Abstract
This paper proposes a novel method to establish and identify a probability density function characterizing the fatigue lifetime. The method is initiated with a quantitative analysis of the microstructure of the material, which provides the initial probability distribution of defects. After identifying a given probability density function of defects, one can transport it into a lifetime probability density function using a growth law involving a measure of the loading over a cycle. Several parameters of the growth law are finally estimated from a given set of fatigue experiments on specimens and several techniques are discussed. The method is applied on real defect observations and lifetime data. The estimated lifetimes using the novel technique is of similar quality with standard estimation providing the probability density function of lifetime as an additional output. This output can be used directly as an input in a stress–strength interference method.
Highlights
The search for performance in lightweight and environmentally-friendly structures leads automotive companies to choose aluminum alloys as the preferred material for engine parts like pistons, cylinder blocks and cylinder heads
A series of fatigue criteria have been identified from an experimental thermomechanical fatigue (TMF) database on over-aged specimens
The results expressed in terms of estimated versus experimental fatigue lifetime show that the initial choice of probability density function for the pore-size has a negligible impact on the final
Summary
The search for performance in lightweight and environmentally-friendly structures leads automotive companies to choose aluminum alloys as the preferred material for engine parts like pistons, cylinder blocks and cylinder heads. Residual porosity and inclusions (intermetallics, oxides), formed during the degradation of the polymeric pattern [9,10], are increased and clustered Even if these phenomena do not reduce the overall mechanical properties of the material, they have an important impact on lifetime of components during in-service loadings. In the second part, starting from the distributions of defects a fatigue lifetime prediction model is proposed The model includes both micro-initiation and micro-propagation and provides both a standard fatigue life estimation and a probability density as a function of a damage parameter and the number of cycles to failure. Over-aging corresponds to a stabilization of both mechanical properties and microstructure under a long term exposure of the material to high temperature. All tests were conducted with a mechanical strain rate of e_ 1⁄4 10À3 sÀ1
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