The Sandia Fracture Challenges provide the mechanics community a forum for assessing its ability to predict ductile fracture through a blind, round-robin format where mechanicians are challenged to predict the deformation and failure of an arbitrary geometry given experimental calibration data. The Third Challenge, issued in 2017, required participants to predict fracture in an additively manufactured 316L stainless steel tensile-bar configuration containing through holes and internal cavities that could not have been conventionally machined. The volunteer participants were provided extensive materials data, from tensile tests of specimens printed on the same build tray to electron backscatter diffraction maps of the microstructure and micro-computed tomography scans of the Challenge geometry. The teams were asked to predict a number of quantities of interest in the response, including predictions of variability in the resulting fracture response, as the basis for assessment of the predictive capabilities of the modeling and simulation strategies. This paper describes the Third Challenge, compares the experimental results to the predictions, and identifies successes and gaps in capabilities in both the experimental procedures and the computational analyses to inform future investigations.

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The aggregate-objective optimization technique has been used to assess the optimal set of coefficients of the Barlat yield criterion for anisotropic alloy sheets by means of the evolution strategy. The aggregate-objective problem has been quantified by the Euclidean-type objective function.Computational results indicate that standard procedures, based on three uniaxial yield stresses for different directions of loading in-plane, the balanced biaxial yield stress, and three Lankford coefficients of anisotropy determined for three different directions of loading in-plane, cannot be sufficient to assess unequivocally the Barlat yield criterion for anisotropic alloy sheets.Keywords Aggregate-objective optimization; Planar anisotropy; Searching the extremum of the objective function with the evolution strategy; Yield function.