Efficient Calibration Strategy for Crystal Plasticity Constitutive Law to Model Additively Manufactured Alloys

Abstract Spatial heterogeneity in microstructure presents a significant challenge for part qualification in metal additive manufacturing, particularly when relying on physically accurate computational models to replace costly trial-and-error testing. Reliable structural simulation needs a large amount of offline data and several very expensive forward runs to learn the most appropriate material model parameters. This work introduces a systematic framework for accurately calibrating crystal plasticity (CP) material law parameters using limited characterization data. The calibrated CP law is validated through blind predictions of mechanical responses in laser powder-bed fusion Inconel 625 (IN 625) tensile coupons across varying build orientations and strategies. Two surrogate modeling approaches—a higher-order proper generalized decomposition (HOPGD) and a novel interpolating neural network (INN)—are evaluated for their ability to approximate full-field simulations. The study presents an adaptive sampling strategy to efficiently utilize an offline database and outlines a methodology for representing microstructure from sparse characterization inputs. Results demonstrate that the differentiable INN surrogate achieves accurate calibration with significantly reduced data requirements and avoids reliance on computationally expensive genetic algorithms. Both surrogate models exhibit strong predictive performance, and the proposed workflow was instrumental in winning the 2022 NIST Additive Manufacturing Benchmark Challenge.