Descriptor extraction on inherent creep strength of carbon steel by exhaustive search

Abstract

The alloying elements that control the creep rupture life of low carbon steel in the region of inherent creep strength were investigated by a data-driven model selection method. The experimental data published in NRIM Creep Data Sheet No. 7B was used. A model in which the Larson–Miller parameter is expressed as a linear sum of the logarithmic stress and the chemical composition of alloying elements was proposed on the basis of the analysis of the experimental data. There were 1023 ($${ = 2^{10}} - 1$$) candidate models containing one or more of the 10 alloying elements, and they were compared using two model selection methods: 10-fold cross validation (CV) and posterior probability based on Bayesian inference. The 10-fold CV suggested that Mo must be included in the model to improve the predictivity of the unknown data. A comparison among the Bayesian posterior probabilities of the candidate models showed that the Mo-only model has a predominantly high posterior probability of 79.76% compared with the other models. Furthermore, other element terms that co-occur with Mo evenly appeared in the top list after the Mo-only model, indicating that only Mo is crucial for increasing the posterior probability. From these results, it is concluded that the Mo-only model is the most appropriate as a data generation model. The selected Mo-only model was confirmed to have high predictivity of the creep life.

Keywords

• creep rupture
• inherent creep strength
• carbon steel
• larson–miller parameter
• bayesian inference
• exhaustive search
• model selection
• linear regression