Accurate experimental characterization of the crack closure process is important for a number of reasons, the most important of which may be for its use in developing materials and structures that are resistant to the growth of fatigue cracks. This paper describes the results of an investigation aimed at quantifying the point of crack closure. An α/β titanium alloy, Ti-6242S, was chosen for this investigation because its microstructure can be manipulated to produce large variations in the level of asperity-induced crack closure and is thus an ideal model alloy for understanding this important crack closure mechanism. Comparisons of two “global” compliance techniques (backface strain and crack mouth opening displacement) and a near crack-tip surface technique (two-stage replication) were made. In tests conducted at low stress intensities, all three methods gave nearly identical measurements of crack closure. For the two compliance techniques, different numerical procedures were used to determine the inflection point in the load-displacement curve. The magnitude of and variations in crack closure determined using these compliance methods were found to be strongly dependent on the numerical procedure used. Studies of crack-tip opening showed that in α/β-processed Ti-6242S the crack tip is tightly closed even at positive loads, indicating that plasticity induced closure is the predominant form of closure. In contrast, measurements in β-processed material showed that the crack tip is propped open even when no load is applied to the sample, indicating that closure in this microstructure is asperity-induced.
This paper describes a methodology for statistically extrapolating a single measured service loading history to the expected long-term service usage spectra. The measured time history first is processed into a rainflow counted histogram. Nonparametric kernel smoothing techniques are employed to convert the rainflow histogram of cycles into a probability density histogram. Once the probability density histogram is obtained, Monte Carlo methods are used to produce a rainflow histogram of any desired number of cycles. A new loading history then is reconstructed from the expected rainflow histogram, which can be combined with a probabilistic fatigue analysis to obtain an estimate of the durability of a structure. Obtaining an estimate of the loading spectra for a ground vehicle is difficult because there are many users, each with different service usage. The extrapolating methodology is extended to combine data from several users to obtain loading spectra that represent more severe users in the population.
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