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AFRL Additive Manufacturing Modeling Series: Challenge 1, Characterization of Residual Strain Distribution in Additively-Manufactured Metal Parts Using Energy-Dispersive Diffraction

Abstract

A machine component fabricated by additive manufacturing (AM) of metal powders often possesses steep residual stress gradients with significant magnitudes due to large temperature gradients that transpire within a localized area during fabrication. These processing-induced residual stresses can cause distortion of the part, and if they are of significant magnitude, they could induce cracking of the component, degrade printability, and/or diminish subsequent mechanical performance. The ability to predict these residual stresses imparted by AM is an important step in permeating AM technology for advanced manufacturing. Calibration and validation of AM process models used for prediction are, therefore, a critical step in understanding the origin and mitigating the challenges associated with residual stresses inherent to the AM process. In the present work, the residual strain distributions in components with simple geometries fabricated by a laser powder bed fusion (LBPF) process were characterized non-destructively using energy-dispersive X-ray diffraction in support of the US Air Force Research Laboratory Additive Manufacturing Modeling Challenge Series Groeber et al. (JOM 70:441-448, 2018). The measurement setup and approach are described in detail so that the data can be used as a benchmark to calibrate and validate models for the prediction of macro-scale residual stresses due to the LPBF process.

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Notes

  1. 1.

    Precision of stages is better than 1\({\upmu} \hbox{m}\). Surface roughness of the specimen hinder our ability to position VOI at higher precision.

  2. 2.

    Only the 1-mm-thick tube was measured using MC3 because thicker-walled (3-mm-thick and 5-mm-thick) tubes had too much attenuation for an adequate EDXRD measurement.

  3. 3.

    Here, the equation is a rearrangement of Bragg’s law and \(\frac{hc}{E^{hkl}}\) corresponds to the wavelength of the diffracting photons.

  4. 4.

    This rough equivalence is based on the assumption of isotropic elasticity, an assumed room temperature elastic modulus for IN625 of 200 GPa, and assuming a uniaxial stress field. More accurate calculation of corresponding stresses could be obtained by considering all components of strain tensor and appropriate anisotropic elastic behavior.

  5. 5.

    The data package is available at https://doi.org/10.18126/8jfl-i4d8.

References

  1. 1.

    Groeber M, Schwalbach E, Musinski W, Shade P, Donegan S, Uchic M, Sparkman D, Turner T, Miller J (2018) A preview of the U.S. air force research laboratory additive manufacturing modeling challenge series. JOM Member J Miner Metals Mater Soc 70:441–444

    Article  Google Scholar 

  2. 2.

    Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Wang CC, Shin YC, Zhang S, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. CAD Comp Aided Des 69:65–89

    Article  Google Scholar 

  3. 3.

    Gardan J (2016) Additive manufacturing technologies: state of the art and trends. Int J Prod Res 54:3118–3132

    Article  Google Scholar 

  4. 4.

    DebRoy T, Mukherjee T, Milewski JO, Elmer JW, Ribic B, Blecher JJ, Zhang W (2019) Scientific, technological and economic issues in metal printing and their solutions. Nat Mater 18:1026–1032

    CAS  Article  Google Scholar 

  5. 5.

    Nagesha B, Dhinakaran V, Varsha Shree M, Manoj Kumar K, Chalawadi D, Sathish T (2020) Review on characterization and impacts of the lattice structure in additive manufacturing. Mater Today: Proc 21:916–919

  6. 6.

    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

    Google Scholar 

  7. 7.

    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

    Google Scholar 

  8. 8.

    Shipley H, McDonnell D, Culleton M, Coull R, Lupoi R, O’Donnell G, Trimble D (2018) Optimisation of process parameters to address fundamental challenges during selective laser melting of Ti-6Al-4V: a review. Int J Mach Tools Manuf 128:1–20

    Article  Google Scholar 

  9. 9.

    Seede R, Shoukr D, Zhang B, Whitt A, Gibbons S, Flater P, Elwany A, Arroyave R, Karaman I (2020) An ultra-high strength martensitic steel fabricated using selective laser melting additive manufacturing: densification, microstructure, and mechanical properties. Acta Mater 186:199–214

    CAS  Article  Google Scholar 

  10. 10.

