Bimodal grain structures are common in many\nalloys, arising from a number of different causes including\nincomplete recrystallization and abnormal grain growth.\nThese bimodal grain structures have important technological\nimplications, such as the well-known Goss texture\nwhich is now a cornerstone for electrical steels. Yet our\nability to detect bimodal grain distributions is largely\nconfined to brute force cross-sectional metallography. The\npresent study presents a new method for rapid detection of\nunusually large grains embedded in a sea of much finer\ngrains. Traditional X-ray diffraction-based grain size\nmeasurement techniques such as Scherrer, Williamsonââ?¬â??\nHall, or Warrenââ?¬â??Averbach rely on peak breadth and shape\nto extract information regarding the average crystallite\nsize. However, these line broadening techniques are not\nwell suited to identify a very small fraction of abnormally\nlarge grains. The present method utilizes statistically\nanomalous intensity spikes in the Bragg peak to identify\nregions where abnormally large grains are contributing to\ndiffraction. This needle-in-a-haystack technique is\ndemonstrated on a nanocrystalline Niââ?¬â??Fe alloy which has\nundergone fatigue-induced abnormal grain growth. In this\ndemonstration, the technique readily identifies a few large\ngrains that occupy \\0.00001 % of the interrogation volume.\nWhile the technique is demonstrated in the current\nstudy on nanocrystalline metal, it would likely apply to any\nbimodal polycrystal including ultrafine grained and fine\nmicrocrystalline materials with sufficiently distinct bimodal\ngrain statistics.
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