5. Discussions
Based on the results of the above four tests, it is shown that the
proposed model has improved the accuracy of predicting the remaining
life of fatigue loading to a considerable extent compared to the fatigue
driving stress model (K-R model). Miner’s rule has a simple application
in the form that fatigue damage at different levels can be summed
linearly, but fatigue failure in many metallic materials often exhibits
highly nonlinear damage behavior. The fatigue driving force model (K-R
model) improves on the inherent flaws of the linear damage accumulation
rule. It is a nonlinear model that requires only the S-N curve of the
material. In addition, it does not require many material property
parameters and takes into account the effects of the loading sequence.
The K-R model has improved predictions compared to Miner’s rule by
having a sum of damage values greater than 1 in the low-high loading
sequence and less than 1 in the high-low loading sequence. However, it
was found that loading interactions are also a part of the load history
and the K-R model does not consider loading interactions. Zhu’s model
considers loading interactions by adding the ratio of the two loading
levels, while Li’s model considers load interactions by adding a
pre-circulation factor. However, it was found that the improvement in
the predictive capability of the two modified K-R models relative to the
original model was limited, probably due to the complexity of the
inherent variable amplitude loading damage evolution, and the two
modified models did not provide an explanation in terms of fatigue
driving stress evolution. In this paper, based on the driving stress
evolution, a new driving stress evolution curve is obtained by taking
into account the load interactions, so that the original evolution curve
is shifted. Then, the fatigue driving stress equivalence was carried out
to obtain a new fatigue residual life prediction model. In particular,
compared with Miner’s rule, K-R
model, Zhu’s model, and Li’s model, the proposed model has similar
prediction ability under multi-stage loading, but for two-stage loading,
the proposed model has significantly higher prediction accuracy.