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Research Papers

Critical Review and Appraisal of Traditional and New Procedures for the Quantification of Creep Fracture Behavior Using 1Cr–1Mo–0.25V Steel

[+] Author and Article Information
M. Evans

School of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UKm.evans@swansea.ac.uk

J. Eng. Mater. Technol 131(2), 021011 (Mar 09, 2009) (14 pages) doi:10.1115/1.3078391 History: Received September 15, 2008; Revised December 11, 2008; Published March 09, 2009

The approaches traditionally used to quantify creep and creep fracture are critically assessed and reviewed in relation to a new approach proposed by Wilshire and Scharning. The characteristics, limitations, and predictive accuracies of these models are illustrated by reference to information openly available for the bainitic 1Cr–1Mo–0.25V steel. When applied to this comprehensive long-term data set, the estimated 100,000–300,000 h strength obtained from the older so called traditional methods varied considerably. Further, the isothermal predictions from these models became very unstable beyond 100,000 h. In contrast, normalizing the applied stress through an appropriate ultimate tensile strength value not only reduced the melt to melt scatter in rupture life, but also the 100,000 h strengths determined from this model for this large scale test program are predicted very accurately by extrapolation of creep life measurements lasting less than 5000 h. The approach therefore offers the potential for reducing the scale and cost of current procedures for acquisition of long-term engineering design data.

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Copyright © 2009 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Dependence of time to failure on minimum creep rate at 773–923 K for multiple batches of 1Cr–1Mo–0.25V steel (9)

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Figure 2

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel using the Dorn–Shepherd model (15). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using the Dorn–Shepherd (15) model of Eq. 2.

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Figure 8

(a) Dependence of ln[tF exp(Qc/RT)] on ln[−ln(t/tTS)] for 1Cr–1Mo–0.25V at 723–948 K with Qc=300 kJ mol−1. (b) Actual v prediction plot for 1Cr–1Mo–0.25V steel using the Wilshire–Scharning method (7-9). (c) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 773–923 K (9), compared with predicted curves derived using the using the Wilshire–Scharning method (7-8) of Eq. 9.

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Figure 3

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel sing the Larson–Miller model (16). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using the Larson–Miller (16) model of Eq. 3.

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Figure 4

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel using Soviet Model 1 (17). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using Soviet Model 1 (17) of Eq. 5.

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Figure 5

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel using Soviet Model 2 (17). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using the using Soviet Model 2 (17) of Eq. 5.

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Figure 6

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel using the minimum commitment method (18). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using the using the minimum commitment method (18) of Eq. 6.

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Figure 7

(a) Actual v prediction plot for 1Cr–1Mo–0.25V steel using the Manson–Haferd method (19). (b) Multibatch stress rupture data for 1Cr–1Mo–0.25V steel at 723–948 K (9), compared with predicted curves derived using the using the Manson–Haferd (19) method of Eq. 7.

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