As EBSD Product Manager, one of the things I have missed the most in the last 18 months during the COVID pandemic is visiting customers. Generally, in a year, I will attend a few meetings. Some are reoccurring: M&M for microscopy topics, TMS for materials science, and an annual EBSD meeting (either the RMS or MAS version, depending on the year) to keep up with the latest and greatest in these fields. Additionally, I will attend a new show to learn about potential markets and applications. It’s always enjoyable to meet both users and prospects to learn more about their applications and how EDAX tools can help their characterization needs.
In place of these shows, I’ve been turning towards social media to keep track of trends for EBSD. Twitter is one tool I use, where there is a strong scientific group that shares their thoughts on a range of subjects and offers support to each other in this networked community. Recently, my Twitter feed showed a beautiful EBSD map on the cover of Science. Professor Andrew Minor’s group out of UC Berkeley had used EDAX EBSD to analyze twinning in cryoforged titanium. I feel connected to this work, as I’ve looked at twinning in titanium on other samples (Bringing OIM Analysis Closer to Home blog). Seeing different posts about various applications helps me understand where EBSD is used is very exciting and rewarding.
Figure 1. September 17, 2021 issue of Science magazine featuring an EBSD orientation map of cryoforged titanium.
LinkedIn is another social media tool I use. One of my favorite things about this platform is seeing how the careers of different people I know have developed over the years. I turn 50 in a couple of weeks, and I’ve been involved in EBSD for over half of these years. With that experience, I’ve seen the generational development of scientists and engineers in my field. The post-docs who first adopted EBSD when I started are now department chairs and running their own research groups. The students who came to a training course now advise the new users at their companies on EBSD. Recent students are graduating and now asking about EBSD for their new positions. It’s easy to get a sense of how the EBSD knowledge I’ve shared with people has percolated out into the greater world.
While I expect to see some EBSD on Twitter and LinkedIn, this year, I also had a pleasant surprise finding some wonderful EBSD in Gizmodo (https://gizmodo.com/these-microscopic-maps-of-3d-printed-metals-look-like-a-1846669930). I’ve had a strong interest in additive manufacturing since visiting NASA 15 years ago. Seeing this technology develop and how EBSD can help understand the microstructures produced is very satisfying to me. I reached out to Jake Benzing, who was the driver behind this post. This led to his group at NIST being featured in our latest EDAX Insight newsletter. It also helped me connect with a user and be better positioned to get feedback on using our products to drive development and improvement.
Figure 2. Ti-6Al-4V created by a form of AM called electron-beam melting powder-bed fusion. This map of grain orientations reveals an anisotropic microstructure, with respect to the build direction (Z). In this case, the internal porosity was sealed by a standard hot isostatic pressing treatment.
This last year has been different in many ways, both personally and at work. For me, it meant being in the office or working from home instead of being out and about and meeting customers and performing operator schools in person. This does not exactly mean that things are quieter, though! At home, I got confronted with lots of little maintenance things in and around my house that otherwise somehow manage to escape my attention. At work, lots of things vying for my attention have managed to land on my desk.
The upside is that with almost everything now being done through remote connections. I get to sit more at the microscope in the lab to work on customer samples, collect example datasets, perform system tests, and also practice collecting data on difficult samples so that I can support our customers better. To do that, I have the privilege of being able to choose which EBSD detector I want to mount, from the fast Velocity to the familiar Hikari to the sensitive Clarity Direct Electron System. But how do I decide what samples to use for such practice sessions?
Figure 1. A common garden snail (Cornu aspersum) and an empty shell used for the analysis.
In the past, I wrote about my habit of occasionally going “dumpster diving” to collect interesting materials (well, to be honest, I try to catch the things just before they land in the dumpster). That way, I have built up a nice collection of interesting alloys, rocks, and ceramics to keep me busy. But this time, I wanted to do something different, and an opportunity presented itself when I was working on a fun DIY project, a saddle stool for my daughter. On one of the days that I was shaping wood in my garden for the saddle-support, I noticed some garden snails moving about leisurely. These were the lucky chaps (Figure 1). While we occasionally feel the need to redecorate our walls to get a change of view, the snail’s home remains the same and follows him wherever he goes; sounds great! No need to do any decoration or maintenance, and always happy at home!
