In the present work, we postulate that a critical value of the stored plastic strain energy density (SPSED) is associated with fatigue failure in metals and is independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress–strain hysteresis loops, we use crystal plasticity finite element simulations to compute the (local) SPSED at each material point within polycrystalline aggregates of a nickel-based superalloy. A Bayesian inference method is used to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and −1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a lognormal distribution. Moreover, for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude; this is indicative of the scatter observed in the fatigue experiments. Finally, the lognormal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.

Elastic interactions of atomic steps can greatly impact surface morphology. Recent atomistic calculations and experimental observations find the standard dipole model of steps is valid only for very large step separations. In this Letter, a new model is presented that displays remarkable agreement with atomistic predictions for step separations larger than just a few step heights. It is shown that the interaction energy of steps exhibits a novel intermediate-ranged behavior and that, for particular systems, step interactions switch from repulsive to attractive as separation distance decreases.

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Qualification of aerospace components is a long and costly process involving material properties, material specifications, manufacturing process, and design among others. Reducing qualification time and cost while maintaining safety offers a large economic advantage and enables faster response to the market demands. In 2012, DARPA established the Open Manufacturing program, a project to develop an integrated computational materials engineering (ICME) framework aimed at rapid qualification. Rapid qualification requires the integration of several technologies: materials, process, design, models, monitoring and control, non-destructive evaluation (NDE), testing, among others. A probabilistic design approach is adopted in the rapid qualification process to enable the integration of these technologies into a single risk-based function to optimize the design process. This approach directs the efforts to those areas that play the most important roles, potentially reducing specimen testing that will be required to develop material databases and design limits. New tests also will be required to validate and verify the ICME framework and develop a better understanding of the processing-microstructure-property relation and associated variability of the processing conditions. The probabilistic design approach is demonstrated for the rapid qualification of an actual aircraft engine component constructed via the powder-bed additive manufacturing process. This paper summarizes the probabilistic rapid qualification design approach and its application to this novel manufacturing process with the goal of reducing the overall qualification process time by 40 % and qualification process cost by 20 %.