Load sequence effects in fatigue crack growth of thick-walled welded C–Mn steel members. (October 2015) - Maljaars, Johan; Pijpers, Richard; Slot, Henk
Highlights: Retardation is found in thick walled welded steel even with high weld stresses. Different load cycle sequences resulted in a factor 6 different fatigue lives. Simple models can reasonably predict sequence effects in welded steels. Abstract: Many welded steel structures in marine, offshore, and infrastructural industries are subjected to variable amplitude (VA) fatigue loads. It is well known that the level and sequence of the load cycles can cause crack growth retardation or acceleration and thus influence the fatigue life. An important sequence effect is generated by a large stress cycle followed by smaller stress cycles. Whereas the effect of single large stress cycles in a further constant amplitude (CA) load on central through cracks in thin-walled aluminium sheet is well established, studies into the effects of practical VA loads on cracks in thick-walled welded steel structures are less common. This paper presents the results of CA tests with large stress peaks and VA tests on 70 mm C–Mn steel butt welded 4-point bending specimens with crack growth in thickness direction. It is demonstrated that loading by a sequence of accelerating and subsequent decelerating stress cycles cause significant retardation of the crack growth and that the same stress cycles but placed in random sequence hardly result in retarded crack growth. The obtained crack growth versus number of cycles for as-welded and stress relieved specimens have been simulated using two relatively simple crack rate retardation models, being the well-known Willenborg model and the Space-state model developed by Ray and Patankar. The latter model is also used to simulate crack growth of semi-elliptical surface cracks in welded steel structures tested by others. The Space-state model is able to predict experimental results with reasonable to good accuracy. A proposal is put forward for future improvement of the model.