But all kidding aside, I have long been interested in the structure of these snail shells and have wanted to do microstructural analysis on one. So, when I found an empty shell nearby belonging to one of its cousins that had perished, I decided to try to do some Scanning Electron Microscope (SEM) imaging and collect Electron Backscatter Diffraction (EBSD) data to figure out how the shell was constructed. The fragility of the shell and especially the presence of organic material in between the carbonate crystals that make up the shell makes them challenging for EBSD, so I decided to mount my Clarity Detector and give it a very gentle try.
The outer layer that contains the shell’s color was already flaking off, so I had nice access to the shell’s outer surface without the need to clean or polish it. And with the Gatan PECS II Ion Mill that I have available, I prepared a cross-section of a small fragment. I was expecting a carbonate structure like you see in seashells and probably all made of calcite, which is the stable crystal form of CaCO3 at ambient temperatures. What I found was quite a bit more exotic and beautiful.
In the cross-section, the shell was made up of multiple layers (Figure 2). First, on the inside, a strong foundation made of diagonally placed crossed bars, then two layers of well-organized small grains, was topped by an organic layer containing the color markings.
Figure 2. A PECS II milled cross-section view of the shell with different layers. The dark skin on the top is the colored outer layer.
At the edge of the PECS II prepared cross-section, a part of the outer shell surface remained standing, providing a plan view of the structure just below the surface looking from the inside-out. In the image (Figure 3), a network of separated flat areas can be recognized with a feather-like structure on the top, which is the colored outer surface of the shell. An EDS map collected at the edge suggests that the smooth areas are made up of Ca-rich grains, which you would expect from a carbonate structure. Still, the deeper “trenches” contain an organic material with a higher C and O content, explaining why the shell is so beam-sensitive.
Figure 3. A plan view SEM image of the structure directly below the colored surface together with EDS maps showing the C (purple), O (green), and Ca (blue) distribution.
The EBSD data was collected from the outer surface, where I could peel off the colored organic layer. This left a clean but rough surface that allowed successful EBSD mapping without further polishing.
My first surprise here was the phase. All the patterns that I saw were not of calcite but aragonite (Figure 4). This form of calcium carbonate is stable at higher temperatures and forms nacre and pearls in shells in marine and freshwater environments. I was not expecting to see that in a land animal.
Figure 4. An aragonite EBSD pattern and orientation determination.
The second surprise was that the smooth areas that you can see in Figure 3 are not large single crystals but consist of a very fine-grained structure with an average grain size of only 700 nm (Figure 5). The organic bands are clearly visible by the absence of diffraction patterns – the irregular outline is caused by projection due to the surface topography.
Figure 5. Image Quality (IQ) and aragonite IPF maps of the outer surface of the shell. The uniform red color and (001) pole figure indicate a very strong preferred crystal orientation.
After this surface map, I wanted to try something more challenging and see if I could get some information on the crossbar area underneath. At the edge of the fractured bit of the shell, I could see the transition between the two layers with the crossbars on the left, which were then covered by the fine-grained outer surface (Figure 6).
Figure 6. An IQ map of the fracture surface. The lower left area shows the crossbar structure, then a thin strip with the fine-grained structure, and at the top right some organic material remains.
Because the fractured sample surface is very rough, EBSD patterns could not be collected everywhere. Nevertheless, a good indication of the microstructure could be obtained. The IPF map (Figure 7) shows the same color as the previous map, with all grains sharing the same crystal direction pointing out of the shell.
Figure 7. An IPF map showing the crystal direction perpendicular to the shell surface. All grains share the same color indicating that the  axes are aligned.
But looking at the in-plane directions showed a very different picture (Figure 8). Although the sample normal direction is close to  for all grains, the crystals in the crossbar structure are rotated by 90° and share a well-aligned  axis with the two main directions rotated by ~30° around it.
