An integrated macroscopic model for simulating SLM and milling processes

Production Process
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Abstract

Due to their flexibility to also build up highly complex geometries, Additive Manufacturing (AM) processes are increasingly applied. Although near net-shape components can be manufactured using, for example, the Selective Laser Melting (SLM) process, the required surface quality can often not be achieved. In order to manufacture contact areas or functional surfaces, subsequent machining processes can be used to achieve the required accuracy in shape and dimension as well as the desired surface quality. In order to reduce the experimental effort during process design and optimization, simulation systems that are able to efficiently model both processes are required. In this paper, an empirical geometry-based model for SLM and milling processes will be presented. Due to the usage of an empirical model, based on the analysis of a set of reference structures, the simulation of macroscopic geometries can be achieved and used in subsequent milling simulations. Furthermore, an experimental validation of the combination of the two simulation models will be presented.

Keywords

Modeling Milling Selective laser melting 

Notes

Acknowledgements

The authors would like to thank K. Geenen, A. Röttger and W. Theisen from the Chair of Materials Technology (LWT, RUB) for providing the specimens for the slot milling tests and the SLM Solutions Group AG for manufacturing the test workpiece.

References

  1. 1.
    Uhlmann E, Kersting R, Klein TB, Cruz MF, Borille AV (2015) Additive manufacturing of titanium alloy for aircraft components. Procedia CIRP 35:55–60CrossRefGoogle Scholar
  2. 2.
    Thompson MK, Moroni G, Vaneker T, Fadel G, Campbell RI, Gibson I, Bernard A, Schulz J, Graf P, Ahuja B et al (2016) Design for additive manufacturing: trends, opportunities, considerations, and constraints. CIRP Ann Manuf Technol 65(2):737–760CrossRefGoogle Scholar
  3. 3.
    Strano G, Hao L, Everson RM, Evans KE (2013) Surface roughness analysis, modelling and prediction in selective laser melting. J Mater Process Technol 213(4):589–597CrossRefGoogle Scholar
  4. 4.
    Levy GN, Schindel R, Kruth J-P (2003) Rapid manufacturing and rapid tooling with layer manufacturing (lm) technologies, state of the art and future perspectives. CIRP Ann Manuf Technol 52(2):589–609CrossRefGoogle Scholar
  5. 5.
    Yasa E, Poyraz O, Solakoglu EU, Akbulut G, Oren S (2016) A study on the stair stepping effect in direct metal laser sintering of a nickel-based superalloy. Procedia CIRP 45:175–178CrossRefGoogle Scholar
  6. 6.
    Markl M, Körner C (2016) Multiscale modeling of powder bed-based additive manufacturing. Annu Rev Mater Res 46:93–123CrossRefGoogle Scholar
  7. 7.
    Kruth J-P, Levy G, Klocke F, Childs T (2007) Consolidation phenomena in laser and powder-bed based layered manufacturing. CIRP Ann Manuf Technol 56(2):730–759CrossRefGoogle Scholar
  8. 8.
    Rombouts M, Kruth J-P, Froyen L, Mercelis P (2006) Fundamentals of selective laser melting of alloyed steel powders. CIRP Ann Manuf Technol 55(1):187–192CrossRefGoogle Scholar
  9. 9.
    Liu Y, Yang Y, Wang D (2017) Investigation into the shrinkage in z-direction of components manufactured by selective laser melting (slm). Int J Adv Manuf Technol 90(9–12):2913–2923CrossRefGoogle Scholar
  10. 10.
    Craeghs T, Clijsters S, Kruth J-P, Bechmann F, Ebert M-C (2012) Detection of process failures in layerwise laser melting with optical process monitoring. Phys Procedia 39:753–759CrossRefGoogle Scholar
  11. 11.
    Zaeh MF, Branner G (2010) Investigations on residual stresses and deformations in selective laser melting. Prod Eng 4(1):35–45CrossRefGoogle Scholar
  12. 12.