    Nguyen DS, Park HS, Lee CM (2020) Optimization of selective laser melting process parameters for Ti-6Al-4V alloy manufacturing using deep learning. J Manuf Process 55:230–235

    Article  Google Scholar 

  11. 11.

    Tolosa I, Garciandía F, Zubiri F, Zapirain F, Esnaola A (2010) Study of mechanical properties of AISI 316 stainless steel processed by “selective laser melting”, following different manufacturing strategies. Int J Adv Manuf Technol 51:639–647

    Article  Google Scholar 

  12. 12.

    Calignano F, Lorusso M, Pakkanen J, Trevisan F, Ambrosio EP, Manfredi D, Fino P (2017) Investigation of accuracy and dimensional limits of part produced in aluminum alloy by selective laser melting. Int J Adv Manuf Technol 88:451–458

    Article  Google Scholar 

  13. 13.

    Han J, Wu M, Ge Y, Wu J (2018) Optimizing the structure accuracy by changing the scanning strategy using selective laser melting. Int J Adv Manuf Technol 95:4439–4447

    Article  Google Scholar 

  14. 14.

    Ganesh P, Kaul R, Sasikala G, Kumar H, Venugopal S, Tiwari P, Rai S, Prasad RC, Kukreja LM (2014) Fatigue crack propagation and fracture toughness of laser rapid manufactured structures of AISI 316L stainless steel. Metallogr Microstruct Anal 3:36–45

    CAS  Article  Google Scholar 

  15. 15.

    Huynh L, Rotella J, Sangid MD (2016) Fatigue behavior of IN718 microtrusses produced via additive manufacturing. Mater Des 105:278–289

    CAS  Article  Google Scholar 

  16. 16.

    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

    CAS  Article  Google Scholar 

  17. 17.

    Bian L, Thompson SM, Shamsaei N (2015) Mechanical properties and microstructural features of direct laser-deposited Ti-6Al-4V. JOM 67:629–638

    CAS  Article  Google Scholar 

  18. 18.

    Ran X, Liu D, Li A, Wang H, Tang H, Cheng X (2016) Microstructure characterization and mechanical behavior of laser additive manufactured ultrahigh-strength AerMet100 steel. Mater Sci Eng A 663:69–77

    CAS  Article  Google Scholar 

  19. 19.

    Wang Q, Zhang S, Zhang CH, Wu C, Wang J, Chen J, Sun Z (2017) Microstructure evolution and EBSD analysis of a graded steel fabricated by laser additive manufacturing. Vacuum 141:68–81

    CAS  Article  Google Scholar 

  20. 20.

    du Plessis A, le Roux SG (2018) Standardized X-ray tomography testing of additively manufactured parts: a round robin test. Additive Manuf 24:125–136

    Article  CAS  Google Scholar 

  21. 21.

    Cakmak E, Bingham P, Cunningham RW, Rollett AD, Xiao X, Dehoff RR (2021) Non-destructive characterization of additively manufactured components with x-ray computed tomography for part qualification: a study with laboratory and synchrotron x-rays. Mater Character 173:110894

    CAS  Article  Google Scholar 

  22. 22.

    Pesach A, Tiferet E, Vogel SC, Chonin M, Diskin A, Zilberman L, Rivin O, Yeheskel O, Caspi EN (2018) Texture analysis of additively manufactured Ti-6Al-4V using neutron diffraction. Additive Manuf 23:394–401

    CAS  Article  Google Scholar 

  23. 23.

    Zhao C, Fezzaa K, Cunningham RW, Wen H, De Carlo F, Chen L, Rollett AD, Sun T (2017) Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction. Sci Rep 7:1–11

    Article  CAS  Google Scholar 

  24. 24.

    Kenel C, Grolimund D, Li X, Panepucci E, Samson VA, Sanchez DF, Marone F, Leinenbach C (2017) In situ investigation of phase transformations in Ti-6Al-4V under additive manufacturing conditions combining laser melting and high-speed micro-X-ray diffraction. Sci Rep 7:1–10

    CAS  Article  Google Scholar 

  25. 25.

