Additive manufacturing (AM) shows great promise for the fabrication of metallic structural components; however, uncertainty surrounding the plasticity and fracture of these materials continues to limit the application of this technology. To assess current modeling techniques and drive development of new methods to address emergent mechanisms of deformation for these materials, Sandia National Laboratories (SNL) organized the 2016 Sandia Fracture Challenge. This competition centered around the blind prediction of plasticity and fracture behavior for AM 316L austenitic stainless steel. This presentation focuses on the authors’ response to the challenge. The training data set for the challenge consisted of experimental results for a suite of tension tests. The authors used phenomenological plasticity and damage models to simulate the yield, hardening, and failure of the AM stainless steel, with model parameters calibrated to the tension data in the training set. The fully calibrated model showed excellent agreement with the experimental results, indicating the appropriateness of the model form and the parameterization. The calibrated models were subsequently used to make blind predictions of the plasticity and failure behavior of a more complicated AM geometry. In addition, uncertainty quantification techniques were applied to predict the distribution of this behavior in the challenge specimens, based on material behavior variability in the training set and reported variations in the geometry of the challenge specimens. Ultimately, both the predictions of mean response and variability showed good agreement with the revealed behavior of the challenge specimens.
Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.