Detecting rare, abnormally large grains by x-ray diffraction

被引:10
作者
Boyce, B. L. [1 ]
Furnish, T. A. [1 ]
Padilla, H. A., II [1 ]
Van Campen, D. [2 ]
Mehta, A. [2 ]
机构
[1] Sandia Natl Labs, Mat Sci & Engn Ctr, Albuquerque, NM 87185 USA
[2] Stanford Synchrotron Radiat Lightsource, Menlo Pk, CA USA
基金
美国能源部;
关键词
NANOCRYSTALLINE NI; FATIGUE BEHAVIOR; IN-SITU; GROWTH; SIZE; EVOLUTION; COPPER; TEMPERATURE; KINETICS; STRESS;
D O I
10.1007/s10853-015-9226-3
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Bimodal grain structures are common in many alloys, arising from a number of different causes including incomplete recrystallization and abnormal grain growth. These bimodal grain structures have important technological implications, such as the well-known Goss texture which is now a cornerstone for electrical steels. Yet our ability to detect bimodal grain distributions is largely confined to brute force cross-sectional metallography. The present study presents a new method for rapid detection of unusually large grains embedded in a sea of much finer grains. Traditional X-ray diffraction-based grain size measurement techniques such as Scherrer, Williamson-Hall, or Warren-Averbach rely on peak breadth and shape to extract information regarding the average crystallite size. However, these line broadening techniques are not well suited to identify a very small fraction of abnormally large grains. The present method utilizes statistically anomalous intensity spikes in the Bragg peak to identify regions where abnormally large grains are contributing to diffraction. This needle-in-a-haystack technique is demonstrated on a nanocrystalline Ni-Fe alloy which has undergone fatigue-induced abnormal grain growth. In this demonstration, the technique readily identifies a few large grains that occupy < 0.00001 % of the interrogation volume. While the technique is demonstrated in the current study on nanocrystalline metal, it would likely apply to any bimodal polycrystal including ultrafine grained and fine microcrystalline materials with sufficiently distinct bimodal grain statistics.
引用
收藏
页码:6719 / 6729
页数:11
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