OIM

My Turn

Dr. Stuart Wright, Senior Scientist, EDAX

One of the first scientific conferences I had the good fortune of attending was the Eighth International Conference on Textures of Materials (ICOTOM 8) held in 1987 in Santa Fe, New Mexico. I was an undergraduate student at the time and had recently joined Professor Brent Adams’ research group at Brigham Young University (BYU) in Provo, Utah. It was quite an introduction to texture analysis. Most of the talks went right over my head but the conference would affect the direction my educational and professional life would take.

Logos of the ICOTOMs I've attended

Logos of the ICOTOMs I’ve attended

Professor Adams’ research at the time was focused on orientation correlation functions. While his formulation of the equations used to describe these correlations was coming along nicely, the experimental side was quite challenging. One of my tasks for the research group was to explore using etch pits to measure orientations on a grain-by-grain basis. It was a daunting proposition for an inexperienced student. At the ICOTOM in Santa Fe, Brent happened to catch a talk by a Professor from the University of Bristol named David Dingley. David introduced the ICOTOM community to Electron Backscatter Diffraction (EBSD) in the SEM. Brent immediately saw this as a potential experimental solution to his vision for a statistical description of the spatial arrangement of grain orientations in polycrystalline microstructures.

At ICOTOMs through the years

At ICOTOMs through the years

After returning to BYU, Brent quickly went about preparing to get David to BYU to install the first EBSD system in North America. Instead of etch pits, my Master’s degree became comparing textures measured by EBSD and those measured with traditional X-Ray Pole Figures. I had the opportunity to make some of the first EBSD measurements with David’s system. From those early beginnings, Brent’s group moved to Yale University where we successfully built an automated EBSD system laying the groundwork for the commercial EBSD systems we use today.

I’ve had the good fortune to attend every ICOTOM since that one in Santa Fe over 30 years ago now. The ICOTOM community has helped germinate and incubate EBSD and continues to be a strong supporter of the technique. This is evident in the immediate rise in the number of texture studies undertaken using EBSD immediately after EBSD was introduced to the ICOTOM community.

The growth in EBSD in terms of the percentage of EBSD related papers at the ICOTOMs

The growth in EBSD in terms of the percentage of EBSD related papers at the ICOTOMs

Things have a way of coming full circle and now I am part of a group of three (with David Fullwood of BYU and my colleague Matt Nowell of EDAX) whose turn it is to host the next ICOTOM in St George Utah in November 2017. The ICOTOM meetings are held every three years and generally rotate between Europe, the Americas and Asia. At ICOTOM 18 we will be celebrating 25 years since our first papers were published using OIM.
icotom-2017
It is a humbling opportunity to pay back the texture community, in just a small measure, for the impact my friends and colleagues within this community have had both on EBSD and on me personally. It is exciting to consider what new technologies and scientific advances will be germinated by the interaction of scientists and engineers in the ICOTOM environment. All EBSD users would benefit from attending ICOTOM and I invite you all to join us next year in Utah’s southwest red rock country for ICOTOM 18! (http://event.registerat.com/site/icotom2017/)

Some of the spectacular scenery in southwest Utah (Zion National Park)

Some of the spectacular scenery in southwest Utah (Zion National Park)

With Great Data Comes Great Responsibility

Matt Nowell, EBSD Product Manager, EDAX

First, I have to acknowledge that I stole the title above from a tweet by Dr. Ben Britton (@BMatB), but I think it applies perfectly to the topic at hand. This blog post has been inspired by a few recent events around the lab. First, our data server drives suffered from multiple simultaneous hard drive failures. Nothing makes you appreciate your data more than no longer having access to it. Second, my colleague and friend Rene de Kloe wrote the preceding article in this blog, and if you haven’t had the opportunity to read it, I highly recommended it. Having been involved with EBSD sample analysis for over 20 years, I have drawers and drawers full of samples. Some of these are very clearly labeled. Some of these are not labeled, or the label has worn off, or the label has fallen off. One of these we believe is one of Rene’s missing samples, although both of us have spent time trying to find it. Some I can recognize just by looking, others need a sheet of paper with descriptions and details. Some are just sitting on my desk, either waiting for analysis or around for visual props during a talk. Here is a picture of some of these desk samples including a golf club with a sample extracted from the face, a piece of a Gibeon meteorite that has been shaped into a guitar pick, a wafer I fabricated myself in school, a rod of tin I can bend and work harden, and then hand to someone else to try, and a sample of a friction stir weld that I’ve used as a fine grained aluminum standard.

