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TECHNICAL PAPERS: Gas Turbines: Structures and Dynamics

A Probabilistic Micromechanical Code for Predicting Fatigue Life Variability: Model Development and Application

[+] Author and Article Information
K. S. Chan, M. P. Enright

 Southwest Research Institute ®, 6220 Culebra Road, San Antonio, TX 78238

J. Eng. Gas Turbines Power 128(4), 889-895 (Sep 06, 2005) (7 pages) doi:10.1115/1.2180811 History: Received August 30, 2005; Revised September 06, 2005

This paper summarizes the development of a probabilistic micromechanical code for treating fatigue life variability resulting from material variations. Dubbed MICROFAVA (micromechanical fatigue variability), the code is based on a set of physics-based fatigue models that predict fatigue crack initiation life, fatigue crack growth life, fatigue limit, fatigue crack growth threshold, crack size at initiation, and fracture toughness. Using microstructure information as material input, the code is capable of predicting the average behavior and the confidence limits of the crack initiation and crack growth lives of structural alloys under LCF or HCF loading. This paper presents a summary of the development of the code and highlights applications of the model to predicting the effects of microstructure on the fatigue crack growth response and life variability of the α+β Ti-alloy Ti-6Al-4V.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematics of the probabilistic framework in MICROFAVA for modeling material-specific random variables

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Figure 6

Measured PDF for cycles-to-initiation compared against MICROFAVA model prediction based on measured grain-size distribution. From Chan (16).

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Figure 7

Comparison of model predictions against measured ΔKth for Ti-6Al-4V as a function of Kmax From Chan (9)

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Figure 14

Computed probability of fracture using MICROFAVA and DARWIN (16)

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Figure 13

Analysis of a Ti rotor design using MICROFAVA and DARWIN

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Figure 12

Comparison of computed and observed probability density of FCG lives based on microstructure-based computational model and experimental data da∕dN results (22-23) for R=0.1 at 24°C

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Figure 11

Comparison of predicted crack growth life values based on microstructure-based computational model and experimental data da∕dN results (22-23) for R=0.1 at 24°C(16)

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Figure 10

Comparison of computed crack growth rate (da∕dN) coefficient of variation with experimental data for R=0.1 at 24°C

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Figure 9

Comparison of computed crack growth rate predictions and probability density functions with experimental data for R=0.1 at 24°C

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Figure 8

Measured da∕dN data compared against model predictions based on a log-normal grain size distribution (16)

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Figure 5

A comparison of predicted and measured crack lengths versus fatigue cycles for the initiation (Ni) of one grain-sized crack and its growth (Ng) to failure. Experimental data are from (17). From Chan (16).

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Figure 4

Predicted crack initiation life, Ni, crack growth life, Ng, and total life, Nf, compared against observed failure life for Ti-6Al-4V. From Chan (16). Experimental data are from (17).

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Figure 3

Grain size of Ti-6Al-4V compared to the log normal distribution

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Figure 2

Microstructure of Ti-6Al-4V shows 60% primary α grain (light phase) and a 40% of α+β Widmanstatten colonies

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