Skip to main content
SpringerLink
Log in

AFRL Additive Manufacturing Modeling Series: Challenge 2, Microscale Process-to-Structure Data Description

  • Thematic Section: Metal Additive Manufacturing Modeling Challenge Series 2020
  • Published:
Integrating Materials and Manufacturing Innovation Aims and scope Submit manuscript

Abstract

The Air Force Research Laboratory Additive Manufacturing Modeling Series was executed to create calibration and validation data sets relevant to models of laser powder bed fusion-processed metallic materials. This article describes the data generated for the 2nd of 4 challenge questions which was specifically focused on microscale process-to-structure modeling needs. This work describes the experimental methods, and the resulting characterization data collected from a series of single-track and multi-track deposits built with an EOS M280 from the nickel-based alloy IN625. In general, track dimensions followed common scaling behaviors as a function of processing parameters in quasi-steady-state regions, but significant systematic track geometry variations were quantified in transient regions with more dynamic energy input processes.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

All of the data described above for calibration and validation data sets in both raw and reduced form have been published at the Materials Data Facility [23, 24], specifically at Ref. [25]. The supplementary information to this article includes a “Data Manifest” document which describes the directory structure, file naming convention, and additional details of the files.

References

  1. Groeber M, Schwalbach E, Musinski W, Shade P, Donegan S, Uchic M, Daniel S, Turner T, Miller J (2018) A preview of the US air force research laboratory additive manufacturing modeling challenge series. JOM 70:441–444. https://doi.org/10.1007/s11837-018-2806-3

    Article  Google Scholar 

  2. Cox ME, Schwalbach EJ, Blaiszik BJ, Groeber MA (2021) AFRL additive manufacturing modeling series: overview. Integr Mater Manuf Innov. https://doi.org/10.1007/s40192-021-00215-6

    Article  Google Scholar 

  3. Chuang AC, Park J-S, Shade PA, Schwalbach EJ, Groeber MA, and Musinski WD AFRL additive manufacturing modeling series: challenge 1, characterization of residual strain distribution in additively-manufactured metal parts using energy dispersive diffraction. Integr Mater Manuf Innov (In Review)

  4. Musinski W, Blosser P, Torbet C, Schwalbach E, Chapman M, Donegan S, Pollock T, and Groeber M AFRL additive manufacturing modeling series: challenge 3, room temperature testing of additively-manufactured Inconel 625 under a range of microstructural and specimen configurations. Integr Mater Manuf Innov (In Review)

  5. Chapman MG, Shah MN, Donegan SP, Scott JM, Shade PA, Menasche D, Uchic MD (2021) AFRL additive manufacturing modeling series: challenge 4, 3D reconstruction of an IN625 high-energy diffraction microscopy sample using multi-modal serial sectioning. Integr Mater Manuf Innov 10:111. https://doi.org/10.1007/s40192-021-00212-9

    Article  Google Scholar 

  6. Menasche DB, Musinski WD, Obstalecki M, Shah MN, Donegan SP, Bernier JV, Kenesei P, Park J-S and Shade PA (2021) AFRL additive manufacturing modeling series: challenge 4, in situ mechanical test of an IN625 sample with concurrent high-energy diffraction microscopy characterization. Integr Mater Manuf Innov. https://doi.org/10.1007/s40192-021-00218-3

  7. Montgomery C, Beuth J, Sheridan L and Klingbeil N Process mapping of Inconel 625 in laser powder bed additive manufacturing. In:Solid freeform fabrication proceedings 2015. Austin, TX. http://utw10945.utweb.utexas.edu/sites/default/files/2015/2015-97-Montgomery.pdf. Accessed 21 Jun 2021

  8. Gong H, Rafi K, Gu H, Starr T, Stucker B (2014) Analysis of defect generation in Ti–6Al–4V parts made using powder bed fusion additive manufacturing processes. Addit Manuf 1–4:87–98. https://doi.org/10.1016/j.addma.2014.08.002

    Article  Google Scholar 

  9. Poulin JR, Brailovski V, Terriault P (2018) Long fatigue crack propagation behavior of Inconel 625 processed by laser powder bed fusion: influence of build orientation and post-processing conditions. Int J Fatigue 116(July):634–647. https://doi.org/10.1016/j.ijfatigue.2018.07.008

    Article  CAS  Google Scholar 

  10. Poulin JR, Kreitcberg A, Terriault P, Brailovski V (2019) Long fatigue crack propagation behavior of laser powder bed-fused inconel 625 with intentionally-seeded porosity. Int J Fatigue 127(June):144–156. https://doi.org/10.1016/j.ijfatigue.2019.06.008

    Article  CAS  Google Scholar 

  11. Heigel JC, Lane BM (2018) Measurement of the melt pool length during single scan tracks in a commercial laser powder bed fusion process. J Manuf Sci Eng 140(5):051012. https://doi.org/10.1115/1.4037571

    Article  Google Scholar 

  12. Heigel J, Lane B, Levine L, Phan T, Whiting J (2020) Thermography of the metal bridge structures fabricated for the 2018 additive manufacturing benchmark test series (AMBench 2018). J Res Nat Inst Stand Technol 125:125005. https://doi.org/10.6028/jres.125.005

    Article  CAS  Google Scholar 

  13. Lane B, Heigel J, Ricker R, Zhirnov I, Khromschenko V, Weaver J, Phan T, Stoudt M, Mekhontsev S, Levine L (2020) Measurements of melt pool geometry and cooling rates of individual laser traces on IN625 bare plates. Integr Mater Manuf Innov 9:16–30. https://doi.org/10.1007/s40192-020-00169-1