    Calta NP, Wang J, Kiss AM, Martin AA, Depond PJ, Guss GM, Thampy V, Fong AY, Weker JN, Stone KH, Tassone CJ, Kramer MJ, Toney MF, Van Buuren A, Matthews MJ (2018) An instrument for in situ time-resolved X-ray imaging and diffraction of laser powder bed fusion additive manufacturing processes. Rev Sci Instrum 89:055101

    Article  CAS  Google Scholar 

  26. 26.

    Brown DW, Losko A, Carpenter JS, Clausen B, Cooley JC, Livescu V, Kenesei P, Park J-S, Stockman TJ, Strantza M (2020) In-Situ high-energy X-ray diffraction during a linear deposition of 308 stainless steel via wire arc additive manufacture. Metall Mater Trans A 51:1379–1394

    CAS  Article  Google Scholar 

  27. 27.

    Mercelis P, Kruth J (2006) Residual stresses in selective laser sintering and selective laser melting. Rapid Prototyp J 12:254–265

    Article  Google Scholar 

  28. 28.

    Lu Y, Wu S, Gan Y, Huang T, Yang C, Junjie L, Lin J (2015) Study on the microstructure, mechanical property and residual stress of SLM Inconel-718 alloy manufactured by differing island scanning strategy. Optics Laser Technol 75:197–206

    CAS  Article  Google Scholar 

  29. 29.

    Fang ZC, Wu ZL, Huang CG, Wu CW (2020) Review on residual stress in selective laser melting additive manufacturing of alloy parts. Optics Laser Technol 129:106283

    CAS  Article  Google Scholar 

  30. 30.

    Parry L, Ashcroft IA, Wildman RD (2016) Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation. Additive Manuf 12:1–15

    Article  Google Scholar 

  31. 31.

    Ahmad B, van der Veen SO, Fitzpatrick ME, Guo H (2018) Measurement and modelling of residual stress in wire-feed additively manufactured titanium. Mater Sci Technol 34:2250–2259

    CAS  Article  Google Scholar 

  32. 32.

    Wei HL, Mukherjee T, Zhang W, Zuback JS, Knapp GL, De A, DebRoy T (2021) Mechanistic models for additive manufacturing of metallic components. Progress Mater Sci 116:100703

    CAS  Article  Google Scholar 

  33. 33.

    Moshiri C (2019) Carmignato, Mohanty Tosello, benchmarking of laser powder bed Ffusion machines. J Manuf Mater Process 3:85

    CAS  Google Scholar 

  34. 34.

    Huang H, Chen J, Carlson B, Wang H-P, Crooker P, Frederick G,  Feng Z (2018) Stress and distortion simulation of additive manufacturing process by high performance computing, In: Volume 6A: materials and fabrication. American Society of Mechanical Engineers

  35. 35.

    Peter N, Pitts Z, Thompson S, Saharan A (2020) Benchmarking build simulation software for laser powder bed fusion of metals. Additive Manuf 36:101531

    CAS  Article  Google Scholar 

  36. 36.

    Levine L, Stoudt M, Lane B (2018) A preview of the NIST/TMS additive manufacturing benchmark test and conference series. JOM 70:259–260

    Article  Google Scholar 

  37. 37.

    Levine L, Lane B, Heigel J, Migler K, Stoudt M, Phan T, Ricker R, Strantza M, Hill M, Zhang F, Seppala J, Garboczi E, Bain E, Cole D, Allen A, Fox J, Campbell C, (2020) Outcomes and conclusions from the 2018 AM-Bench measurements, challenge problems, modeling submissions, and conference. Integr Mater Manuf Innov 9(2020):1–15

    Article  Google Scholar 

  38. 38.

    Phan TQ, Strantza M, Hill MR, Gnaupel-Herold TH, Heigel J, D’Elia CR, DeWald AT, Clausen B, Pagan DC, Peter Ko JY, Brown DW, Levine LE (2019) Elastic residual strain and stress measurements and corresponding part deflections of 3D additive manufacturing builds of IN625 AM-Bench artifacts using neutron diffraction synchrotron X-Ray diffraction and contour method. Integr Mater Manuf Innov 8:318–334

    Article  Google Scholar 

  39. 39.

    CHALLENGE RESULTS ANNOUNCED | Air Force Research Laboratory (AFRL) Additive Manufacturing (AM) Modeling Challenge Series (2021) https://materials-data-facility.github.io/MID3AS-AM-Challenge/

  40. 40.

