The Effect of Micro-Segregation on the Quantification of Microstructural Parameters in Grade 91 Steel

Figures 2 and 3 show µ-XRF-derived chemical distribution maps of a 42 mm² head section cut along the gauge length of the creep test bar for samples B2 and TP1, respectively. Although chemical segregation is observed in both samples, the pattern of segregation is very different. In B2, chemical banding along the axis of the creep bar is observed. These bands are typically between 0.5 and 2 mm in length and are approximately 20 to 50 µm wide, dimensions which are consistent with the reported literature for rolled/drawn interdendritic micro-segregation.[23,31] In TP1, the segregation exists in non-directional diffuse “clouds” which are typically 0.2 to 1 mm in length and 0.2 to 1 mm in width. These clouds are localized in specific regions rather than spread homogeneously across the sample like B2. The differences in segregation behavior of the two samples can be attributed to their different forming processes and their effects on the interdendritic regions from casting; B2 is from a drawn pipe (axis of the creep test bar is along the drawing direction) and TP1 is from a forging.

The local extremes of composition for both samples are compared together with the average measured using µ-XRF and from the bulk chemical analysis in Table III. In this work, positive segregation is used to describe regions where the alloying element content (e.g., Mo, Cr, Nb, and V) is above the bulk value of the steel, and conversely negative segregation is used for areas where the alloying element content is below the bulk value of the steel. It is noteworthy that the value obtained using the standard-less µ-XRF technique is close in absolute terms to the bulk composition measured by the commercial test house for the elements considered in Table II. (It should be noted that the µ-XRF technique does not have the sensitivity to detect trace impurities in these steels due to diffraction effects, and elements with a Z <  Na due to X-ray absorption from the Be windows on the EDS detectors.) It is important to note that Nb and Mo had the largest proportional variation in relative concentration from all the major alloying elements (as expected from their equilibrium partition coefficients and carbide-forming nature). For example, Mo showed a difference of ~ 24 and ~ 38 pct between positively and negatively segregated regions in B2 and TP1, respectively, comparatively Cr showed a difference of ~ 4 and ~ 10 pct, respectively. While it is possible to simulate segregation in complex alloys,[27] the non-equilibrium nature of segregation means that the level recorded here will depend on the casting conditions and the cumulative effects of heating and deformation (through dissolution and diffusion processes) during shaping. These data are not available and so this study relates quantified Laves phase parameters to the measured local compositions (segregated).

Although Figures 2 and 3 were collected in the head section of the samples, similar results were observed in the corresponding gauge sections. There was no observed necking aside from a very localized region in the notch and the applied temperature for the test (625°C) is much too low to homogenize the composition of the sample.

Figure 4 shows a typical SEM image and the accompanying EDS spectra collected from a bright phase particle. The Si and Mo content of this particle is consistent with Laves phase.[14] Similar spectra were found in positively and negatively segregated regions of both samples. Although these materials contain a number of second phase particles, Laves phase (Mo₂Fe) was chosen to assess the effect of segregation on the quantification of microstructural features because the segregation of Mo (a major constituent of Laves phase) is high relative to other alloying elements (e.g., Cr and V). Furthermore, Laves phase is relatively straight forward to quantify in the SEM for the current steels of interest due to the approximate range in particle diameter (40 to ~ 1000 nm) and the high number per unit area (typically > 100,000 particles per mm²). This permitted a full size distribution to be collected and analyzed in a time-efficient and to a statistically relevant procedure.

In the literature, there are many studies detailing the quantification of Laves phase particles using back-scattered electron imaging in the SEM.[12,16,17] In these studies, the effect of local chemical differences are minimized by collecting multiple images to get an average over the sample. A similar systematic approach was used here to collect 20 images across the head section of both samples. The images were processed to show the variation in area content of Laves phase across the data set as shown in Figure 5 (SCRA analysis). The range in area coverage across the data set is from 0.51 to 1.25 pct in B2 and from 0.32 to 0.77 pct for TP1. It is tempting to simply attribute this variation to sampling factors such as the relatively low number of particles per image (31 to 55 for B2 and 17 to 51 for TP1) and/or local variations in boundary type on which these particles preferentially precipitate. Typical investigations would overcome this by combining the images together to yield an average coverage. Alternatively, the possible statistical variation between images can be reduced simply by decreasing the magnification so that more particles per image are collected. This was not done here for two reasons:

This allows the contribution that segregation has on the image-to-image variation to be quantitatively assessed as in the following sections.