fig-1_modified
Each sample leads to data. With high speed cameras, it’s easier to collect more data in a shorter period of time. With simultaneous EDS collection, it’s more data still. With things like NPAR™, PRIAS™, HR-EBSD, and with OIM Analysis™ v8 reindexing functionality, there is also a driving force to save EBSD patterns for each scan. With 3D EBSD and in-situ heating and deformation experiments, there are multiple scans per sample. Over the years, we have archived data with Zip drives, CDs, DVDs, and portable hard drives. Fortunately, the cost for storage has dramatically decreased in the last 20+ years. I remember buying my first USB storage stick in 2003, with 256 MB of storage. Now I routinely carry around multiple TBs of data full of different examples for whatever questions might pop up.

cost-per-gigabyte-large_modified
How do we organize this plethora of data?
Personally, I sometimes struggle with this problem. My desk and office are often a messy conglomerate of different samples, golf training aids (they help me think), papers to read, brochures to edit, and other work to do. I’m often asked if I have an example of one material or another, so there is a strong driving force to be able to find this quickly. Previously I’ve used a database we wrote internally, which was nice but required all of us to enter accurate data into the database. I also used photo management software and the batch processor in OIM Analysis™ to create a visual database of microstructures, which I could quickly review and recognize examples. Often however, I ended up needing multiple pictures to express all the information I wanted in order to use this collection.

blog-fig-3_modified

To help with this problem, the OIM Data Miner function was implemented into OIM Analysis™. This tool will index the data on any given hard drive, and provide a list of all the OIM scan files present. A screenshot using the Data Miner on one of my drives is shown above. The Data Miner is accessed through this icon on the OIM Analysis™ toolbar. I can see the scan name, where it is located, the date associated with the file, what phases were used, the number of points, the step size, the average confidence index, and the elements associated with any simultaneous EDS collection. From this tool, I can open a file of interest, or I can delete a file I no longer need. I can search by name, by phase, or by element, and I can display duplicate files. I have found this to be extremely useful in finding datasets, and wanted to write a little bit about it in case you may also have some use for this functionality.

There’s A Hole in Your Analysis!

Dr. René de Kloe, Applications Specialist EDAX.

EBSD analysis is all about characterizing the crystalline microstructure of materials. When we are analyzing materials using EBSD the goal is to perform a comprehensive analysis on the entire field of interest. We strive to obtain the highest possible indexing rates and when we happen to misindex points we feel compelled to replace or clean these with other “valid” measurements that we simply copy from neighbor points so we do not have to show these failures in a report or paper.

But what should we do when we don’t expect data from specific spots in the first place? For example if a sample is porous or contains non-crystalline patches, or perhaps we have phases that don’t produce patterns? Then we typically simply try to ignore that. We may perhaps state that a certain fraction of our scan field refuses to produce indexing results and show which pixels these are, but that’s about it.

Pearlitic cast iron with graphite nodules - 13.5% graphite.

Figure 1: Pearlitic cast iron with graphite nodules – 13.5% graphite.

And that is strange as such areas where we don’t expect patterns are truly an integral part of a material and as such should also be characterized for a complete microstructural description. A traditional example of such a material is a cast iron which, although not porous, contains graphite inclusions which typically do not produce indexable EBSD patterns (Figure 1). Another example is the characterization of material produced by 3D printing of different metals, where small metal particles are sintered together using localized laser heating. This process doesn’t generate a fully dense product (Figure 2) and understanding the pore structure is important in predicting its mechanical response to stressed conditions.

Figure 2: Porous 3D printed steel - Indexing success 97.2%

Figure 2: Porous 3D printed steel – Indexing success 97.2%

Analyzing the non-indexed areas creates some challenges for the data treatment, especially any cleanup that you may want to do. You need to be careful to ensure that individual misindexed points, for example along grain boundaries or inside grains are not considered as pores. For a full analysis we need to be able to treat pore spaces as a special type of grain. Not one where pixels are grouped together based on similarity in measured orientation, but just the opposite, where pixels are combined based on misfit. This poses a special challenge on cleaning your data. When a typical clean-up acts like an in-situ grain growth experiment, where grains are expanded to consume bad points, in porous materials data cleanup needs to be done carefully to prevent the real grains from growing into real non-indexed spaces.

