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Title: Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy

Abstract

The third Sandia Fracture Challenge illustrated the geometric and material uncertainties presented by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Particularly, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This method allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1559548
Report Number(s):
SAND-2018-11774J
Journal ID: ISSN 0376-9429; 669291
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
International Journal of Fracture
Additional Journal Information:
Journal Volume: 218; Journal Issue: 1-2; Journal ID: ISSN 0376-9429
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Ductile failure; Finite element modeling; Additive manufacturing; Direct metal laser sintering (DMLS); 316L stainless steel; Material calibration; Plastic anisotropy

Citation Formats

Karlson, Kyle N., Alleman, Coleman, Foulk III, James W., Manktelow, Kevin L., Ostien, Jakob T., Stender, Michael E., Stershic, Andrew J., and Veilleux, Michael G. Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy. United States: N. p., 2019. Web. doi:10.1007/s10704-019-00365-x.
Karlson, Kyle N., Alleman, Coleman, Foulk III, James W., Manktelow, Kevin L., Ostien, Jakob T., Stender, Michael E., Stershic, Andrew J., & Veilleux, Michael G. Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy. United States. doi:10.1007/s10704-019-00365-x.
Karlson, Kyle N., Alleman, Coleman, Foulk III, James W., Manktelow, Kevin L., Ostien, Jakob T., Stender, Michael E., Stershic, Andrew J., and Veilleux, Michael G. Tue . "Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy". United States. doi:10.1007/s10704-019-00365-x.
@article{osti_1559548,
title = {Sandia Fracture Challenge 3: detailing the Sandia Team Q failure prediction strategy},
author = {Karlson, Kyle N. and Alleman, Coleman and Foulk III, James W. and Manktelow, Kevin L. and Ostien, Jakob T. and Stender, Michael E. and Stershic, Andrew J. and Veilleux, Michael G.},
abstractNote = {The third Sandia Fracture Challenge illustrated the geometric and material uncertainties presented by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Particularly, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This method allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.},
doi = {10.1007/s10704-019-00365-x},
journal = {International Journal of Fracture},
issn = {0376-9429},
number = 1-2,
volume = 218,
place = {United States},
year = {2019},
month = {7}
}

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