4.7 Article

Examining metrics for fatigue life predictions of additively manufactured IN718 via crystal plasticity modeling including the role of simulation volume and microstructural constraints

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ELSEVIER SCIENCE SA
DOI: 10.1016/j.msea.2020.139312

关键词

Selective laser melting; Microstructure-sensitive modeling; Fatigue crack initiation; High cycle fatigue (HCF); Boundary conditions; Microstructure size

资金

  1. DARPA [N66001-14-1-4041]

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There is a growing demand for the development of precise, accurate, and industrially applicable predictive models for evaluating the fatigue performance of additively manufactured (AM) materials to accelerate their qualification. Six fatigue metrics based on microstructure-sensitive crystal plasticity finite element (CPFE) simulations are utilized to assess the fatigue performance of Inconel 718 produced via selective laser melting (an AM technique). Each metric is used to predict the probable locations of failure and the scatter in fatigue life under high cycle fatigue loading conditions. The predicted scatter from all the fatigue metrics was in good agreement with the experimental test data. The predictions locations of failure using two of the metrics, plastic strain accumulation, and plastic strain energy density, were found to correlate in a statistical sense with the post mortem fractography results. Moreover, an additional set of CPFE simulations were performed with varying volumes of the input microstructures, with the largest microstructure having a volume close to that of the test specimen's gauge section. This additional analysis was intended to provide informed guidelines for simulation volume that is both computationally tractable, and results in consistent scatter predictions, which was estimated to be a simulation volume consisting of similar to 200 grains. Lastly, fatigue life predictions obtained from traction-free and more realistic microstructure constraints were compared to understand the role of boundary conditions (BCs) in fatigue life predictions. This work is beneficial in determining the statistical minimum for safe-life analyses, thereby reducing the overall number of experimental tests and accelerating the qualification process.

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