In general, EBSD data cleanup should be done in 3 steps:
1. Identify the good points,
2. Preserve the good points
and only then
3. Replace bad points.

For steps 1 and 2 we can use the patented Confidence Index in the OIM™ Analysis software. For step 1 we setup a filter to allow only correctly indexed points (typically with CI>0.1). However, this may remove too many points along grain boundaries, for example, where patterns overlap and indexing is uncertain. In step 2 we apply a confidence index standardization to retrieve all pixels that were indexed correctly, but had a low CI value and were excluded in step 1. This step assumes that if the orientation of a pixel matches that of adjacent pixels that had a high CI value, it was correctly indexed and needs to be included. This step does not change any measured orientations.

In step 3 we must be more careful as it is easy to accidentally replace too many points and shrink the non-indexed space (Figure 3):

Figure 3: Effect of too rigorous cleanup of partially crystalline material - Cu interconnects.

Figure 3: Effect of too rigorous cleanup of partially crystalline material – Cu interconnects.

A cleanup method that verifies whether a minimum number of neighboring points belong to a single grain such as the neighbor orientation correlation, is preferred.

Now that we know where the holes in the material are, we can get serious about analyzing them. First we need to define our real grains. Grains in EBSD analysis are defined by groups of points with similar orientations and a minimum number of pixels, for example maximum point to point misorientation less than 5 degrees and minimal 3 pixels in size. When you remove these grains from your partition, the left over pixels that do not fit into the grains can now be recognized. Coherent clusters of these misfit pixels are then grouped together into what might be called antigrains (Figure 4).

Figure 4: Grain and antigrain definition.

Figure 4: Grain and antigrain definition.

But even when the pores are recognized this way, the antigrains are not characterized by their orientation and as such their boundaries will do not show up in a traditional misorientation boundary overlay, which only shows the misorientation between recognized grains (Figure 5a). In order to make the antigrains visible as well, a boundary type that does not use misorientation as a criterion, but rather the position of triple junctions, needs to be selected. Between the triple junction nodes, vectors that follow all grain and antigrain interfaces are then constructed (Figure 5b).

a) Standard grain boundary overlay on IQ map based on grain orientation recognition. Non-indexed areas are white. b) IQ map with reconstructed boundaries including the antigrain edges.

Figure 5a) Standard grain boundary overlay on IQ map based on grain orientation recognition. Non-indexed areas are white. b) IQ map with reconstructed boundaries including the antigrain edges.

Once the antigrains are fully defined, all normal grain characterization tools are also available to describe the pore properties ranging from a basic size distribution (Figure 6) to a full analysis of the pore elongation and alignment (Figure 7).

Pore size distribution with colored highlighting in 3D printed iron sample.

Figure 6: Pore size distribution with colored highlighting in 3D printed iron sample.

Figure 7: Alignment of pore elongation direction.

Figure 7: Alignment of pore elongation direction.

With non-indexed points now properly assigned into antigrains, a full microstructural description of not fully dense materials or materials containing areas that cannot be indexed, is possible.

Finally we can do a (w)hole EBSD analysis.

Intelligent Use of IQ

Dr. Stuart Wright, Senior Scientist, EDAX

You don’t have to be a genius with a high IQ to recognize that IQ is an imperfect measure of intelligence much less EBSD pattern quality.

A Brief History of IQ
At the time we first came up with the idea of pattern quality, we were very focused on finding a reliable (and fast) image processing technique to detect the bands in the EBSD patterns. Thus, we were using the term “image” more frequently than “pattern” and the term “image quality” stuck.  The first IQ metric we formulated was based on the Burn’s algorithm (cumulative detected edge length) that we were using to detect the bands in the patterns in our earliest automation work1.