To study the effect of micro-segregation on Laves phase particle population characteristics, the chemical variations observed using µ-XRF needed to be found within an SEM. This was achieved by locating the approximate area based on their proximity to features within the XRF maps (i.e., the screw threads machined into the head sections—red asterisk in Figures 2 and 3). The chemical composition was then measured in these approximate areas using SEM-EDS maps at low magnification, long dwell time, and high count rate. Figure 6 shows typical SEM-derived EDS maps for both B2 and TP1 samples. The local range in composition was extracted from these maps and compared with the compositional variations measured using µ-XRF in Table IV. In all samples and all areas, the compositional extremes measured within the maps are lower in SEM-EDS than with µ -XRF. This is to be expected since the µ-XRF scans were performed over a much larger area providing many more positively segregated bands from which to select the highest and lowest areas. Even where the same area is studied from both techniques, the much larger interaction volume of µ-XRF at 50 kV compared with SEM-EDS at 10 kV would be expected to yield result differences. From the SEM-EDS maps, a series of 5 images were collected in both positively and negatively Mo-segregated areas. Typical examples of BSE-SEM images collected from these areas in the head section of both samples are shown in Figure 7. These images were processed to produce the area coverage, size distribution, and number of Laves particles per unit area graphs shown in Figures 5, 8, and 9, respectively.

In Figure 5, the area coverage of Laves phase particles within individual images collected in positively (red) and negatively (black) segregated areas are compared with the distribution from the systematically collected random areas (blue). In the B2 sample, where the segregation has a banded structure, the extremes in the random distribution are similar to the coverages from the positively and negatively segregated regions. This is expected as the systematic random collection process will hit some positively and negatively segregated areas, but the majority will straddle the segregation bands and provide a natural compositional averaging effect. In the TP1 sample, although the negatively segregated area fits well with the lower tail of the systematically collected data set, the positively segregated area has much higher coverages than any images in the systematically collected data set. This is because the positive segregation in this sample is very localized and the systematic collection method used to collect the average data did not sample an extreme area of positive Mo segregation. This is important because if localized segregation is present in the steel, the sampling of these regions becomes a matter of chance and could consequently lead to misleading results where blind imaging strategies are used. An overall comparison of the Laves phase coverage from the data sets is shown in Figure 9(a). This shows that the Laves phase coverage is on average 2.4 and 2.6 times higher in the positively segregated Mo areas of B2 and TP1, respectively, as compared to the corresponding negatively segregated Mo region.

Figure 8 shows the number of particles per unit area as a function of size for all data sets obtained for both samples. Consistently, the number of Laves phase particles per unit area is > 2X higher in the positively segregated Mo regions compared with the negatively segregated Mo regions as shown in Figure 9(b). The data collected from random locations sit in between these extremes. For the B2 sample, the data collected from random locations are at the mid-point between the extremes, whereas for TP1 they are located closer to the negatively segregated Mo point. This can be explained by the differences in the segregation characteristics of the samples, TP1 has localized enriched Mo ‘clouds,’ whereas B2 has a finer and much more ordered banded structure.

The size distribution data show that although the number of particles per unit area is much higher in positively Mo-segregated regions compared with negatively segregated regions, the size distribution of the particles is not significantly affected. This is important because this clearly shows that local increases of Mo result in increased nucleation of Laves phase particles but not an increased growth rate.

The amount of Laves phase as a function of temperature at thermodynamic equilibrium for these materials is predicted in Figure 10 using SEM-EDS compositions acquired from Figure 6. This shows that at the service temperature of the components (570 °C), the level of Mo segregation observed (with SEM-EDS) would result in approximately a variation by a factor of 2 between the extremes in composition. The absolute range predicted at this temperature of approximately 0.6 to 1.2 vol pct is consistent with the range in average area coverage values obtained by image analysis between the composition extremes in the samples (0.55 to 1.3 for B2 and 0.5 to 1.3 for TP1). Figure 10 shows that the predicted content of Laves phase decreases with increasing temperature for both samples and all compositions considered, and it also suggests at the creep test temperature (625 °C) there would be no Laves phase present in the negatively segregated regions. It is clear that this last point is not consistent with the observations documented in this investigation. Since the amount of Laves phase measured is consistent with the amount expected at thermal equilibrium at the service temperature, the relatively short creep tests at 625 °C do not seem to have affected the Laves phase content of these materials. This is important as the thermodynamic driving force for the Laves phase particles to dissolve into the steel matrix appears to be a kinetically slow process.

Segregation effects could cause Laves phase particles to form in localized regions at higher temperatures where these particles tend to be coarser. This is important as large Laves phase particles have been reported to be possible nucleation sites for creep cavities in these steels,[12,18‐20] although it should be emphasized that direct evidence for this is lacking because of the high number density of these particles and the small size of the particles relative to creep cavities. However, the similarity in size distributions recorded here would suggest that, in these examples, the effect of segregation is to activate more nucleation sites in the steel, but not to accelerate diffusional growth once nucleated.