The Wilshire equation is a recent addition to the literature on safe life prediction. While the effect of temperature on creep life is reasonably understood, the effect of stress isn't. The Wilshire equation deals with this by partitioning over sub ranges of stress, but this approximation can lead to poor life time predictions. This paper introduces a semi-parametric procedure that allows the data itself to identify the stress relationship. When applied to 2.25Cr-1Mo steel it was found that the stress relationship is non-linear, and this semi-parametric version of the Wilshire model had better predictive performance compared to any partitioned Wilshire model. This approach contains a limit to valid extrapolation and the isothermal predictions for creep life have a more realistic pattern of behaviour.
ArrheniusS.Über die Dissociationswärme und den Einfluss der Temperatur auf den Dissociationsgrad der Elektrolyte. Zeitschrift für physikalische Chemie. 1889;4:96–116.
2.
IUPAC. 1997. Compendium of chemical terminology. 2nd ed. (the “Gold Book”). Compiled by A. D. McNaught and A. Wilkinson. Oxford: Blackwell Scientific Publications.
KlingerDJ.Humidity acceleration factor for plastic packaged electronic devices. Qual Reliab Eng Int. 1991;7:365–370. doi: 10.1002/qre.4680070505
5.
NelsonW.Accelerated testing: statistical models, test plans and data analyses, page 100. New York: Wiley; 1990.
6.
EyringH.Basic chemical kinetics. New York: Wiley; 1980.
7.
MonkmanFC, GrantNJ.An empirical relationship between rupture life and minimum creep rate in creep-rupture tests. Proc Am Soc Test Mater. 1956;56:593.
8.
TruninII, GolobovaNG, LoginovEA.New method of extrapolation of creep test and long time strength results. In: Proceedings of 4th International Symposium on Heat-resistant Metallic Materials, Mala Fatravol, 168, CSSR; 1971.
9.
LarsonFR, J.A. Miller: time-temperature relationship for rupture and creep stresses. Trans ASME. 1952;74:765–775.
10.
OrrLR, SherbyOD, DornJE.Correlation of rupture data for metals at elevated temperatures. Trans ASME. 1954;46:113.
11.
WilshireB, BattenboughAJ.Creep and creep fracture of polycrystalline copper. Mater Sci Eng A. 2007;443:156–166. doi: 10.1016/j.msea.2006.08.094
12.
YangM, WangQ, SongXL, Rationalization of creep data of creep-resistant steels on the basis of the new power law creep equation. Metall Mater Trans A. 2016;48(7):3479–3487.
13.
MansonSS, MuraldihanU.Analysis of creep rupture data for five multi heat alloys by the Minimum Commitment method. Research project 638-1, EPRI CS-3171; 1983.
14.
MansonSS, HaferdAM.A linear time-temperature relation for extrapolation of creep and stress rupture data. NACA TN 2890; 1953.
15.
MansonSS, BrownWF.Time-temperature stress relations for rupture and creep stresses. Proc ASTM. 1953;53:683–719.
16.
BoltonJ.Reliable analysis and extrapolation of creep rupture data. Int J Press Vessels Pip. 2017;157:1–19. doi: 10.1016/j.ijpvp.2017.08.001
17.
National Institute of Materials Science of Japan. Creep datasheets in MatNavi database. www.nims.go.jp/matnavi.
18.
National Institute for Materials Science, Creep Data Sheet No. 3b: Data sheets on the elevated-temperature properties of 2.25Cr-1Mo steel, 1986.
19.
WilshireB, ScharningPJ.Extrapolation of creep life data for 1Cr-0.5Mo steel. Int J Press Vessels Pip. 2008;85:739–743. doi: 10.1016/j.ijpvp.2008.04.002
20.
WhittakerMT, EvansM, WilshireB.Long-term creep data prediction for type 316H stainless steel. Mater Sci Eng A. 2012;552:145–150. doi: 10.1016/j.msea.2012.05.023
21.
AbdallahaZ, PerkinsK, WilliamsS.Advances in the Wilshire extrapolation technique – Full creep curve representation for the aerospace alloy Titanium 834. Mater Sci Eng: A. 2012;550(30):176–182. doi: 10.1016/j.msea.2012.04.054
22.
WilshireB, WhittakerMT.Long term creep life prediction for Grade 22 (2·25Cr-1Mo) steels. Mater Sci Technol. 2011;27(3):642–647. doi: 10.1179/026708310X520529
23.
NadarayaEA.On nonparametric estimates of density functions and regression curves. Theory Probab Appl. 1965;10:186–190. doi: 10.1137/1110024
24.
SilvermanBW.Density estimation for statistics and data Analysis. London: Chapman & Hall; 1992.
25.
HallP, TurlachB.Reducing bias in curve estimation by the use of weights. Comput Stat Data Anal. 1999;30:67–86. doi: 10.1016/S0167-9473(98)00081-4
26.
ClevelandWS, DevlinSJ.Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc. 1988;83:596–610. doi: 10.1080/01621459.1988.10478639