We presented this early work at the MS&T meeting in Indianapolis in October 1991. Niels Krieger-Lassen showed some promising band detection results using the Hough Transform2. Even though the Burn’s algorithm was working well we thought it would be good to compare it to the Hough Transform approach.  During that time we decided to use the sum of the Hough peak magnitudes to define the IQ when using the Hough Transform3. The impetus for defining an IQ was to compare how well the Hough Transform approach performed versus the Burn’s algorithm as a function of pattern quality. In case you are curious, here is the result. Our implementation of the Hough transform coupled with the triplet indexing routine clearly does a good job at indexing patterns of poor quality. Notice the relatively small IQ Hough-based values; this is because in this early implementation the average intensity of the pattern was subtracted from each pixel. This step was later dropped, probably simply to save time, which was critical when the cycle time was about four second per pattern.

Image1

After we did this work we thought it might be interesting to make an image by mapping the IQ value to a gray scale intensity at each point in a scan. Here is the resulting map – our first IQ map (Hough based IQ).
Image2

Not only did we explore ways of making things faster, we also wanted to improve the results. One by-product of those developments was that we modified the Hough Transform to be the average of the detected Hough peak heights instead of the sum. A still later modification was to use the number of peaks requested by the user, instead of the number of peaks detected. This was done so that patterns, where only a few peaks were found, did not receive unduly high IQ values.

The next change came not from a modification in how the IQ was calculated, but from the introduction of CCD cameras with 12 bit dynamic range which dramatically increased the IQ values.
In 2005 Tao and Eades proposed using other metrics for measuring IQ4. We implemented these different metrics and compared them with our Hough based IQ measurement in a paper we published in 20065. One of the main conclusions of that paper was that while for some very specific instances the other metrics had some value, our standard Hough based IQ was the best parameter for most cases. Interestingly, exploring the different IQ values was the seed for our PRIAS6 ideas but that is another story. Our competitors use other measures of IQ, but unfortunately these have not been documented – at least to my knowledge.

Factors Influencing IQ
While we have always tried to keep our software chronologically compatible, the IQ parameter has evolved and thus comparing absolute IQ values from data sets obtained using older versions of OIM with results obtained using new ones is probably not a good idea. Not only has the IQ definition evolved but so has the Hough Transform. In fact, since we created the very first IQ maps we realized that while the IQ maps are quite useful they are only quantitative in the sense of relative values within an individual dataset. We have always cautioned against using absolute IQ values of a method for comparing different datasets. In part, because we know a lot of factors affect the IQ values:

  • Camera Settings:
    • Binning
    • Exposure
    • Gain
  • SEM Settings
    • Voltage
    • Current
  • Hough Transform Settings
    • Pattern Size
    • Mask Size
    • Number of peaks
    • Secondary factors (peak symmetry, min distance, vertical bias,…)
  • Sample Prep
  • Image Processing

In developing the next version of OIM we thought it might be worthwhile revisiting the IQ parameter as implemented in our various software packages to see what we could learn about the absolute value of IQ.  In that vein, I thought it would be particularly interesting to look at the Mask Size and the Number of Peaks selected.  To do this, I used a dataset where we had recorded the patterns. Thus, we were able to rescan the dataset using different Hough settings to ascertain the impact of these settings on the IQ values. I also decided to add some Gaussian noise7 to the patterns to see what effect the noise had on the Hough settings.

It would be nice to scale the peak heights with the mask size. However, the “butterfly” masks have negative values in them, making it quite difficult to scale to the weighting of the individual elements of the convolution masks. In the original 7×7 mask we selected the individual components so that the sum would equal zero, to provide some inherent scaling. However, as we introduced other mask sizes this became increasingly difficult, particularly with the smaller masks (intended primarily for more heavily binned patterns).  Thus, we expected the peak heights to be larger for larger masks simply due to the number of matrix components. This trend was confirmed and is shown using the red curves in the figure below.  It should be noted that the smaller mask was used on a 48×48 pixel pattern, the medium on a 96×96 and the larger on a 192×192 pixel pattern.

We also decided to look at the effect of the number of peaks selected. It is assumed that, as we include more peaks we expect the pattern quality to decrease, as the weaker peaks will drive the average Hough peak heights down. This trend was also confirmed as can be seen by the blue curves in the figure.