    Article  Google Scholar 

  14. Masoomi M, Gao X, Thompson SM, Shamsaei N, Bian L, and Elwany A (2015) Modeling, simulation and experimental validation of heat transfer during selective laser melting. In: Proceedings of the ASME 2015 international mechanical engineering congress and exposition. Houston, TX. https://doi.org/10.1115/IMECE2015-52165

  15. ISO/ASTM (2013) 52921: Standard terminology for additive manufacturing - coordinate systems and test methodologies, ISO/ASTM International

  16. Tang M, Pistorius P, Beuth J (2017) Prediction of lack-of-fusion porosity for powder bed fusion. Addit Manuf 14:39–48. https://doi.org/10.1016/j.addma.2016.12.001

    Article  CAS  Google Scholar 

  17. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White D, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  Google Scholar 

  18. Uchic M, Groeber M, Shah M, Callahan P, Shiveley A, Scott M, Chapman M and Spowart J (2016) An automated multi-modal serial sectioning system for characterization of grain-scale microstructures in engineering materials. In: Proceedings of the 1st international conference on 3D materials science. https://doi.org/10.1007/978-3-319-48762-5_30

  19. Rubenchik A, King W, Wu S (2018) Scaling laws for the additive manufacturing. J Mater Process Technol 257:234–243. https://doi.org/10.1016/j.jmatprotec.2018.02.034

    Article  Google Scholar 

  20. Trapp J, Rubenchik A, Guss G, Matthews M (2017) In situ absorptivity measurements of metallic powders during laser powder-bed fusion additive manufacturing. Appl Mater Today 9(Suppl. C):341–349. https://doi.org/10.1016/j.apmt.2017.08.006

    Article  Google Scholar 

  21. Lowe DG (2006) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  22. Ostu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  23. Blaiszik B, Chard K, Pruyne J, Ananthakrishnan R, Tuecke S, Foster I (2016) The materials data facility: data services to advance materials science research. JOM 68(8):2045–2052. https://doi.org/10.1007/s11837-016-2001-3

    Article  Google Scholar 

  24. Blaiszik B, Ward L, Schwarting M, Gaff J, Chard R, Pike D, Chard K, Foster I (2019) A data ecosystem to support machine learning in materials science. MRS Commun. https://doi.org/10.1557/mrc.2019.118

    Article  Google Scholar 

  25. Groeber M, Schwalbach E, Donegan S, Uchic M, Chapman M, Shade P, Musinski W, Miller J, Turner T, Sparkman D and Cox M AFRL AM modeling challenge series: challenge 2 data package. https://doi.org/10.18126/M27H1Z

  26. Schwalbach EJ, Donegan SP, Chapman MG, Chaput KJ, Groeber MA (2019) A discrete source model of powder bed fusion additive manufacturing thermal history. Addit Manuf 25:485–498. https://doi.org/10.1016/j.addma.2018.12.004

    Article  Google Scholar 

  27. Martin AA, Calta NP, Khairallah SA, Wang J, Depond PJ, Fong AY, Thampy V, Guss GM, Kiss AM, Stone KH, Tassone CJ, Weker JN, Toney MF, van Buuren T (2019) Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nat Commun 10:1987. https://doi.org/10.1038/s41467-019-10009-2

    Article  CAS  Google Scholar 

  28. Zhang Y, Chen Y, Li P, Male AT (2003) Weld deposition-based rapid prototyping: a preliminary study. J Mater Process Technol 135:347–357. https://doi.org/10.1016/S0924-0136(02)00867-1

    Article  Google Scholar 

  29. Khairallah S, Martin A, Lee J, Guss G, Calta N, Hammons J, Nielsen M, Chaput K, Schwalbach E, Shah M, Chapman M, Willey T, Rubenchik A, Anderson A, Wang Y, Matthews M, King W (2020) Controlling interdependent meso-nanosecond dynamics and defect generation in metal 3D printing. Science 368(6491):660–665. https://doi.org/10.1126/science.aay7830

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We wish to acknowledge Kevin Cwiok for his assistance in preparation and execution of specimen fabrication; Michael Uchic and Sean Donegan for fruitful discussions regarding the details of the sectioning as well as optical and electron imaging experiments; Paul Wittmann for key-point identification in the single- and multi-track cross section image set, E. Begum Gulsoy, Ben Blaiszik, James Fourman and Matt Jacobsen for assistance in data curation; Marie Cox as the program manager for the AFRL AMMC effort; and the AFRL AMMC team at large.

Author information

Authors and Affiliations

  1. Wright-Patterson Air Force Base, Dayton, OH, 45433, USA

    Edwin J. Schwalbach & Michael G. Chapman

  2. UES Inc., Dayton, OH, 45432, USA

    Michael G. Chapman

  3. Department of Integrated Systems Engineering, Ohio State University, Columbus, OH, 43210, USA

    Michael A. Groeber

Authors
  1. Edwin J. Schwalbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael G. Chapman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael A. Groeber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edwin J. Schwalbach.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (DOCX 48 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schwalbach, E.J., Chapman, M.G. & Groeber, M.A. AFRL Additive Manufacturing Modeling Series: Challenge 2, Microscale Process-to-Structure Data Description. Integr Mater Manuf Innov 10, 319–337 (2021). https://doi.org/10.1007/s40192-021-00220-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40192-021-00220-9

Keywords

Search

Navigation