Simple formula learned via machine learning for creep rupture life prediction of high-temperature titanium alloys

Abstract

Creep behavior of high-temperature titanium alloys determines their service life and thereby prediction of creep rupture life is in demand due to the costly and time-consuming measurements. Machine learning (ML) has been employed to build surrogate models for creep rupture life; however, the commonly used algorithms are often black-box due to the pursuit of high accuracy. Here, we first show that multiple linear regression (MLR) can result in models or formulae with much higher accuracy than those based on typical black-box methods. Furthermore, by combining feature selection and symbolic regression, we obtain a simple and unified formula for accurately predicting the creep rupture life of high-temperature titanium alloys. The formula is learned using the short-term creep rupture life data and consists of merely three attributes, that is, the Molybdenum equivalent, the test stress and test temperature. It outperforms the MLR models and generalizes well to different testing data with varying long-term creep rupture life. The simple formula can be readily applied to new titanium alloys for predicting the creep rupture life and is easily accessible to experimentalists in the materials community.

Keywords

Machine learning / symbolic regression / creep rupture life / high-temperature titanium alloys