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The maximum inclusion size in clean steels influences fatigue behaviour and other mechanical properties.
The maximum inclusion size controls fatigue behaviour and other mechanical properties.
The maximum inclusion size in the critical volume of a specimen predicted by the extremal statistics is 7.9 μm.
Predicting the maximum inclusion size in a large volume of clean steel from observations on a small volume is a key problem facing the steel industry.
If the predicted maximum inclusion size is plotted against increasing volume of steel, there is a continuous increase for the log-normal extrapolation but for the Generalized Pareto Distribution method the curve tends towards an upper limit.
At a given probability, the predicted fatigue strength is the highest by the estimated maximum inclusion size using the GEV distribution, then followed by using Gumbel distribution and by using GP distribution, and the lowest by using EXPGP distribution, which is inversely proportional to the evaluated maximum inclusion sizes.
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This allows data on inclusion sizes in small samples of steel to be used to predict the size of the maximum inclusion in a large volume of steel, a parameter of importance to steel users.
The Generalized Pareto Distribution (GPD) method has recently been applied to the estimation of the characteristic size of the maximum inclusion in clean steels for the first time.
We prove the existence of an optimal inclusion size.
Confidence intervals for the fatigue limit distribution, inclusion size and the inclusion intensity are calculated.
Mass inclusion size, shape, and placement were varied.
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