Abstract
In this study, we investigated the fatigue behavior of Q460 welded joints using tensile fatigue tests. Furthermore, real-time temperature profiles of the examined specimens were recorded by infrared thermography. Based on the obtained thermographic data, we calculated the entropy production rate of the specimens under different stress amplitudes. Hypothetically, the entropy production during high-cycle fatigue (HCF) could be divided into two parts. The first is induced by inelastic behavior that corresponds to damage accumulation, and the second originates from anelasticity associated with recoverable non-damaging microstructural motions. The turning point of entropy production under different stress levels represents an index for fatigue limit estimation. Then, considering the average damage threshold that exists during HCF, the entropy production related to damage accumulation (cumulative damage entropy) is obtained by testing three specimens under the same stress amplitude above the fatigue limit. Finally, a rapid three-parameter S-N curve with a survival probability rate of 50% is obtained. Then, combined with the maximum likelihood method, the 5% and 95% survival probability rate S-N curves are established. Most of experimental data are distributed in the area between S-N curves that correspond to 5% and 95% survival probability rate, indicating good accordance with the test data.
Highlights
Fatigue fracture is a primary failure of mechanical structures during their service life [1,2,3,4]
The entropy production rate per unit time under under different stress amplitudes and the tested data of three specimens can be used to different stress levels was calculated using the obtained thermographic data
The results exhibit that the entropy production rate slowly increases under a stress amplitude lower than the fatigue limit
Summary
Fatigue fracture is a primary failure of mechanical structures during their service life [1,2,3,4]. Many catastrophic accidents were caused by structural fatigue fractures, generating severe economic losses [5]. A reliable prediction of fatigue performance is of crucial significance in engineering applications. Traditional fatigue evaluation methods based on extensive experimental data are time-consuming and tedious, so alternatives that shorten test duration and realize a rapid fatigue reliability evaluation of mechanical components during high-cycle fatigue (HCF) are required
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