Phase Analysis and Identification of Particles in a Complicated Alloy

The physical properties of most engineering alloys can be modified by the formation of particles. Sometimes, the alloy design engineer plans for these particles to be present to enhance the properties or fabricability of the alloy, and sometimes they are formed during fabrication or in use and the properties, in which case the alloy properties may be degraded.

In either case particle analysis is crucial for property optimization. For this analysis, chemical identification with the use of energy dispersive spectroscopy is possible.

A region containing turbine material particles was observed in Figure 1, and a spectral imaging data set was collected using the following conditions:

• Beam Voltage: 5 kV
• Magnification: 8 kx
• Collection time: 40 minutes

The low beam energy was selected to bring down the interaction volume and enhance the EDS analysis spatial resolution. The cumulative spectrum for all of the pixels in the data set is shown in Figure 2. There is a heavy overlap of many of the peaks. These overlaps may complicate the interpretation of the analysis.

Figure 1

Figure 2

Traditional EDS Analyses

It is observed in the cumulative spectrum that the Cr-L peak overlaps with the Ti-L peak, and the Hf-M peak overlaps with the W-M peak. These peak overlaps lead to elemental peak count maps which have a similar appearance as shown in Figure 3. Interpretation of the maps under these circumstances will lead to an incorrect analysis.

Figure 3

However, there is a difference between these sets of elemental maps when full quantitative corrections are applied as shown Figure 4. Since net count maps are created, the spectral background is removed and peak separation is done. Due to these peak corrections, the results of the map are a precise representation of the sample’s elemental distributions when compared to the peak count map. Due to different elemental intensity overlaps at a number of pixels, the analyst infers multiple phases within the analysis area. However, the number and elemental combination of phases cannot be determined as there are so many phases and elements.

Figure 4

COMPASS (Multivariate Statistical Analysis)

COMPASS is a software option for NSS that utilizes multivariate statistical analysis to determine similar spectral regions in a spectral imaging acquisition. The spectrum is analyzed at each pixel location and groups pixels with similar spectra into principal components. The result is a list of chemically-innovative component spectra and component maps.

COMPASS Result

Five primary component spectra and maps were produced automatically for this data set, Figure 5. Because it was observed that the spectrum of the first principal component and the fifth principal component were similar, these components are displayed together for comparison purposes.

In case only the quantitative map results are considered, the phases formed by the sample are difficult to determine. However, this has been extracted by COMPASS automatically with no user bias on the input data. Furthermore, COMPASS has isolated 2, 3 and 4 components even when there is a huge overlap of the elemental peaks for Hf and W. Hence COMPASS is not only spatially deconvoluting but also spectrally deconvoluting.

Figure 5

Since the component spectra are due to the mathematics of the routine, they do not precisely represent the component composition. For precisely determining the composition of each phase extracted by COMPASS, it is necessary to have an independent method for the generation of an extracted spectrum for each component.

XPhase (Compound Location Analysis)

The software option, XPhase, which produces binary extraction images is created around the COMPASS and the quantitative map strength and produces binary extraction images. These map images are used for the extraction of compound spectra directly from the spectral imaging data set. The analytical results of the XPhase and COMPASS are processed together helping quantitative and location analysis of each phase to be performed.

XPhase Results

It is possible to analyze and locate quantitatively each phase using XPhase data. The quantitative results and the extracted spectrum that are extracted from each phase are shown in Figure 6.

When Phase 1 and Phase 5, which is derived from the first and fifth principal component respectively are compared, the Al content of Phase 1 and the Cr and the Co content of Phase 5 are determined to be higher than the other phase. The difference composition in the two phases is identified by the automatic COMPASS analysis even though there is very little contrast in the elemental maps. The initial COMPASS results of the separation of phases 2-4 with Hf:W ratios that vary from 54:10, to 23:30, to 3:20 are confirmed by the quantitative analysis.

Figure 6

Figure 7

Summary

This turbine sample was formed with a number of elements, which include Al, Ti, Cr, Co, Ni, Nb, Hf and W. Furthermore, the respective elements are segregated in a complicated way and multiple particle phases were observed. In order to obtain precise accurate EDS analyses at the required high magnification (8,000 x), the acceleration voltage must be reduced to a low value of 5 kV to minimise the interaction volume and enhance the spatial resolution. This causes many of the elemental peaks to be overlapped. Using traditional peak count map, analysis of the respective phases was nearly impossible using traditional peak count map. It is possible, however, to extract five unique phases simply by using the automated COMPASS analysis. In addition, XPhase offers a quantitative analysis for each of the phases. The combination of COMPASS and XPhase is very effective in phase analysis and identification of particles at high magnification and low beam energy with EDS.

This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Materials & Structural Analysis.

For more information on this source, please visit Thermo Fisher Scientific – Materials & Structural Analysis.