Figure 8. An IPF map along Axis 2 showing the in-plane crystal directions with corresponding color-coded pole figures.
Figure 9. Detail of the IPF map of the crossbar area with superimposed crystal orientations.
I often have a pretty good idea of what to expect regarding phases and microstructure in manufactured materials. Still, I am often surprised by the intricate structures in the smallest things in natural materials like these snail shells.
These maps indicate a fantastic level of biogenic crystallographic control in the snail shell formation. First, a well-organized interlocked fibrous layer with a fixed orientation relationship is then covered by a smooth layer of aragonite islands, bound together by an organic structure, and then topped by a flexible, colored protective layer. With such a house, no redecoration is necessary. Home sweet home indeed!
Of all the papers I’ve written, my favorite title I’ve managed to sneak past the editors and reviewers is “Random thoughts on non-random misorientation distributions.” The paper is a write-up of a presentation I gave at a celebration of Professor David Dingley’s contributions to EBSD, which was held as a special version of the annual Royal Microscopy Society EBSD meeting at New Lanark in Scotland. It was a fun meeting as several of David’s former Ph.D. students shared some great stories and pictures of David, and the talks were a little less formal than usual, which led to some interesting discussions.
There are many terms used to describe the difference in crystallographic orientation between two crystal lattices: misorientation, disorientation, orientation difference, misorientation angle, minimum misorientation angle, grain boundary character, intercrystalline interface. One can get a bit “disoriented” trying to sort out all these different terms. Unfortunately, I am at fault for some of the confusion as I have tended to use the different terms loosely in my presentations and papers. But I am not the only one; I have seen some wandering in the definition of some of these terms as different researchers have followed up on the work of others. I will not pretend to be rigorous in this blog, but let me see if I can help sort through the different terms.
My first exposure to the idea of misorientation was from Bunge’s classic book Texture Analysis in Materials Science from 1969. I was first introduced to the book when I joined Professor Brent Adam’s Lab in 1985. We called it the “Red Bible,” as we had a very well-worn copy in the lab. We were even lucky enough to have Peter Morris with us at the time, who translated the book from German to English (a herculean task for a non-German speaker without modern tools like Google Translate). On page 44 of this book, you will find the following:
If two adjacent grains in a grain boundary have orientations g1 and g2, the orientation difference is thus given by:
∆g = g2 ∙ g1-1 (3.12)
This looks like a relatively simple expression, and we have generally calculated it using orientations described as matrices, and thus the result ∆g would also be a matrix. But the most common description of this orientation difference given in the literature would be an axis-angle pair. Any two crystals have at least one axis in common. A rotation about that axis will bring the two crystal lattices into coincidence.
Figure 1. Axis-angle description of misorientation.
While the equation above seems simple, we need to remember that, due to crystal symmetry, there are multiple symmetrically equivalent descriptions of the orientations g1 and g2. We can term the symmetry operators Li. These are the elements of the crystallographic point group symmetry for the crystals in question. For example, for a cubic crystal, there will be 24 symmetry elements. Since there are 24 symmetric equivalents for g1 and 24 for g2 that means there will be 576 symmetric equivalents for ∆g. In the expression below, the apostrophe denotes symmetrically equivalent.
∆g’12 = Lig2∙(Lig1)-1
As an example, here is a list for a random axis angle pair assuming cubic crystal symmetry: 12° @ 〈456〉. Note that the notation 〈uvw〉 denotes the family of crystal directions and [uvw] denotes a single crystal direction. Once again, for cubic symmetry, there are (in general) 24 [uvw] directions in the 〈uvw〉 family of directions (note in general there are 24 directions in the family, i.e. , , [-123], [-132], …. but this can be reduced for families where multiplicity plays a role, such as 〈00w〉 or 〈uuw〉…).
