Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Gu DD, Meiners W, Wissenbach K, Poprawe R (2012) Laser additive manufacturing of metallic components: materials, processes and mechanisms. Int Mater Rev 57(3):133–164

    Article  Google Scholar 

  3. American Society of Testing Materials (2012) ASTM F2792 - 12a: standard terminology for additive manufacturing technologies. Standard, ASTM. [Online]. Available from http://www.astm.org/Standards/F2792.htm

  4. Wohlers TT, Wohlers Associates, Campbell RI, Caffrey T (2016) Wohlers Report 2016: 3D Printing and Additive Manufacturing State of the Industry: Annual Worldwide Progress Report. Wohlers Associates, USA. ISBN 9780991333226

  5. The Minerals Metals & Materials Society (TMS) (2015) Modeling across scales: a roadmapping study for connecting materials models and simulations across length and time scales. TMS, Warrendale. ISBN 9780692376065. www.tms.org/multiscalestudy

  6. Frazier WE (2010) Direct digital manufacturing of metallic components: vision and roadmap. Direct digital manufacturing of metallic components: affordable, durable, and structurally efficient airframes, at Solomons Island. Austin, pp 9–11

  7. National Institute of Standards and Technology (NIST) Measurement science roadmap for metal-based additive manufacturing, 2013 Online. Available from https://www.nist.gov/sites/default/files/documents/el/isd/NISTAdd_Mfg_Report_FINAL-2.pdf. Accessed 10 Jun 2015

  8. Bourell DL, Leu MC, Rosen DW (2009) Roadmap for additive manufacturing: identifying the future of freeform processing. The University of Texas at Austin, Austin

  9. King WE, Anderson AT, Ferencz RM, Hodge NE, Kamath C, Khairallah SA, Rubenchik AM (2015a) Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl Phys Rev 2(4):041304

    Article  Google Scholar 

  10. Khairallah SA, Anderson AT, Rubenchik A, King WE (2016) Laser powderbed fusion additive manufacturing: physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Mater 108:36–45

    Article  Google Scholar 

  11. Megahed M, Mindt H-W, N’Dri N, Duan H, Desmaison O (2016) Metal additive-manufacturing process and residual stress modeling. Integr Mater Manuf Innov 5(1):1–33

    Article  Google Scholar 

  12. Panwisawas C, Qiu C, Anderson MJ, Sovani Y, Turner RP, Attallah MM, Brooks JW, Basoalto HC (2017) Mesoscale modelling of selective laser melting: thermal fluid dynamics and microstructural evolution. Comput Mater Sci 126:479–490

    Article  Google Scholar 

  13. Markl M, Körner C (2016) Multi-scale modeling of powder-bed-based additive manufacturing. Annu Rev Mater Res 46:1–34

    Article  Google Scholar 

  14. Gürtler F-J, Karg M, Leitz K-H, Schmidt M (2013) Simulation of laser beam melting of steel powders using the three-dimensional volume of fluid method. Phys Procedia 41:881–886

    Article  Google Scholar 

  15. King WE, Barth HD, Castillo VM, Gallegos GF, Gibbs JW, Hahn DE, Kamath C, Rubenchik AM (2014) Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J Mater Process Technol 214(12):2915– 2925

    Article  Google Scholar 

  16. Kleijnen JPC (1975) A comment on blanning’s metamodel for sensitivity analysis: the regression metamodel in simulation. Interfaces 5(3):21–23

    Article  Google Scholar 

  17. King WE, Anderson AT, Ferencz RM, Hodge NE, Kamath C, Khairallah SA (2015b) Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory. Mater Sci Technol 31(8):957–968

    Article  Google Scholar 

  18. Dai Donghua, Dongdong Gu (2014) Thermal behavior and densification mechanism during selective laser melting of copper matrix composites: simulation and experiments. Mater Des 55: 482–491

    Article  Google Scholar 

  19. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge

    MATH  Google Scholar 

  20. Tapia G, Elwany AH, Sang H (2016) Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit Manuf 12:282–290

  21. Tapia G, Johnson L, Franco B, Karayagiz K, Ma J, Arroyave R, Karaman I, Elwany A (2017) Bayesian calibration and uncertainty quantification for a physics-based precipitation model of nickel-titanium shape-memory alloys. J Manuf Sci Eng 139(7): 071002

    Article  Google Scholar 

  22. Friedman J, Hastie T, Tibshirani Rt (2001) The elements of statistical learning, vol 1. Springer Series in Statistics, New York

    MATH  Google Scholar 

  23. Mao R, Zhu H, Zhang L, Chen A (2006) A new method to assist small data set neural network learning. In: Sixth international conference on intelligent systems design and applications, ISDA06, 2006, vol 1. IEEE, New York, pp 17–22

