Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope

Abstract

The routine and unique determination of minor phases in microstructures is critical to materials science. In metallurgy alone, applications include alloy and process development and the understanding of degradation in service. We develop a correlative method, exploring superalloy microstructures which are examined in the scanning electron microscope (SEM) using simultaneous energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). This is performed at an appropriate length scale for characterisation of carbide phases, shape, size, location, and distribution. EDS and EBSD data are generated using two different physical processes, but each provide a signature of the material interacting with the incoming electron beam. Recent advances in post-processing, driven by 'big data' approaches, include use of principal component analysis (PCA). Components are subsequently characterised to assign labels to a mapped region. To provide physically meaningful signals, the principal components may be rotated to control the distribution of variance. In this work, we develop this method further through a weighted PCA approach. We use the EDS and EBSD signals concurrently, thereby labelling each region using both EDS (chemistry) and EBSD (crystal structure) information. This provides a new method of amplifying signal-to-noise for very small phases in mapped regions, especially where the EDS or EBSD signal is not unique enough alone for classification.

Quantitative Precipitate Classification and Grain Boundary Property Control in Co/Ni-Base Superalloys

“…We have previously discussed this effect and other PCA artifacts. [10] VI. CONCLUSIONS From the present study, we are able to draw the following conclusions:…”

“…These signals are the principal components of the data matrix. A VARIMAX rotation is performed on the principal components and corresponding (spatial) scores, as per Wilkinson et al [51] and with inclusion of the EDS signal as per McAuliffe et al [10] This rotation acts to maximize and equalize the variance of the principal components. We then enforce positivity on the rotated components, mimicking the statistics of experimentally measured EBSPs to obtain physically meaningful results.…”

“…A certain proportion of variance is assigned to EBSD information, and the remainder to EDS. As discussed further in our previous work, [10] this has the effect of selecting the effective interaction volume of our analysis pipeline. Biasing toward EBSD information gives a finer effective spatial resolution of principal component spatial scoring.…”

“…We have previously shown that these give very similar results, as the VARIMAX rotation applied to make the principal component EBSPs physical has a similar effect on the EDS spectra. [10] Presently we have elected to quantify the average spectrum for each label and quantified compositions using Bruker Esprit 2.1. Subsequently we numerically average the at pct chemistries of each phase by alloy and present in Figure 6 trends in phase chemistry across the alloy series.…”

“…Intergranular precipitates are quickly and accurately classified using a correlative electron backscatter diffraction (EBSD)/energy dispersive X-ray spectroscopy (EDS) approach. [10] We examine the robustness of the classification, present chemical statistics as a function of precipitate structure, and compare our observations to thermodynamic modeling. We also briefly investigate the effect of intergranular phase morphology on grain boundary control.…”