(4 5 6)
(139 132 170)
(2 18 155)
(118 121 148)
(20 4 157)
(44 43 45)
(4 45 325)
(2 161 177)
(33 3 262)
(235 6 265)
(4 20 177)
(2 149 172)
(62 7 617)
(10 8 167)
(39 38 40)
(8 12 167)
(137 173 177)
(12 10 167)
(130 103 136)
(25 196 221)
(137 177 181)
(188 26 207)
(149 153 192)
(155 18 179)
So, this is a list of symmetrical misorientations given as axis-angle pairs. The minimum rotation angle in this set is the disorientation. But, you will also see the disorientation called the orientation distance (Bunge equation 2.123), rotation angle and misorientation angle (OIM), minimum misorientation angle, as well as simply the misorientation, orientation difference, grain boundary angle, . For a little comic relief at intense EBSD workshops, I have often said that I prefer the term misorientation because disorientation is what we tend to feel at the end of the day of lectures. I give Professor Marc De Graef credit for helping me finally get these terms straight. So, now I can retire that joke that probably never really translated very well into different languages anyway.
One more note on terminology. A grain boundary is a five-parameter entity: three for the misorientation and two to describe the orientation of the boundary plane.
Figure 2. 5D Grain Boundary Character.
This five-dimensional entity is now often referred to as the Grain Boundary Character (Rohrer) but has also been termed the Intercrystalline Interface Structure (Adams). In the past and in OIM Analysis, the Grain Boundary Character Distribution or GBCD refers to the distribution of grain boundaries across three classifications, low-angle random boundaries, high-angle random boundaries, and “special” (generally CSL) boundaries. As a side note, Grain Boundary Character has been called a “full” or “complete” description of a grain boundary, but this is a bit of an overreach. There are still other parameters associated with a grain boundary that may be just as important as these five, for example, curvature, faceting, chemical composition.
It should be noted that we can calculate the misorientation between two crystals of different symmetry and get a nice, neat axis-angle pair.
However, the concept of coincidence is not as clear as for two crystals of the same symmetry, as illustrated in the schematic shown in Figure 3. Nonetheless, this terminology (and its corresponding mathematical methods) can be helpful when analyzing the orientation relationships associated with phase transformations.
Figure 3. Misorientation between a hexagonal and cubic crystal.
I hope this brief discussion has helped “orient” you in the right direction. I know I am now trying to be more careful in using these terms, which will probably result in a few changes in our user interface for a future version of OIM to reflect this.
Wright, SI (2006) Random thoughts on non-random misorientation distributions. Materials Science and Technology 22: 1287-1296.
Bunge, HJ (1969) Mathematische Methoden der Texturanalyse. Akademie-Verlag: Berlin.
Beladi H, Nuhfer NT, and Rohrer GS (2014) The five-parameter grain boundary character and energy distributions of a fully austenitic high-manganese steel using three dimensional data. Acta Materialia 70:281-289
Zhao J, Koontz JS, and Adams BL, 1988. Intercrystalline structure distribution in alloy 304 stainless steel. Metallurgical Transactions A, 19:1179-1185.
One of the joys of my job is troubleshooting issues and ensuring you acquire the best results to advance your research. Sometimes, it requires additional education to help users understand a concept. Other times, it requires an exchange of numerous emails. At the end of the day, our goal is not just to help you, but to ensure you get the right information in a timely manner.
For any sort of EDS related question, we almost always want to look at a spectrum file. Why? There is so much information hidden in the spectrum that we can quickly point out any possible issues. With a single spectrum, we can quickly see if something was charging, tilted, or shadowed (Figure 1). We can even see weird things like beam deceleration caused by a certain imaging mode (Figure 2). With most of these kinds of issues, it is common to run into major quant related problems. Any quant problems should always start with a spectrum.
Figure 1. The teal spectrum shows a strange background versus what a normal spectrum (red) should look like for a material.
This background information tells us that the sample was most likely shadowed and that rotating the sample to face towards the detector may give better results.
Figure 2. Many microscopes can decelerate the beam to help with imaging. This deceleration is great for imaging but can cause EDS quant issues. Therefore, we recommend reviewing the spectrum up front to reduce the number of emails to troubleshoot this issue.