    Riedlbauer D, Steinmann P, Mergheim J (2015) Thermomechanical simulation of the selective laser melting process for pa12 including volumetric shrinkage. In: AIP conference proceedings, vol 1664, pp 160005. AIP PublishingGoogle Scholar
  13. 13.
    San Sebastian M, Setien I, Mancisidor AM, Echeverria A (2017) Slm (near)-net-shape part design optimization based on numerical prediction of process induced distortions. In: TMS 2017 146th annual meeting & exhibition supplemental proceedings, pp 117–126. SpringerGoogle Scholar
  14. 14.
    Brinksmeier E, Levy G, Meyer D, Spierings A (2010) Surface integrity of selective-laser-melted components. CIRP Ann Manuf Technol 59(1):601–606CrossRefGoogle Scholar
  15. 15.
    Ahmed N, Abdo BM, Darwish S, Moiduddin K, Pervaiz S, Alahmari AM, Naveed M (2017) Electron beam melting of titanium alloy and surface finish improvement through rotary ultrasonic machining. Int J Adv Manuf Technol 92(9–12):3349–3361CrossRefGoogle Scholar
  16. 16.
    Salonitis K, D’Alvise L, Schoinochoritis B, Chantzis D (2016) Additive manufacturing and post-processing simulation: laser cladding followed by high speed machining. Int J Adv Manuf Technol 85(9–12):2401–2411CrossRefGoogle Scholar
  17. 17.
    Flynn JM, Shokrani A, Newman ST, Dhokia V (2016) Hybrid additive and subtractive machine tools-research and industrial developments. Int J Mach Tools Manuf 101:79–101CrossRefGoogle Scholar
  18. 18.
    Milton S, Morandeau A, Chalon F, Leroy R (2016) Influence of finish machining on the surface integrity of ti6al4v produced by selective laser melting. Procedia CIRP 45:127–130CrossRefGoogle Scholar
  19. 19.
    Montevecchi F, Grossi N, Takagi H, Scippa A, Sasahara H, Campatelli G (2016) Cutting forces analysis in additive manufactured aisi h13 alloy. Procedia CIRP 46:476–479CrossRefGoogle Scholar
  20. 20.
    Fortunato A, Lulaj A, Melkote S, Liverani E, Ascari A, Umbrello D (2017) Milling of maraging steel components produced by selective laser melting. Int J Adv Manuf Technol 94(5–8):1895–1902Google Scholar
  21. 21.
    Oyelola O, Crawforth P, M’Saoubi R, Clare AT (2016) Machining of additively manufactured parts: implications for surface integrity. Procedia CIRP 45:119–122CrossRefGoogle Scholar
  22. 22.
    Altintas Y, Kersting P, Biermann D, Budak E, Denkena B, Lazoglu I (2014) Virtual process systems for part machining operations. CIRP Ann Manuf Technol 63(2):585–605CrossRefGoogle Scholar
  23. 23.
    Wiederkehr P, Siebrecht T (2016) Virtual machining: capabilities and challenges of process simulations in the aerospace industry. Procedia Manuf 6:80–87CrossRefGoogle Scholar
  24. 24.
    Röttger A, Geenen K, Windmann M, Binner F, Theisen W (2016) Comparison of microstructure and mechanical properties of 316l austenitic steel processed by selective laser melting with hot-isostatic pressed and cast material. Mater Sci Eng A 678:365–376CrossRefGoogle Scholar
  25. 25.
    Wits WW, Bruins R, Terpstra L, Huls RA, Geijselaers H (2016) Single scan vector prediction in selective laser melting. Addit Manuf 9:1–6CrossRefGoogle Scholar
  26. 26.
    Bridson R (2007) Fast poisson disk sampling in arbitrary dimensions. In: SIGGRAPH sketches, p 22Google Scholar
  27. 27.
    Peytavie A, Galin E, Grosjean J, Mérillou S (2009) Procedural generation of rock piles using aperiodic tiling. In: Computer graphics forum, vol 28, pp 1801–1809. Wiley Online LibraryGoogle Scholar
  28. 28.
    Wiederkehr T, Müller H (2013) Acquisition and optimization of three-dimensional spray footprint profiles for coating simulations. J Therm Spray Technol 22(6):1044–1052CrossRefGoogle Scholar
  29. 29.
    Heeling T, Cloots M, Wegener K (2017) Melt pool simulation for the evaluation of process parameters in selective laser melting. Addit Manuf 14:116–125CrossRefGoogle Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2018

Authors and Affiliations

  1. 1.Virtual MachiningTU Dortmund UniversityDortmundGermany

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