Evaluation of Steel Cleanliness by Extreme Value Statistics
Nonmetallic inclusions, primarily oxides play a significant role in the fatigue performance of components such as bearings and gears that undergo fatigue loading. This leads to an increased demand for cleaner steel for longer-life applications. Due to the advances made in steel making processes in the past decades, the oxygen level as well as inclusion size and distribution have been brought under remarkable control enabling production of high-quality steel. Consequently, the earlier inclusion rating methods such as ASTM A534, ASTM E-45 that use a comparison with standard micrographs are insufficient to render an effective comparison of cleanliness of steels from different heats or suppliers, especially for the cleaner heats. It thus becomes imperative to find a reliable method to predict the size of the largest inclusions present in a steel volume and to further correlate it with the fatigue limit of steel. This is another limitation of the existing inclusion rating methods. Extreme value analysis is a method that can surmount these limitations and it comprises of examination of a small area of steel by Optical or Scanning Electron Microscopy to predict the maximum size of inclusions which may inhabit a larger volume of steel.
In this work, the effect of inclusion size distribution on fatigue performance is investigated from the experimental data obtained using ultrasonic fatigue testing. Extreme value analysis is used to predict the characteristic size of the largest inclusion based on the metallographic observations on polished surfaces and this inclusion size is then correlated with the fatigue limit measured by ultrasonic fatigue testing, making use of the Murakami approach.
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