Additive Manufacturing Benchmark 2022 Subcontinuum Mesoscale Tensile Challenge (CHAL-AMB2022-04-MeTT) and Summary of Predictions

This additive manufacturing benchmarking challenge asked the modelling community to predict the stress-strain behavior and fracture location and pathway of an individual meso-scale (gauge dimensions of approximately 200 μm thickness, 200 μm width, 1mm length) tension specimen that was excised from a wafer of nickel allow IN625 manufactured by laser powder bed fusion (L-PBF). The data used for the challenge questions and answers are provided in a public dataset (https://data.nist.gov/od/id/mds2-2587). Testing models against the data is still possible, although a good-faith blinded prediction should be attempted before reading this article, as the results are contained herein. The uniaxial tension test was pin loaded, conducted at quasi-static strain rates under displacement control, and strain was measured via non-contact methods (digital image correlation). The predictions are challenging since the number of grains contained in the thickness of the specimen are sub-continuum. In addition, pores can be heterogeneously distributed by the L-PBF process, as opposed to intentionally seeded defects. The challenge provided information on chemical composition, grain and subgrain structure (surface-based measurements via electron backscatter diffraction and scanning electron microscopy) and pore structure (volume-based measurements via X-Ray computed tomography) along the entire gauge length for the tension specimen. During the challenge, prediction responses were collected from six different groups. Prediction accuracy compared to the measurements varied, with elastic modulus and strain at ultimate tensile strength consistently over-predicted, while most other values were a mix of over- and under-predicted. Overall, no one model performed best at all predictions. Failure-related properties proved quite challenging to predict, likely in part due to the data provided as well as the inherent difficulty in predicting fracture. Future directions and areas of improvement are discussed in the context of improving model maturity and measurement uncertainty.