for welding heat sources. Metall Trans B 15(2):299–305. https://doi.org/10.1007/BF02667333

    Article  Google Scholar 

  32. Al Hamahmy MI, Deiab I (2020) Review and analysis of heat source models for additive manufacturing. Int J Adv Manuf Technol 106:1223–1238. https://doi.org/10.1007/s00170-019-04371-0

    Article  Google Scholar 

  33. Gouge MF, Heigel JC, Michaleris P, Palmer TA (2015) Modeling forced convection in the thermal simulation of laser cladding processes. Int J Adv Manuf Technol 79:307–320. https://doi.org/10.1007/s00170-015-6831-x

    Article  Google Scholar 

  34. Omega (1998) Non-contact temperature measurement, 2nd ed., vol. 1. Putman Publishing Company and OMEGA Press LLC

  35. Saad NR, Douglas WJM, Mujumdar AS (1977) Prediction of heat transfer under an axisymmetric laminar impinging jet. Ind Eng Chem Fundam 16(1):148–154. https://doi.org/10.1021/i160061a027

    Article  Google Scholar 

  36. Amaran S, Sahinidis NV, Sharda B, Bury SJ (2016) Simulation optimization: a review of algorithms and applications. Ann Oper Res 240(1):351–380. https://doi.org/10.1007/s10479-015-2019-x

    Article  MathSciNet  MATH  Google Scholar 

  37. Fu MC (ed) (2015) Handbook of simulation optimization. Springer Science+Business Media, New York

    MATH  Google Scholar 

Download references

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The authors acknowledge financial support from the National Science and Engineering Research Council (NSERC).

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Correspondence to Mohamed El Hamahmy.

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El Hamahmy, M., Deiab, I. Optimization of time-variant laser power in a cladding process. Prog Addit Manuf 7, 1155–1168 (2022). https://doi.org/10.1007/s40964-022-00290-x

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