To save the spectrum, right-click in the spectrum window, then click on Save (Figure 3). From there, save the file with a descriptive name, and send it off to the applications group. These spectrum files also include other metadata, such as amp time, working distance, and parameters that give us so many clues to get to the bottom of possible issues.
Figure 3. Saving a spectrum in APEX is intuitive. Right-click in the area and a pop-up menu will allow you to save the spectrum wherever you want quickly.
The actual image file can also help us confirm most of the above.
Troubleshooting EBSD can be tricky since the issue could be from sample prep, indexing, or other issues. To begin, it’s important to rule out any variances associated with sample preparation. Useful information to share includes a description of the sample, as well as the step-by-step instructions used to prepare the sample. This includes things like the length of time, pressure, cloth material, polishing compound material, and even the direction of travel. The more details, the better!
Now, how do I know it is a sample prep problem? If the pattern quality is low at long exposure times (Figure 4) or the sample looks very rough, it is probably related to sample preparation (Figure 4). That being said, there could be non-sample prep related issues too.
Figure 4. This pattern is probably not indexable on its own. Better preparation of the sample surface is necessary to index and map this sample correctly.
Indexing problems can be challenging to troubleshoot without a full data set. How do I know my main issues could be related to indexing? If indexing is the source, a map often appears to be very speckled or just black due to no indexing results. For this kind of issue, full data sets are the way to go. By full, I mean patterns and OSC files. These files can be exported out of TEAM/APEX. They are often quite large, but there are ways available to move the data quickly.
As for camera set up, this is a dance between the microscope settings, operator’s requirements, and the camera settings. In general, more electrons (higher current) allow the experiment to go faster and cover more area. With older CCD based cameras, understanding this interaction was key to good results. With the newer Velocity cameras based on CMOS technology, the dance is much simpler. If you are having difficulty while trying to optimize an older camera, the Understanding and Optimizing EBSD Camera Settings webinar can help.
So how do you get your questions answered fast? Bury us with information. More information lets us dive deeper into the data to find the root cause in the first email, and avoids a lengthy back and forth exchange of emails. If possible, educate yourself using the resources we have made available, be it webinars or training courses. And always, feel free to reach out to my colleagues and me at firstname.lastname@example.org!
Jonathan McMenamin, Marketing Communications Coordinator, EDAX
EDAX is considered one of the leaders in the world of microscopy and microanalysis. After concentrating on advancements to our Energy Dispersive Spectroscopy (EDS) systems for the Scanning Electron Microscope (SEM) over the past few years, EDAX turned its attention to advances in Electron Backscatter Diffraction (EBSD) and EDS for the Transmission Electron Microscope (TEM) in 2019.
After the introduction of the Velocity Plus EBSD camera in June 2018, which produces indexing speeds greater that 3,000 indexed points per second, EDAX raised the bar further in 2019. In March, the company announced the arrival of the fastest EBSD camera in the world, the Velocity Super, which can go 50% faster at 4,500 indexed points per second. This was truly a great accomplishment!
EBSD orientation map from additively manufactured Inconel 718 collected at 4,500 indexed points per second at 25 nA beam current.
Less than three months later, EDAX added a new detector to its TEM product portfolio. The Elite T Ultra is a 160 mm2 detector that offers a unique geometry and powerful quantification routines for comprehensive analysis solutions for all TEM applications. The windowless detector’s geometric design gives it the best possible solid angle to increase the X-ray count rates for optimal results.
EDAX Elite T Ultra EDS System for the TEM.
Just before the annual Microscopy & Microanalysis conference, EDAX launched the OIM Matrix software module for OIM Analysis. This new tool gives users the ability to perform dynamic diffraction-based EBSD pattern simulations and dictionary indexing. Users can now simulate EBSD patterns based on the physics of dynamical diffraction of electrons. These simulated patterns can then be compared to experimentally collected EBSD patterns. Dictionary indexing helps improve indexing success rates over standard Hough-based indexing approaches. You can watch Dr. Stuart Wright’s <a href=”https://youtu.be/Jri181evpiA” target=”_blank”>presentation from M&M</a> for more information.
