Abstract
The key microstructural feature providing strength to age-hardenable Al alloys is nanoscale precipitates. Alloy development requires a reliable statistical assessment of these precipitates, in order to link the microstructure with material properties. Here, it is demonstrated that scanning precession electron diffraction combined with computational analysis enable the semi-automated extraction of precipitate statistics in an Al-Mg-Si-Cu alloy. Among the main findings is the precipitate number density, which agrees well with a conventional method based on manual counting and measurements. By virtue of its data analysis objectivity, our methodology is therefore seen as an advantageous alternative to existing routines, offering reproducibility and efficiency in alloy statistics. Additional results include improved qualitative information on phase distributions. The developed procedure is generic and applicable to any material containing nanoscale precipitates.
Highlights
The number and type of the different phases forming throughout the Al matrix are crucially dependent on alloy composition and all prior thermo-mechanical processing
Scanning precession electron diffraction (SPED) was performed using a NanoMEGAS DigiSTAR scan generator fitted to a JEOL 2100F transmission electron microscopy (TEM) operated at 200 kV, with a precession angle of 1◦ and a step size of 2.28 nm
The main SPED dataset is presented as a virtual dark-field (VDF) image (Figure 1) formed by plotting the intensity of a sub-set of pixels in each PED pattern as a function of probe position
Summary
The number and type of the different phases forming throughout the Al matrix are crucially dependent on alloy composition and all prior thermo-mechanical processing. In order to further optimise material properties, detailed and reliable precipitate statistics and its change versus different ageing times, is needed This is generally acquired through bright-field (BF) and/or dark-field (DF) transmission electron microscopy (TEM) imaging techniques, with subsequent. With recent developments of TEM techniques yielding large, multi-dimensional datasets [7], and corresponding parallel advancements in powerful data processing tools [8], techniques for obtaining the statistics in more objective and reproducible manners are sought. This forms the main motivation behind this work
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