AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions
Source abstract
Abstract
Abstract The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress–strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion. Two different laser scan strategies were considered: an X-only raster and an XY raster, which involved a 90$$^\circ $$ rotation in the scan direction between subsequent layers. To measure anisotropy, multiple tensile orientations with respect to the build direction were investigated (e.g., parallel, perpendicular, and intervals in between). Benchmark participants were provided grain structure information via electron backscatter diffraction measurements, as well as the stress–strain response for tensile specimens manufactured parallel to the build direction and produced by the XY scan strategy. Then, participants were asked to predict tensile properties, like the ultimate tensile strength, for the remaining specimens and orientations. Interestingly, the measured mechanical properties did not vary linearly as a function of tensile orientation. Moreover, specimens manufactured with the XY scan strategy exhibited greater yield strength than those corresponding to the X-only scan strategy, regardless of orientation. The benchmark data have been made publicly available for anyone that is interested [1]. For the modeling aspect of the challenge, five teams participated in this benchmark. While most of the models incorporated a crystal plasticity framework, one team chose to use a more semiempirical approach and to great success. However, no team excelled at all the predictions, and all teams were seemingly challenged with the predictions associated with the X-only scan strategy.
Source: crossref · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metal Forming Simulation Techniques · Advanced Surface Polishing Techniques · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (19)
Show source-provided references
1. AM Bench (2022) Challenge macroscale tensile tests at different orientations (CHAL-AMB2022-04-MaTTO). National Institute of Standards and Technology https://doi.org/10.18434/mds2-258810.18434/mds2-2588 ↗
2. Boyce BL, Kramer SLB, Fang HE, Cordova TE, Neilsen MK, Dion K, Kaczmarowski AK, Karasz E, Xue L, Gross AJ, Ghahremaninezhad A, Ravi-Chandar K, Lin S-P, Chi S-W, Chen JS, Yreux E, Rüter M, Qian D, Zhou Z, Bhamare S, O’Connor DT, Tang S, Elkhodary KI, Zhao J, Hochhalter JD, Cerrone AR, Ingraffea AR, Wawrzynek PA, Carter BJ, Emery JM, Veilleux MG, Yang P, Gan Y, Zhang X, Chen Z, Madenci E, Kilic B, Zhang T, Fang E, Liu P, Lua J, Nahshon K, Miraglia M, Cruce J, DeFrese R, Moyer ET, Brinckmann S, Quinkert L, Pack K, Luo M, Wierzbicki T (2014) The Sandia fracture challenge: blind round robin predictions of ductile tearing. Int J Fract. https://doi.org/10.1007/s10704-013-9904-610.1007/s10704-013-9904-6 ↗
3. Kramer SLB, Jones A, Mostafa A, Ravaji B, Tancogne-Dejean T, Roth CC, Bandpay MG, Pack K, Foster JT, Behzadinasab M, Sobotka JC, McFarland JM, Stein J, Spear AD, Newell P, Czabaj MW, Williams B, Simha H, Gesing M, Gilkey LN, Jones CA, Dingreville R, Sanborn SE, Bignell JL, Cerrone AR, Keim V, Nonn A, Cooreman S, Thibaux P, Ames N, Connor DO, Parno M, Davis B, Tucker J, Coudrillier B, Karlson KN, Ostien JT, Foulk JW, Hammetter CI, Grange S, Emery JM, Brown JA, Bishop JE, Johnson KL, Ford KR, Brinckmann S, Neilsen MK, Jackiewicz J, Ravi-Chandar K, Ivanoff T, Salzbrenner BC, Boyce B (2019) The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal. Int J Fract. https://doi.org/10.1007/s10704-019-00361-110.1007/s10704-019-00361-1 ↗
4. Cox ME, Schwalbach EJ, Blaiszik BJ, Groeber MA (2021) AFRL additive manufacturing modeling challenge series: overview. Integr Mater Manuf Innov. https://doi.org/10.1007/s40192-021-00215-610.1007/s40192-021-00215-6 ↗
