The AFRL Additive Manufacturing Modeling Challenge: Predicting Micromechanical Fields in AM IN625 Using an FFT-Based Method with Direct Input from a 3D Microstructural Image

The efficacy of an elasto-viscoplastic fast Fourier transform (EVPFFT) code was assessed based on blind predictions of micromechanical fields in a sample of Inconel 625 produced with additive manufacturing (AM) and experimentally characterized with high-energy X-ray diffraction microscopy during an in situ tensile test. The blind predictions were made in the context of Challenge 4 in the AFRL AM Modeling Challenge Series, which required predictions of grain-averaged elastic strain tensors for 28 unique target (Challenge) grains at six target stress states given a 3D microstructural image, initial elastic strains of Challenge grains, and macroscopic stress–strain response. Among all submissions, the EVPFFT-based submission presented in this work achieved the lowest total error in comparison with experimental results and received the award for Top Performer. A post-Challenge investigation by the authors revealed that predictions could be further improved, by over 25% compared to the Challenge-submission model, through several model modifications that required no additional information beyond what was initially provided for the Challenge. These modifications included a material parameter optimization scheme to improve model bias and the incorporation of the initial strain field through both superposition and eigenstrain methods. For the first time with respect to EVPFFT modeling, an ellipsoidal-grain-shape Eshelby approximation was tested and shown to improve predictive capability compared to previously used spherical-grain-shape assumptions. Lessons learned for predicting full-field micromechanical response using the EVPFFT modeling method are discussed.