Phase mapping of 2xxx‐series aluminium alloys by scanning precession electron diffraction

Abstract

2xxx‐series Al alloys are Cu containing age‐hardenable alloys, which are strengthened by numerous metastable precipitates formed during heat treatment. Many different precipitates exist, some of which are not well defined crystallographically. However, phases known to contribute particularly to strengthening and that exist in the over‐aged condition are: θ' and T1 in Al‐Cu‐Li alloys [1], and Ω and S in Al‐Mg‐Cu‐Ag alloys [1]. These precipitates have various morphologies, ranging from long needles to thin plates, and coexist with inclusion particles as well as with dispersoids. The resulting microstructure is complex both in terms of coexistence and by precipitates deviating from simply defined phases. This makes complete characterization a demanding task for which techniques are required to enable statistical treatment of precipitate distributions in terms of their atomic structure. Here, we apply scanning precession electron diffraction (SPED) to heat‐treated Al‐Cu‐Li and Al‐Mg‐Cu‐Ag alloys, shedding light on the distribution of phases present and the complex interplay between these microstructural features. 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 it below. In this way, integrated diffraction intensities are recorded in the geometry of a conventional electron diffraction pattern [2]. A 4D dataset is obtained comprising a 2D PED pattern at each position in the 2D scan region. Combined with subsequent data processing, this constitutes a powerful method for extracting valuable crystallographic information and orientation relationships in complex multiphase materials [3]. In this work, SPED was performed using a NanoMEGAS DigiSTAR scan generator fitted to a JEOL 2100F FEG‐(S)TEM operated at 200 kV, with a precession angle of 1º and a step size of 4.5 nm. Typical datasets comprised 90 000 diffraction patterns (DPs), which were analysed using the open source platform HyperSpy [4] as described below. Obtained results from an Al‐Cu‐Li alloy are shown here as an example. All DPs in the SPED dataset were first summed (Fig. 1) and compared to a simulated DP, including the Al‐matrix in the [001] orientation and the aforementioned θ'‐ and T1 precipitates (Fig. 2). This allowed identification of reflections associated with these particular phases. These phases are then visualised in ‘virtual dark‐field’ (VDF) images, formed by plotting the intensity in pixels around selected reflections as a function of probe position (Fig. 3 and 4). For example, the thin T1‐precipitate plates are seen on {111} Al planes inclined relative to [001] Al , and it is noted that even overlapping plates can still be discerned and visualized. The obtained VDF images exhibit a sharper, more consistent contrast between precipitate phases and the Al matrix as compared conventional imaging techniques, such as dark‐field TEM. More sophisticated analysis applies machine learning in order to identify the main component patterns in the data, as well as their spatial localisation referred to as ‘loading maps’. These ‘loading maps’ look similar to VDF images but are obtained by an automated and objective approach, requiring little or no prior knowledge. This opens the possibility of identifying features with unexpected crystallographic structures. The analysis approaches demonstrated in this work offer important insight into the complex microstructures of these Al alloys.