On micro‐defect induced cracking in very high cycle fatigue regime

Abstract

It is known that fine granular area morphology is often formed around interior micro-defect in the case of very high cycle fatigue failure of high strength steel.The mechanism of "fragmentation of martensitic laths and formation of dislocation cells" (Zhu's model), which revealed a microstructure-dependent crack initiation and early growth for micro-defect induced internal fatigue cracking, was discussed in several lathy martensitic or similar steels, verified under environmental fatigue, and applicable to white etching crack in rotating contact fatigue. A machine learning-Z parameter integrated approach was developed for fatigue life, which is promising for design against fatigue of engineering structures for long life.

Formation of fine granular area in a non‐defect matrix of austenitic stainless steel during very high cycle fatigue

“…A fatigue crack initiation origin with an FGA near the sample surface has been observed. Different from earlier studies, 4–8,15–21 the fatigue crack initiation origin with an FGA observed here occurred in the matrix without any defect. This means that the fatigue damage and consequently the formation of fatigue crack initiation origin can mainly be related to the material behavior.…”

“…This mechanism provides a confirmation of the interesting models on the formation of FGA at a subsurface inclusion recently 7,15,19,21,45 . However, the current study provides a fundamental understanding of how material damage and crack initiation occur in a material matrix during VHCF.…”

“…The FGA formations previously studied 1,4–8,15–21 are mainly related to the materials with either inclusion, multi‐phase, or structure inhomogeneity. This will cause localized stress concentration at these defects and material damage during cyclic loading even with stress lower than the yield strength of the material and leads to the formation of FGA 7,15,19,21 . For a single‐phase material like the austenitic stainless steel with a homogeneous structure; however, it is difficult to use the earlier theory or models to give an explanation on the formation of FGA in the material matrix without a defect.…”

“…The local grain refinement leads to a sufficient decrease in the local threshold value, and a crack can initiate and propagate in the fine‐grained volume 15,19,20 . Another similar model is the fragmentation of martensitic laths and the formation of dislocation cells 21 . FGA phenomena observed and studied so far are mainly correlated to inclusion/defect initiations.…”

Application of machine learning methods in multiaxial fatigue life prediction

“…Gan et al 16 applied a variant of the ANN model, called extreme learning machine, for fatigue life prediction considering the mean stress effect. Zhu et al 17 used an ML‐based method combined with a physics‐based parameter to evaluate the fatigue life of four materials in the very high‐cycle fatigue regime. Nashed et al 18 developed a probabilistic neural network (PNN) and evaluated it using case studies available in the literature.…”