Image3While these results went as expected, it can be harder to predict the effects of various image processing routines on IQ. The following plot shows the effect of various image processing routines on the IQ values. Perhaps someone with higher IQ could have predicted these results but to me the trends were not all expected. Of course, we usually apply image processing to improve indexing not IQ.

Image4Conclusions
In theory, if all the settings are the same, then the absolute value of the IQ for a matrix of samples should be meaningful. However, it would be rare to use the same settings (Camera, SEM, sample prep,…) for all materials in all states (e.g. deformed vs recrystallized). In fact this is one of the challenges of doing in-situ EBSD work for either a deformation experiment or a recrystallization/grain growth experiment – it is not always easy to predict how the SEM parameters or camera settings need to change as an in-situ experiment progresses. In addition, any changes made to the hardware generally mean that changes to the software are needed as well. Keeping everything constant is a lot easier in theory than it is in practice.

In conclusion, the IQ metric is “relatively” straightforward, but it must “absolutely” be used with some intelligence.☺

Bibliography
1. S.I. Wright and B.L. Adams (1992) “Automatic Analysis of Electron Backscatter Diffraction Patterns”  Metallurgical Transactions A 23, 759-767.
2. K. Kunze, S.I. Wright, B.L. Adams and D.J. Dingley  (1993) “Advances in Automatic EBSP Single Orientation Measurements” Textures and Microstructures 20, 41-54.
3. N.C. Krieger Lassen, D. Juul Jensen and K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning microscopy 6,  115-121.
4. X. Tap and A. Eades (2005) “Errors, artifacts, and improvements in EBSD processing and mapping” Microscopy and Microanalysis 11, 79-87.
5. S.I. Wright and M.M Nowell (2006) “EBSD Image Quality Mapping” Microscopy and Microanalysis 12, 72-84.
6. S. I. Wright, M. M. Nowell, R. de Kloe, P. Camus and T. M. Rampton  (2015) “Electron Imaging with an EBSD Detector” Ultramicroscopy 148, 132-145.
7. S I. Wright, M. M. Nowell, S. P. Lindeman, P. P. Camus, M. De Graef and M. Jackson (2015) “Introduction and Comparison of New EBSD Post-Processing Methodologies”  Ultramicroscopy 159, 81

Happy Thanksgiving!

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

In the United States we celebrate a holiday this time of year we call Thanksgiving.  It is one of my favorite holidays as we gather as families to enjoy a celebratory feast and spend time together. I also like it because it is not nearly as commercialized as the surrounding holidays of Halloween and Christmas – although I enjoy those holidays as well. In keeping with the holiday spirit, I want to express some gratitude related to my career in EBSD.

On one of my recent trips I was in Connecticut once again and was able to revisit some of my old grad-school haunts. This has caused me to get a little nostalgic for those years at Yale as I went through the ups and downs of trying to automate EBSD. My colleague from those days, David Field, remarked recently that how many people get to see their PhD research blossom as large as mine has. It has been rewarding to have been a part of the early development and the continued advancements but even more rewarding to see the wide variety of applications. I never imagined back in the early 90s that EBSD would be applied to historical artifacts1, used to determine the temperature of the eggs of dinosaurs2, gain understanding into the behavior of trilobites3 or track the movements of proteins4 to name just a few examples.

The First OIM – Channel Die Compressed Aluminum – December 1991

The First OIM – Channel Die Compressed Aluminum – December 1991

I also did not anticipate that being involved with EBSD would take me all over the world. Since SEMs are generally located in basements and time is often tight on my trips I often joke that my travels have allowed me to see the great airports and basements of the world. However, that is certainly an exaggeration. My travels have allowed me the opportunity to see some of the great sights of our planet and to learn more about the world’s diverse cultures.  It is fun to visit a lab in some far away corner of the world and then spend the evening after working together to be proudly shown the local sights and tastes by my host.

1) Brent Adams, Karsten Kunze (ETH Zurich) and I receiving the Henry Marion Howe Medal in 1994. 2) Francisco Cruz Gandarilla (Instituto Politécnico Nacional, Mexico) and Lisa Chan (Tescan USA) at Sundance Ski Resort. 3) David Dingley and I with colleagues from TSL Solutions in Tokyo (Adachi-San, Jo-San and Suzuki-San) 4) David Field (Washington State University), Abdul Majeed Mohammed (Business Communications LLC, UAE) at the Grand Mosque in Abu Dhabi 5) Matt Nowell and I getting a tour of Gyeongbokgung Palace in Seoul from a local colleague after ICOTOM 13.

