Crystallographic mapping in engineering alloys by scanning precession electron diffraction
Authors: Johnstone Duncan N. , Knowles Alexander J. , Krakow Robert , Wenner Sigurd , van Helvoort Antonius T. J. , Holmestad Randi , Stone Howard , Rae Catherine , Midgley Paul A. 
Published: December 2016

Crystallographic, compositional and morphological complexity in modern engineering alloys necessitates the use of sophisticated tools for multi‐scale materials characterisation. Here, we develop scanning precession electron diffraction (SPED) for mapping crystalline phases in engineering alloys. SPED involves scanning the electron beam across the specimen and recording a PED pattern at each point by rocking a focused probe in a hollow cone above the specimen and de‐rocking the beam back to the optic axis below. In this way, integrated diffraction intensities are recorded in the geometry of a conventional electron diffraction pattern [1]. A 4D dataset is obtained comprising a 2D PED pattern at each position in the 2D scan region, which can be analysed in a number of ways. Most simply, ‘virtual diffraction images’ can be formed by plotting the intensity of a sub‐set of pixels in each PED pattern as a function of probe position to elucidate variations in the diffraction condition in a versatile post‐acquisition scheme. Phase and orientation maps can also be formed by matching each PED pattern to a library of simulated patterns [2]. Here, we use this approach to determine the phases of precipitates in a nickel base superalloy and to identify orientation relationships existing between these phases. To do this we explore the orientation data in disorientation space where the rotation axis and angle between the two crystallographic bases is plotted (Figure 1). This automated analysis enabled treatment of multiple precipitates yielding a more representative view of the microstructure compared to conventional SAED methods.

New methods for strain mapping and phase characterisation based on machine learning were developed as part of this work to extract further insight into microstructural features. Strain maps were obtained by comparing each pattern to an unstrained reference and used to explore the strain distribution between precipitates in aluminium alloys (Figure 2). These SPED based strain maps offer a greater field of view as compared to methods based on atomic resolution imaging whilst retaining nm‐scale spatial resolution. This yields unique insights such as the ability to map the interaction of strain fields associated with multiple precipitates, which can be seen in Figure 2. Phase characterisation, on the other hand, addresses the challenge of determining the chemistry and crystallography of phases in the microstructure that are often embedded and overlap in projection. We apply machine learning algorithms to SPED [3] and STEM‐EDX [4] data acquired from the same region to achieve a correlated crystallographic and chemical characterisation of a Ti‐Fe‐Mo alloy with a nanometre scale lamellar microstructure (Figure 3). This approach learns component signals (spectra or patterns), which make up the particular dataset, together with their associated loading at each real space pixel. An efficient representation of the data is therefore found with minimal prior knowledge and signals from overlapping crystals are separated to achieve phase specific characterisation. Combined, the analysis approaches developed in this work provide comprehensive 'crystal cartography' of engineering alloys paving the way to better understanding of relationships between processing, structure and properties.