    Cox ME, Schwalbach EJ, Blaiszik B, Groeber MA (2021) AFRL additive manufacturing modeling challenge series: overview. Integr Mater Manuf Innov 10:125–128

    Article  Google Scholar 

  41. 41.

    Schwalbach EJ, Chapman MG, Groeber MA (2021) AFRL additive manufacturing modeling series: challenge 2. Microscale process-to-structure data description. Integr Mater Manuf Innov 10:319–337

    Article  Google Scholar 

  42. 42.

    Musinski WD, Blosser PE, Torbet CJ, Schwalbach EJ, Chapman MG, Donegan SP, Pollock TM, Groeber MA AFRL additive manufacturing modeling series: challenge 3, room temperature testing of additively-manufactured inconel 625 under a range of microstructural and specimen configurations, Submitted to Integr Mater Manuf Innov

  43. 43.

    Chapman MG, Shah MN, Donegan SP, Scott JM, Shade PA, Menasche DB, 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 10:129–141

    Article  Google Scholar 

  44. 44.

    Menasche DB, Musinski WD, Obstalecki M, Shah MN, Donegan SP, Bernier JV, Kenesei P, Park J-S, 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 10:338–347

    Article  Google Scholar 

  45. 45.

    CHALLENGE 1: Macro-scale Process-to-Structure Predictions, Problem Statement | Air Force Research Laboratory (AFRL) Additive Manufacturing (AM) Modeling Challenge Series (2021). https://materials-data-facility.github.io/MID3AS-AM-Challenge/Challenge1ProblemStatement_2019Release.pdf

  46. 46.

    ISO/ASTM52921-13 (2019) Standard terminology for additive manufacturing–coordinate systems and test methodologies, vol 10.04, 14 p

  47. 47.

    Park JS, Okasinski J,  Chatterjee K, Chen Y, Almer J (2017) Non-destructive characterization of engineering materials using high-energy X-rays at the advanced photon source. Synchrotron Radiat News 30:9–16. http://dx.doi.org/10.1080/08940886.2017.1316125

  48. 48.

    Park JS, Chuang CP, Okasinski J 6-bm, an energy dispersive x-ray diffraction beamline at the advanced photon source for engineering applications, in prep (unpublished)

  49. 49.

    APS, 6BM beamline configuration (2020). https://www.aps.anl.gov/Sector-6/6-BM

  50. 50.

    Kaiser DL, Watters JRL (2007) National Institute of standards and technology standard reference material674b X-ray powder diffraction intensity set for quantitative analysis by X-ray powder diffraction. Technical Report, National Institute of Standards and Technology

  51. 51.

    Rowles MR (2011) On the calculation of the gauge volume size for energy-dispersive X-ray diffraction. J Synchrot Radiat 18:938–941

    Article  Google Scholar 

  52. 52.

    David WIF (1986) Powder diffraction peak shapes. Parameterization of the pseudo-Voigt as a Voigt function. J Appl Crystallogr 19:63–64

    CAS  Article  Google Scholar 

  53. 53.

    Young RA (1995) The Rietveld method IUCr, monographs on crystallography. Oxford Science Publications

    Google Scholar 

  54. 54.

    Ma D, Stoica AD, Wang Z, Beese AM (2017) Crystallographic texture in an additively manufactured nickel-base superalloy. Mater Sci Eng A 684:47–53

    CAS  Article  Google Scholar 

  55. 55.

    de Jager B, Zhang B, Song X, Papadaki C, Zhang H, Romano Brandt L, Salvati E,  Sui T, Korsunsky A (2017) Texture and microstructure analysis of IN718 nickel superalloy samples additively manufactured by selective laser melting, In: Proceedings of the international multiConference of engineers and computer scientists 2017, vol II, pp. 1689–1699

  56. 56.

    Noyan I, Cohen J (2009) Residual stress-measurement by diffraction and interpretation. Spinger, Verlag

    Google Scholar 

  57. 57.

    Schajer GS (2013) Practical residual stress measurement methods. John Wiley & Sons Ltd., NY

    Book  Google Scholar 

  58. 58.

    Hauk VM, Vaeseen GJH (1983) Residual stress evaluation with X-rays in steels having preferred orientation. Metall Trans A 15A:1407–1414

    Google Scholar 

  59. 59.

