The role of strength distributions for premature creep failure

Premature and unexpected creep damage is a significant concern in high-temperature engineering. Identifying outliers in creep rupture data is essential for assessing the risk of premature creep failure. This study proposes a new method to evaluate premature creep failure using log-logistic distribution fit of prediction errors and outlier positions. Fitting results for seven different alloys were obtained from extrapolation procedures using soft-constrained machine learning algorithms (SCMLAs) and constrained time-temperature parameters (TTPs) based on prior research. A comprehensive statistical analysis was conducted for all materials. The log-logistic distribution was validated as a suitable method for fitting prediction error distributions. Regression plots demonstrate effective residual balance and accurate outlier capture. The best fitting methods were identified based on the width of the distributions. Outlier positions were used to evaluate the probability of premature creep failure quantitatively. For example, a 0.5% probability of observing creep rupture strengths that are approximately 50% lower than the standardized creep stress was found for TP316H, T321H, and high Cr steels SUH616B. These findings offer valuable insights for estimating premature creep failure in materials.