A comparative study of creep-fatigue life prediction for complex geometrical specimens using supervised machine learning

Abstract

This study proposes a supervised machine learning approach to predict the creep-fatigue life of complex geometrical specimens. Seven different specimens were tested under creep-fatigue loading, and finite element analysis and test results showed that the stress distribution and life of the specimens were significantly influenced by the diameters and arrangement of holes. Characteristic parameters were proposed to describe the specimens' features, and support vector regression (SVR) and artificial neural network (ANN) methods were utilized to predict their life. The results indicate that both methods are effective in predicting the life of the specimens, with the ANN showing better performance when input data is limited. This study offers valuable insights into the leading factors behind the failure of complex geometrical specimens under creep-fatigue loading.