Creep Life Prediction of 10CrMo9–10 Steel by Larson–Miller Model

Creep is defined as the permanent deformation of materials under the effect of sustained stress and elevated temperature for long periods of time, which can essentially lead to fracture. Due to very time-consuming and expensive testing requirements, existing experimental creep data are often analyzed using derived engineering parameters and models to predict and find the correlations between creep life (time to rupture), temperature and stress. The objective of this study was to analyze and compare different numerical algorithms by using the Larson–Miller parameter ([Formula: see text] extrapolation model. Calculations were performed using the classical [Formula: see text] equation where different values of parameter [Formula: see text] were selected, as well as using a modified [Formula: see text] equation in which parameter [Formula: see text] was stress dependent [Formula: see text]. The impact of two different approaches of extrapolation and correlation functions (linear and polynomial) applied to fit the [Formula: see text] model was also investigated. A detailed analysis was performed to choose the best extrapolation fit function and error tolerance. The numerical algorithm implemented was validated through creep rupture testing performed on 10CrMo9–10 steel at 600 °C (873 K) and 80 MPa. Creep model behavior analysis proved that different values of [Formula: see text] can significantly change the estimated time to rupture. An excellent response of the [Formula: see text] model was obtained by considering polynomial dependency when parameter [Formula: see text] was assumed to be 18, especially for the temperature range from 773 to 873 K. Promising results were also achieved when parameter [Formula: see text] was taken as stress-dependent, but only for linear fitting, which requires further analysis. However, at validation stage it turned out that only the linear extrapolation function and [Formula: see text] taken as a constant value provided adequate time-to-rupture prediction. In the case of [Formula: see text] = 18, estimated time was slightly overestimated (~8%) and for [Formula: see text] = 20 it was underestimated by 27%. In all other cases error largely exceeded 50%.