Using digital twins to accelerate qualification and certification of fatigue critical components

icaf2023 Tracking Number 33

Fatigue of engineering alloys is a major challenge in aerospace applications. Developing and qualifying fatigue critical components for use in aerospace requires manufacturing and testing of a significant number of test coupons. This process is highly time-consuming and expensive. QuesTek’s ICMD® modeling software can provide reliable property predictions that can significantly lower the amount of testing required while still yielding a robust material property dataset. Utilizing integrated computational materials engineering methodologies, QuesTek has been developing microstructure sensitive fatigue models that can account for both intrinsic (e.g., grain size, grain morphology, phase fractions, crystallographic texture, etc.) and extrinsic (e.g., inclusions, surface roughness, porosity, etc.) features that drive fatigue life in engineering alloys. QuesTek uses a fatigue modeling framework combining crystal plasticity finite element method to predict fatigue indicator parameters at the mesoscale, with microstructurally small crack propagation and physically long crack propagation algorithms, to predict the full fatigue life from incubation to ultimate fatigue failure in both high and low cycle fatigue. This approach depends on both microstructure characterization used to generate the inputs for a microstructure digital twin, as well as a select few experiments to calibrate the physics-based models. By simulating the majority of loading scenarios of interest, testing can be targeted to just the most informative experiments during qualification. This approach offers three key benefits when compared with traditional design of experiment approaches; (1) decreased cost of qualification, (2) decreased time for qualification, and (3) improved mechanistic understanding of the key driving features for fatigue failure in a given alloy system, enabling optimization and improvements. QuesTek has recently made major strides in improving this modeling approach by decreasing the computational cost of simulations, making the approach feasible on QuesTek’s cloud-based software, and by incorporating key microstructure features of interest for additively manufactured alloy such as anisotropic grain morphology/texture, porosity, and surface roughness. While this toolkit can be applied to traditionally manufactured alloys, it is particularly impactful for additively manufactured alloys due to their complex microstructures which result in difficult and expensive qualification processes when microstructure sensitive models are not utilized.