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TECHNICAL PAPERS: Gas Turbines: Manufacturing, Materials & Metallurgy

Remaining Life Prediction of Thermal Barrier Coatings Based on Photoluminescence Piezospectroscopy Measurements

[+] Author and Article Information
Mei Wen, Maurice Gell

Department of Metallurgy and Materials Engineering, University of Connecticut, Storrs, CT 06269

Eric H. Jordan1

Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269Jordan@engr.uconn.edu

1

To whom correspondence should be addressed.

J. Eng. Gas Turbines Power 128(3), 610-616 (Oct 04, 2005) (7 pages) doi:10.1115/1.2135820 History: Received November 19, 2004; Revised October 04, 2005

Nondestructive determination of the remaining life of thermal barrier coatings (TBCs) is highly desirable for components removed from service engines. Remaining life predictions for EB-PVD/Pt-Al TBCs cycled at two temperatures (1151°C and 1121°C) were made based on the thermally grown oxide stresses measured by the photoluminescence piezospectroscopy technique without knowing the test temperature. The predictions were compared using regression methods and neural network methods. It was found that both methods produce accurate life remaining predictions, but the neural network methods were superior. The lowest root-mean-square (rms) error and maximum absolute error for the prediction was 6.1% and 8.2%, respectively. For a data set with a 48.7% rms spallation life variation about the mean, the prediction results obtained are highly encouraging.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

(a) Schematic illustration of the photostimulated luminescence piezospectroscopy technique, and (b) typical R1∕R2 fluorescence spectra for chromium-containing stress-free and stressed α-Al2O3

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Figure 2

Schematic flow chart of neural network (adopted from The Mathworks)

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Figure 3

Quadratic fit for stress versus life fraction for all samples

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Figure 4

Weibull plot of life fraction data for PLPS inspections at life fraction 25%, 50%, and 75% from master curve

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Figure 5

Cumulative distribution of life fraction data for PLPS inspections at life fraction 25%, 50%, and 75% from master curve

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Figure 6

Quadratic fit for peak area ratio versus life fraction for all samples

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Figure 7

Quadratic fit for standard deviation of stress versus life fraction for all samples

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Figure 8

(a) Smoothed stress, (b) first derivative of stress, and (c) second derivative of stress

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Figure 9

Stress versus life fraction for neural network method 2

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Figure 10

Comparison of predictions performance with inherent variation of sample lives

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