Fatigue Properties Estimation and Life Prediction for Steels under Axial, Torsional, and In‐Phase Loading

Abstract

In this study, several estimation methods of fatigue properties based on different monotonic mechanical parameters were first discussed. The advantages and disadvantages of the Hardness Method proposed by Roessle and Fatemi were investigated and improved through the analysis of a total of 92 fatigue test data. A new Segment Fitting Method from Brinell hardness was then proposed for the fatigue properties estimation, and a total of 96 pieces of fatigue test data under axial, torsional, and multiaxial in-phase loading were collected to verify the applicability of the new proposal. Finally, the prediction accuracy of the new proposal and three exciting estimation methods was compared with the predictions based on the experimental fatigue properties. Based on the results obtained, the newly proposed estimation method has a significant improvement on the relation between fatigue ductility coefficient and Brinell hardness, which consequently improves the fatigue life prediction accuracy with the scatter band of 2, particularly for the materials with low Brinell hardness. The present study can provide a simplified analysis of the preliminary fatigue design of engineering structures.

Failure analysis of an axle shaft in an airport ground support vehicle

“…27 In order to find fatigue strength coefficient ( σ f normal′), fatigue strength exponent ( b), fatigue ductility exponent ( ε f normal′) and fatigue ductility exponent ( c) entered as material data, Equations (12) were used by paying attention to H B values. 28 Equations (13) and (14) were used for cyclic strength coefficient ( K normal′) and cyclic strain hardening exponent ( n normal′). The parameters used in the ɛ - N curves created for seven parts of the axle shaft were given in Table 2. …”

A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning

“…4 Subsequently, Basquin proposed an empirical mathematical expression to calculate the number of cycles of fatigue failure by considering the elastic deformations. 5 Zhao et al 6 improved the fatigue life forecast accuracy of steel under axial, torsional, and in-phase loading by combining the Von-Mises equivalent strain parameters with the CM equation.…”

“…1. Classical [2][3][4]6 and previously developed ML models 12,13,15,[18][19][20][21] are constrained to predicting either creep life or fatigue life. This highlights the necessity for a comprehensive approach that addresses both types of failure.…”

“…2. The classical [2][3][4]6 and ML models 12,13,15,[18][19][20][21] have not adequately addressed the functional dependence of creep and fatigue life on both the chemical and microstructural features along with the physical features, underscoring the need for a threesome investigation.…”

“…Despite extensive efforts to develop reliable creep and fatigue life prediction models, the literature review points out several shortcomings in the existing classical and ML models: Classical 2–4,6 and previously developed ML models 12,13,15,18–21 are constrained to predicting either creep life or fatigue life. This highlights the necessity for a comprehensive approach that addresses both types of failure. The classical 2–4,6 and ML models 12,13,15,18–21 have not adequately addressed the functional dependence of creep and fatigue life on both the chemical and microstructural features along with the physical features, underscoring the need for a threesome investigation. The prediction reliability of past ML models is low due to lack of incorporating the feature importance analysis 12–15,17,19,20 The datasets utilized earlier had limited representation and required expansion, curation, and inclusion of hitherto unexplored data. …”