  24. O’Hagan A (2013) Polynomial chaos: a tutorial and critique from a statistician’s perspective. SIAM/ASA J Uncert Quantif 20:1– 20

    Google Scholar 

  25. Liu P u, Lusk MT (2002) Parametric links among monte carlo, phase-field, and sharp-interface models of interfacial motion. Phys Rev E 66(6):061603

    Article  Google Scholar 

  26. Büche D, Schraudolph NN, Koumoutsakos P (2005) Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Trans Appl Rev Syst Man Cybern Part C 35(2):183–194

    Article  Google Scholar 

  27. Christen A, Sansó B (2008) Advances in the design of Gaussian processes as surrogate models for computer experiments. Technical report, Tech. Report 5, University of California, Santa Cruz CA

  28. O’Hagan A (2006) Bayesian analysis of computer code outputs: a tutorial. Reliab Eng Syst Saf 91(10):1290–1300

    Article  Google Scholar 

  29. Conti S, Gosling JP, Oakley JE, O’Hagan A (2009) Gaussian process emulation of dynamic computer codes. Biometrika 96(3): 663–676

  30. Bastos LS, O’Hagan A (2009) Diagnostics for Gaussian process emulators. Technometrics 51(4):425–438

    Article  MathSciNet  Google Scholar 

  31. Gelfand AE, Diggle P, Guttorp P, Fuentes M (2010) Handbook of spatial statistics. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  32. Cressie NAC (1993) Statistics for spatial data. Wiley, New York

    MATH  Google Scholar 

  33. Higdon D, Gattiker J, Williams B, Rightley M (2008) Computer model calibration using high-dimensional output. J Amer Stat Assoc 103(482):570–583

  34. Higdon D, Kennedy M, Cavendish JC, Cafeo JA, Ryne RD (2004) Combining field data and computer simulations for calibration and prediction. SIAM J Sci Comput 26(2):448–466

    Article  MathSciNet  MATH  Google Scholar 

  35. Kamath C (2016) Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 1–19

  36. Tapia G, Elwany AH (2015) Prediction of porosity in SLM parts using a MARS statistical model and bayesian inference. In: Proceedings of the solid freeform fabrication symposium. Austin, pp 1205–1219

  37. Stein ML (2012) Interpolation of spatial data: some theory for Kriging, Springer Science & Business Media, New York

  38. Gong H, Gu H, Zeng K, Dilip JJS, Pal D, Stucker B, Christiansen D, Beuth J, Lewandowski JJ (2014) Melt pool characterization for selective laser melting of Ti-6Al-4V pre-alloyed powder. In: Proceedings of the solid freeform fabrication symposium. Austin, pp 256–267

  39. Thijs L, Verhaeghe F, Craeghs T, Van Humbeeck J, Kruth J-P (2010) A study of the microstructural evolution during selective laser melting of Ti-6Al-4V. Acta Mater 58(9):3303–3312

    Article  Google Scholar 

  40. Yadroitsev I, Gusarov A, Yadroitsava I, Smurov I (2010) Single track formation in selective laser melting of metal powders. J Mater Process Technol 210(12):1624–1631

    Article  Google Scholar 

  41. Kruth J-P, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M, Lauwers B (2004) Selective laser melting of iron-based powder. J Mater Process Technol 149(1):616–622

    Article  Google Scholar 

  42. Aboulkhair NT, Everitt NM, Ashcroft I, Tuck C (2014) Reducing porosity in AlSi10Mg parts processed by selective laser melting. Addit Manuf 1:77–86

    Article  Google Scholar 

  43. Thijs L, Kempen K, Kruth J-P, Van Humbeeck J (2013) Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder. Acta Mater 61(5):1809–1819

    Article  Google Scholar 

  44. Qiu C, Panwisawas C, Ward M, Basoalto HC, Brooks JW, Attallah MM (2015) On the role of melt flow into the surface structure and porosity development during selective laser melting. Acta Mater 96:72–79

    Article  Google Scholar 

Download references

Acknowledgments

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344, and was partially supported by an Early Stage Innovations grant from NASA’s Space Technology Research Grants Program, Grant No. NNX15AD71G. This work was also partially funded through a Laboratory Directed Research and Development (LDRD) grant, Grant No. 15-ERD-037. LLNL Release No. LLNL-JRNL-726754.

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA

    Gustavo Tapia & Alaa Elwany

  2. Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Saad Khairallah

  3. Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Manyalibo Matthews & Wayne E. King

Authors
  1. Gustavo Tapia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saad Khairallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manyalibo Matthews
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wayne E. King
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alaa Elwany
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alaa Elwany.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tapia, G., Khairallah, S., Matthews, M. et al. Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. Int J Adv Manuf Technol 94, 3591–3603 (2018). https://doi.org/10.1007/s00170-017-1045-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-1045-z

Keywords

Search

Navigation