It is important to be able to predict creep strains in aeroengine so as to enable small punch disc test results to be related to uniaxial creep test results using finite element models. The capability of the Wilshire equation to interpolate creep curves was assessed using creep tests on Waspaloy. This assessment required modifying the Wilshire equation for time to strains so that the parameters of this equation could be predicted as a function of strain in a way that did not allow predicted creep curves to double back on themselves. An artificial neural network was used to achieve this. It was found that the modified model was capable of interpolating the shape and the end points of the experimental creep curves. These modifications enabled the activation energy to be measured and it was found that the activation energy is dependent upon the average internal stress and thus strain.

Within the aerospace sector, the understanding and prediction of creep strains for materials used in high-temperature applications, such as Nickel-based super alloys, is imperative. Small punch testing offers the potential for understanding creep behavior using much less material than conventional uniaxial testing but in contrast to uniaxial creep tests, the stress in small punch creep (SPC) tests is multiaxial. SPC testing can be a valuable tool for validating models of creep deformation, but the key to unlocking its full capability is through the accurate correlation of the creep material properties measured through both techniques. As such, the focus of this paper is to correlate the creep behavior of Waspaloy obtained through conventional uniaxial testing to that obtained via small punch creep testing. Recently, and for low chrome steels, this has been achieved through use of the ksp method, but there are good reasons for believing this technique will not work so well for Nickel-based super alloys. This paper shows this to be the case for Waspaloy and proposes some alternative methods of correlation based on combining the Monkman–Grant relation and the Wilshire equations for both uniaxial and small punch creep. It was found that this latter approach enabled the accurate conversion of SPC minimum displacement rates to equivalent uniaxial minimum creep rates which, when combined with the Wilshire equations, enabled SPC test loads to be converted into equivalent uniaxial stresses (and visa versa) with levels of accuracy that were significantly reduced when compared to using the ksp method. Further, the random error associated with these conversions were dramatically increased.

Being able to accurately predict creep strain as a function of time is important both in preventing aeroengine blades rubbing against their outer casings, but also in being able to convert small punch test data into equivalent uniaxial test results using finite element models. Modern studies have found success in applying the Wilshire equations to uniaxial creep test results. The capability of the Wilshire equation to interpolate creep curves was assessed using uniaxial creep tests carried out on RR1000. In this paper, an artificial neural network (ANN) was used to modify the Wilshire equation for the time taken to reach various strains so that the parameters of the Wilshire equation could be interpolated as a function of strain. The model was then evaluated using statistics on predictive accuracy, which showed that the model was capable of predicting the shape and scale of the creep curves with high accuracy. These modifications also revealed that the activation energy is dependent upon the average internal stress and thus strain.