Understanding geometrical size effect on fatigue life of A588 steel using a machine learning approach

Abstract

• The gauge length and width have more influence on the fatigue life than the thickness when specimen sizes are reduced. • The GBDT algorithm could predict fatigue life more accurately. • The GBDT algorithm is reliable for life prediction of thinner specimen and other materials. In this paper, both experimental and machine learning results show that the fatigue life of A588 steel specimens with different gauge lengths and widths varies more greatly compared with that of the specimens with different thicknesses as the gauge dimensions are reduced from 15 mm to 1.5 mm. The optimal machine learning algorithm is derived to predict the fatigue life of specimens with a thickness of 1 mm, and the predicted results are verified by the fatigue experiments.