Microstructural-based physics of failure models to predict fatigue reliability

Tryon, Robert; Dey, Animesh; Krishnan, Ganapathi; Zhao, Yaowu

doi:10.1109/rams.2006.1677426

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Prediction of the inherent variability of fatigue life, in particular, the lower bound of the distribution of behaviour requires an understanding of the microstructural mechanisms that lead to early crack nucleation. Other researchers have developed methods and demonstrated approaches for predicting fatigue life variability that include microstructure sensitivity . Additionally, recent studies have demonstrated the ability to predict minimum life behaviour of several turbine engine titanium alloys and nickel‐base superalloys using key microstructural feature distributions and small crack growth variability .…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of minimum fatigue life behaviour in α + β titanium alloys

Golden

Whitney-Rawls²,

Jha

et al. 2018

Fatigue Fract Eng Mat Struct

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The objective of this work was to develop and demonstrate a probabilistic life prediction method for the prediction of minimum fatigue lives that are typically used in the design of fracture critical rotating turbine engine components. A Monte Carlo analysis was used to predict the variability in fatigue lives based on the distribution of microstructural features that lead to early crack initiation as well as the variability in small fatigue crack growth rates. Two titanium alloys, both with bimodal microstructures, were tested and analysed in this study. The distribution of critical microstructural features was calibrated based on test results and understanding of microstructure neighbourhood effects. Testing was conducted on both alloys and included both smooth and notched specimens. The predictions are presented and compared with the data for smooth and notch geometries for the various loading conditions. A parametric study was performed to identify the importance of several model inputs and to identify areas for future improvement.

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Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of minimum fatigue life behaviour in α + β titanium alloys

Golden

Whitney-Rawls²,

Jha

et al. 2018

Fatigue Fract Eng Mat Struct

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“…These models are based on observed damage mechanisms and, therefore, predictions of design fatigue limits can be expected to be more accurate than data based statistical estimations. Examples of physics-based probabilistic fatigue life prediction models include the approaches proposed by Magnusen et al [2], Chan et al [3], Tryon et al [4], Laz et al [5], and Jha et al [6][7]. Jha has shown that materials often exhibit a dual failure mode that is stress dependent that is often only observable when fatigue testing is conducted with large sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Investigation of variability in fatigue crack nucleation and propagation in alpha+beta Ti-6Al-4V

Golden

John

Porter

2010

Procedia Engineering

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The understanding of fatigue variability in turbine engine materials is vital to permit a reduction of US Air Force sustainment costs through fatigue life extension and/or inspection interval extension. Additionally, the US Air Force is currently moving further towards the use of probabilistic damage tolerance design methods. These probabilistic models require not only a good understanding of the variability in specimen data, but also an understanding of the microstructural sources of variability to allow scaling to component analysis. The objective of this work was to study the fatigue variability of a common turbine engine alloy Ti-6Al-4V. Typical testing consisted of smooth bar fatigue tests at multiple stress ratios and stress levels in order to generate a fully populated stress-life curve. These tests, however, typically do not consist of many repeats. The approach of this work was to conduct a statistically significant number of repeated fatigue tests at several loading conditions. A similar approach has been performed on several other turbine engine material systems often revealing bimodal life distributions consisting of a number of low life specimens that may fail due to a separate mechanism. This paper discusses the Ti-6Al-4V life distributions and sources of variability. Crack propagation using small crack growth data was used to predict the lower tail of the life distributions.

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