    Daymond MR (2004) The determination of a continuum mechanics equivalent elastic strain from the analysis of multiple diffraction peaks. J Appl Phys 96:4263–4272

    CAS  Article  Google Scholar 

  60. 60.

    Korsunsky AM, Vorster WJJ, Zhang SY, Dini D, Latham D, Golshan M, Liu J, Kyriakoglou Y, Walsh MJ (2006) The principle of strain reconstruction tomography: determination of quench strain distribution from diffraction measurements. Acta Mater 54:2101–2108

    CAS  Article  Google Scholar 

  61. 61.

    Park JS, Ray AK, Dawson PR, Lienert U, Miller MP (2016) Determination of residual stress in a microtextured \(\alpha \) titanium component using high-energy synchrotron X-rays. J Strain Anal Eng Des 51:358–374

    Article  Google Scholar 

  62. 62.

    An K, Yuan L, Dial L, Spinelli I, Stoica AD, Gao Y (2017) Neutron residual stress measurement and numerical modeling in a curved thin-walled structure by laser powder bed fusion additive manufacturing. Mater Des 135:122–132

    CAS  Article  Google Scholar 

  63. 63.

    Li S, Wei Q, Shi Y, Zhu Z, Zhang D (2015) Microstructure characteristics of inconel 625 superalloy manufactured by selective laser melting. J Mater Sci Technol 31:946–952

    CAS  Article  Google Scholar 

  64. 64.

    Kreitcberg A, Brailovski V, Turenne S (2017) Effect of heat treatment and hot isostatic pressing on the microstructure and mechanical properties of Inconel 625 alloy processed by laser powder bed fusion. Mater Sci Eng A 689:1–10

    CAS  Article  Google Scholar 

  65. 65.

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

    Google Scholar 

  66. 66.

    Matheron G (1960) Krigeage d’un panneau rectangulaire par sa périphérie, note géostatistique. http://cg.ensmp.fr/bibliotheque/public/MATHERON_Rapport_00034.pdf

  67. 67.

    Noack MM, Yager KG, Fukuto M, Doerk GS, Li R, Sethian JA (2019) A Kriging-based approach to autonomous experimentation with applications to X-ray scattering. Sci Rep 9:11809

    Article  CAS  Google Scholar 

  68. 68.

    Carrat F, Valleron A-J (1992) Epidemiologic mapping using the “Kriging” method: application to an Influenza-like epidemic in France. Am J Epidemiol 135:1293–1300

    CAS  Article  Google Scholar 

  69. 69.

    Martin JD, Simpson TW (2005) Use of Kriging models to approximate deterministic computer models. AIAA J 43:853–863

    Article  Google Scholar 

  70. 70.

    MATLAB 2017 User’s manual, The MathWorks, Inc., Natick, MA

  71. 71.

    Schiffner K, Droste C (1999) Simulation of residual stresses by shot peening. Comput Struct 72(1–3):329–340

    Article  Google Scholar 

  72. 72.

    Yang F, Gao Y (2016) Predicting the peen forming effectiveness of Ti-6Al-4V strips with different thicknesses using realistic finite element simulations. J Eng Mater Technol Trans ASME 138(1):011004

    Article  CAS  Google Scholar 

  73. 73.

    Buchanan DJ, John R (2014) Residual stress redistribution in shot peened samples subject to mechanical loading. Mater Sci Eng A 615:70–78

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Acknowledgements

ACC, JSP, PAS, EJS, MAG, and WDM acknowledge support from the US Air Force Research Laboratory (AFRL). The authors also acknowledge logistical support from Marie Cox (AFRL, program manager for AFRL AM Modeling Challenge Series), support with machining of the articles from Lance Griffith, and the AFRL AM Modeling Challenge Series team at large. John Okasinski of the APS is acknowledged for support of the beamline experiment. This research used resources of the Advanced Photon Source, a US Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

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Correspondence to William D. Musinski.

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Chuang, A.C., Park, JS., Shade, P.A. et al. 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 (2021). https://doi.org/10.1007/s40192-021-00233-4

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Keywords

  • Residual strain
  • Energy-dispersive diffraction
  • EDXRD
  • Laser powder bed fusion
  • LPBF
  • Selective laser melting
  • SLM
  • Additive manufacturing