1) Brent Adams, Karsten Kunze (ETH Zurich) and I receiving the Henry Marion Howe Medal in 1994.
2) Francisco Cruz Gandarilla (Instituto Politécnico Nacional, Mexico) and Lisa Chan (Tescan USA) at Sundance Ski Resort.
3) David Dingley and I with colleagues from TSL Solutions in Tokyo (Adachi-San, Jo-San and Suzuki-San)
4) David Field (Washington State University), Abdul Majeed Mohammed (Business Communications LLC, UAE) at the Grand Mosque in Abu Dhabi
5) Matt Nowell and I getting a tour of Gyeongbokgung Palace in Seoul from a local colleague after ICOTOM 13.

This year I’ve also been able to spend some extended time with former colleagues which has been a treat. I am thankful for the many men and women I have had the opportunity to interact with. I feel very fortunate to have worked with such remarkable scientists and have learned so much from them (I wish I had more photos!). I am humbled by their technical abilities but even more appreciative of their generous spirit and kindness.  I am grateful to count them as friends. It helps me view the world in a much more positive light than portrayed on the news.  I chuckle as our politicians try to arrogantly portray themselves as self-made men and women. I certainly am not self-made – I am the product of a good family and friends and colleagues who have shaped my life. I am glad that science and engineering is not done in a vacuum but is a team effort and often makes its greatest leaps forward when teams of people of diverse backgrounds come together.

1Wanhill, R. J. H. “Embrittlement of ancient silver.” Journal of failure analysis and prevention 5.1 (2005): 41-54.
2Eagle, Robert A., et al. “Isotopic ordering in eggshells reflects body temperatures and suggests differing thermophysiology in two Cretaceous dinosaurs.” Nature communications 6 (2015).
3Torney, Clare, Martin R. Lee, and Alan W. Owen. “Microstructure and growth of the lenses of schizochroal trilobite eyes.” Palaeontology 57.4 (2014): 783-799.
4Ogawa, Naoki, et al. “Three-Dimensional Picometer-Scale Motions in Aqueous Solution Visualized by Diffracted Electron Tracking.” Biophysical Journal 104.2 (2013): 526a.

Space Rocks Rock!

Matt Nowell, EBSD Product Manager, EDAX

I’ve always liked learning about space.  I remember in 2nd grade we had a test, and the final question was “If you think the Sun is hot, look in the sink”.  It was very rewarding, and obviously memorable, to find a basket of candy there.  With EDAX, I’ve had the opportunity to work with many groups involved with space-related research, including NASA and a visit to the Kennedy Space Center to train users.  Recent confluence of events motivated me to write about my experiences using EBSD to analyze meteorites.

First, I had the opportunity to visit the Planetary Materials Research Group within the Lunar and Planetary Laboratory at the University of Arizona.  You can read more about what this group does in both the recent EDAX Insight article and at their website, but it was interesting to learn about their research and their involvement with different NASA space missions.  One of the students had a great quote, which inspired this post, “Earth rocks are OK, but space rocks are very cool.”

Second, Shawn Wallace recently joined EDAX as an Applications Engineer.  Previously he had been at the American Museum of Natural History in New York City where his research focused on meteorites for understanding planet formation.

Third, I’ve spent some time testing ComboScan, where we montage multiple fields of view together for large area EBSD mapping.  Turns out the Gibeon meteorite is a great sample for this application.

Fourth, and finally, I was recently at a workshop where Joe Michael from Sandia National Laboratory showed transmission-EBSD (t-EBSD, or TKD) results from this Iron meteorite.  I found it very interesting that this same meteorite could be used with EBSD on areas of interest ranging from centimeters to nanometers, and show interesting and useful results.