5. 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(1):1–15. https://doi.org/10.1007/s40192-019-00164-110.1007/s40192-019-00164-1 ↗
6. ASTM E01 Committee: ASTM E1479-16: standard practice for describing and specifying inductively coupled plasma atomic emission spectrometers (2016) https://doi.org/10.1520/E1479-1610.1520/E1479-16 ↗
7. ASTM E01 Committee: (2018) ASTM E1019-18: standard test methods for determination of carbon, sulfur, nitrogen, and oxygen in steel, iron, nickel, and cobalt alloys by various combustion and inert gas fusion techniques https://doi.org/10.1520/E1019-1810.1520/E1019-18 ↗
8. ASTM B2 Committee: (2022) ASTM B564-22: standard specification for nickel alloy forgingshttps://doi.org/10.1520/B0564-2210.1520/B0564-22 ↗
9. ASTM E08 Committee: ASTM E3076-18 standard practice for determination of the slope in the linear region of a test record. Technical report, ASTM International. https://doi.org/10.1520/E3076-1810.1520/E3076-18 ↗
10. Lucon E (June 2019) Use and validation of the slope determination by the analysis of residuals (SDAR) algorithm. Technical Report NIST TN 2050, National Institute of Standards and Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.TN.2050 . https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.2050.pdf Accessed 2023-10-1710.6028/NIST.TN.2050 ↗
11. Scout-and-Scan Reconstructor. Zeiss. https://www.zeiss.com/microscopy/us/l/campaigns/scout-and-scan.html
12. Zhang F, Levine LE, Allen AJ, Young SW, Williams ME, Stoudt MR, Moon K-W, Heigel JC, Ilavsky J (2019) Phase fraction and evolution of additively manufactured (AM) 15–5 stainless steel and inconel 625 AM-bench artifacts. Integr Mater Manuf Innov 8(3):362–377. https://doi.org/10.1007/s40192-019-00148-110.1007/s40192-019-00148-1 ↗
13. 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(3):318–334. https://doi.org/10.1007/s40192-019-00149-010.1007/s40192-019-00149-0 ↗
14. Lee PL, Shu D, Ramanathan M, Preissner C, Wang J, Beno MA, Von Dreele RB, Ribaud L, Kurtz C, Antao SM, Jiao X, Toby BH (2008) A twelve-analyzer detector system for high-resolution powder diffraction. J Synchrotron Rad 15(5):427–432. https://doi.org/10.1107/S090904950801843810.1107/S0909049508018438 ↗
15. Williamson GK, Smallman RE (1956) III. Dislocation densities in some annealed and cold-worked metals from measurements on the X-ray debye-scherrer spectrum. The Philosophical Magazine A J Theor Exp Appl Phys 1(1):34–46 https://doi.org/10.1080/14786435608238074 .10.1080/14786435608238074 ↗
16. Wang Z, Stoica AD, Ma D, Beese AM (2016) Diffraction and single-crystal elastic constants of Inconel 625 at room and elevated temperatures determined by neutron diffraction. Mater Sci Eng A 674:406–412. https://doi.org/10.1016/j.msea.2016.08.01010.1016/j.msea.2016.08.010 ↗
17. Groeber MA, Jackson MA (2014) DREAM 3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3(1):5. https://doi.org/10.1186/2193-9772-3-510.1186/2193-9772-3-5 ↗
18. Lindsay AD, Gaston DR, Permann CJ, Miller JM, Andrš D, Slaughter AE, Kong F, Hansel J, Carlsen RW, Icenhour C, Harbour L, Giudicelli GL, Stogner RH, GermanP Badger J, Biswas S, Chapuis L, Green C, Hales J, Hu T, Jiang W, Jung YS, Matthews C, Miao Y, Novak A, Peterson JW, Prince ZM, Rovinelli A, Schunert S, Schwen D, Spencer BW, Veeraraghavan S, Recuero A, Yushu D, Wang Y, Wilkins A, Wong C (2022) 2.0—MOOSE: Enabling massively parallel multiphysics simulation. SoftwareX 20:101202. https://doi.org/10.1016/j.softx.2022.10120210.1016/j.softx.2022.101202 ↗
19. Moser NH, Landauer AK, Kafka OL (2023) Image processing in Python for 3D image stacks (Imppy3d). National Institute of Standards and Technology. https://doi.org/10.18434/mds2-2806 . https://github.com/usnistgov/imppy3d10.18434/mds2-2806 ↗