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Colloquium | Materials Science Division

Coupling Microstructure-Sensitive Modeling and In Situ Experiments to Understand and Predict Fatigue Behavior: Toward the Rapid Qualification of Additive Manufactured Materials

Materials Science Colloquium

Abstract: The benefits of additive manufacturing have been well documented, but before these materials can be used in critical applications, the mechanisms for fatigue failure must be identified and the life of these materials must be determined for use in a design context. Traditionally, the qualification of these materials is completed based on a time- and cost-intensive, trial-and-error testing program, which jeopardizes the implementation of new materials into service.

In this work, the fatigue behavior of selective laser melting (SLM) IN718, 718Plus, and Ti-6Al-4V is investigated through detailed characterization and modeling efforts, to accelerate the qualification of these materials through simulation-based predictions of material performance. Advanced characterization is used to identify the unique defects and microstructural features produced by SLM of the various materials at the micron and sub-micron scales.  Afterward, many sets of simulations are created based on different combinations of the microstructure that faithfully represent the distributions observed in the SLM materials. Specifically, the simulations track the evolution of accumulated strain and dissipated energy relative to the microstructural features with applied cyclic loading, and as these values reach a critical value, crack initiation is predicted. Additionally, in situ loading is used to identify the strain evolution in these materials through high-energy X-ray diffraction (HEDM) and digital image correlation.

For experimental validation, these in situ HEDM techniques are directly compared with simulated mirror replicates of the experiments, representing the exact same microstructure, in order to measure the point-by-point evolution of material damage over time and compare directly with the simulation results. The fatigue modeling framework is combined with uncertainty quantification and propagation efforts of the model’s readiness level, to build trust in the predictive capabilities of the model. With the time remaining, other examples of coupling microstructure-sensitive modeling and in situ experiments are discussed in the context of understanding plasticity and failure.

Bio: Michael D. Sangid received his B.S., M.S., and Ph.D. in mechanical engineering from the University of Illinois at Urbana-Champaign. He works on building computational materials models for failure of structural materials with experimental validation efforts focused at characterization of the stress/strain evolution at the microstructural scale during in situ loading.

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