Figure 1 shows an EBSD image quality and orientation map (subsequently referred to as EBSD map unless otherwise noted) collected from a polished slice of the Gibeon meteorite, an iron meteorite. The area mapped was approximately 27 mm x 15 mm with an 8µm step size. The microstructure shows a classic Widmanstätten pattern of geometrically arranged plates that correlated with the crystallography of the material. This structure develops as face-centered cubic taenite slowly cools and transforms into body-centered cubic kamacite at specific sites on the taenite crystal lattice. The orientation relationships that develop are easily observed and measured using EBSD.

Figure 1 - Large area EBSD map of Gibeon meteorite.

Figure 1 – Large area EBSD map of Gibeon meteorite.

Visually it is easy to see the long plates of kamacite at this magnification, but you can also see areas of finer microstructure.  Figure 2 shows another EBSD map collected at 100X magnification with a 850 nm step size.  Here you can see 3 different length scales of material, the large kamacite plates, a smaller field of both kamacite and taenite, and regions of fine-grained microstructure.

Figure 2 - 100X EBSD map of Gibeon meteorite.

Figure 2 – 100X EBSD map of Gibeon meteorite.

Pushing the magnification higher, Figure 3 shows an image collected at 1,000X magnification with a 100 nm step size.  Figure 4 shows the same data presented as a phase map, with the kamacite phase colored blue and the taenite phase colored yellow.  We can clearly resolve the taenite grains within the field of kamacite.  You will also notice that the majority of the taenite grains have the same orientation in this region.  This is an area of incomplete transformation, and is similar to retained austenite engineered into modern steel alloys.  You can also see running through the upper left corner of the map a thin band of even finer microstructure.

Figure 3 – 1,000X EBSD map of Gibeon meteorite.

Figure 4 – 1,000X EBSD Phase map of Gibeon meteorite.

This fine microstructure was analyzed with a 25 nm step size, and is shown in Figure 5.  The same geometrical relationships observed on a mm-scale are reproduced on a nm-scale.  This sample nicely shows how EBSD can be used to characterize materials across the spatial range.

Figure 5 - 8,000X EBSD map of Gibeon meteorite.

Figure 5 – 8,000X EBSD map of Gibeon meteorite.

Of course these are all 2D slices on what is a very interesting 3D microstructure.  To go further, I used an FEI Quanta 3D Dual Beam instrument to collect EBSD data from FIB serial-sections, and used the tools in OIM Analysis to reconstruct the microstructure in three dimensions.  I targeted a region where the fine microstructure was adjacent to one of the larger kamacite grains, and the 3D volume is shown in Figure 6.  The large kamacite grain is easily seen, along with the interface layer bordering this grain.  The majority of the displayed volume here is the fine dual-phase microstructure.  Figure 7 shows an animation of the entire 3D volume.

Figure 6 - 3D EBSD data from Gibeon meteorite.

Figure 6 – 3D EBSD data from Gibeon meteorite.

 

Once the 3D data has been collected and reconstructed, it is possible to identify and select specific grains of interest for analysis.  In Figure 8, I selected a kamacite plate, and in Figure 9 I selected the interface grain between the larger and smaller microstructures.  The 3D grain information is able to show how these different plates grow and fit together, like pieces of a complex jigsaw puzzle.

Figure 8 – 3D Kamacite lamella grain.

Figure 9 – 3D interface in Gibeon meteorite.

Other regions of the Gibeon meteorite show interesting inclusions that can be analyzed with both EBSD and simultaneously collected EDS data.  Figure 10 shows a large area Image Quality map with two visible inclusions.  Figure 11 shows a composite EDS map, where the Iron signal is colored Blue, the Nickel signal is colored green, and the Sulfur signal is colored Red.  The inclusion exhibits a Sulfur zoning profile, and can be correlated with the orientation microstructure.

Figure 10 – EBSD Image Quality map of inclusion in Gibeon meteorite.

Figure 11 – Composite EDS map of inclusion in Gibeon meteorite.

Finally, this work has focused on results from one specific meteorite, the Gibeon meteorite.  As Shawn would tell us, there are many, many more, which tell interesting tales about the Universe we live in.  Figure 12 shows an EBSD map from a meteorite section we analyzed at the University of Arizona.  This meteorite is primarily enstatite.  I’m excited to learn what this microstructure has to tell us in their future work!

Figure 12 – EBSD map from meteorite (primarily Enstatite phase)