Dictionary indexing flow chart and conventional indexing results compared with dictionary indexing results for a nickel sample with patterns collected in a high-gain/noisy condition.
EDAX has several exciting product announcements on the way in early 2020. We have teased a two of these releases, APEX Software for EBSD and the Clarity Direct Electron Detector. APEX EBSD will give users the ability to characterize both compositional and structural characteristics of their samples on the APEX Platform. It gives them the ability to collect and index EBSD patterns and EBSD maps, as well as allow for simultaneous EDS-EBSD collection. You can learn more about APEX EBSD in the September issue of the Insight newsletter and in our “APEX EBSD – Making EBSD Data Collection How You Want It” webinar.
EBSD of a Gibeon Meteorite sample covering a 7.5 mm x 6.5 mm area using ComboScan for large area analysis.
The Clarity is the world’s first commercial direct electron detector (DeD) for EBSD. It provides patterns of the highest quality and sensitivity with no detector read noise and no distortion for optimal performance. The Clarity does not require a phosphor screen or light transfer system. The DeD camera is so sensitive that individual electrons can be detected, giving users unprecedented performance for EBSD pattern collection. It is ideal for analysis of beam sensitive samples and potential strain applications. We recently had a webinar “Direct Electron Detection with Clarity – Viewing EBSD Patterns in a New Light” previewing the Clarity. You can also get a better understanding of the system in the December issue of the Insight newsletter or the .
EBSD pattern from Silicon using the Clarity detector.
All this happened in one year! 2020 looks to be another great year for EDAX with further improvements and product releases to offer the best possible tools for you to solve your materials characterization problems.
We all give presentations. We write and review papers. Either way, we have to be critical of our data and how it is presented to others, both numerically and graphically.
With that said, I thought it would be nice to start this year with a couple of quick tips or notes that can help with mistakes I see frequently.
The most common thing I see is poorly documented cleanup routines and partitioning. Between the initial collection and final presentation of the data, a lot of things are done to that data. It needs to be clear what was done so that one can interpret it correctly (or other people can reproduce it). Cleanup routines can change the data in ways that can either be subtle (or not so subtle), but more importantly they could wrongly change your conclusions. The easiest routine to see this on is the grain dilation routine. This routine can turn noisy data into a textured dataset pretty fast (fig. 1).
Figure 1. The initial data was just pure noise. By running it iteratively through the grain dilation routine, you can make both grains and textures.
Luckily for us, OIM Analysis keeps track of most of what is done via the cleanup routines and partitioning in the summary window on either the dataset level or the partition level (fig. 2).
Figure 2. A partial screenshot of the dataset level summary window shows cleanup routines completed on the dataset, as well as the parameters used. This makes your processing easily repeatable.
The other common issue is not including the full information needed to interpret a map. I really need to look at 3 things to get the full picture for an EBSD dataset: the IPF map (fig. 3), the Phase Map (fig. 4) and the IPF Legend (fig. 5) of those phases. This is very important because while the colors used are the same, the orientations differ between the different crystal symmetries.
Figure 3. General IPF Map of a geological sample. Many phases are present, but the dataset is not complete without a legend and phase map. The colors mean nothing without knowing both the phase and the IPF legend to use for that phase.
Below is a multiple phase sample with many crystal symmetries. All use Red-Green-Blue as the general color scheme. By just looking at the general IPF map (fig. 3), I can easily get the wrong impression. Without the phase map, I do not know which legend I should be using to understand the orientation of each phase. Without the crystal symmetry specific legend, I do not know how the colors change over the orientation space. I really need all these legends/maps to truly understand what I am looking at. One missing brick and the tower crumbles.
Figure 5. With all the information now presented, I can actually go back and interpret figure 3 using figures 4 and 5 to guide me.
Figure 4. In this multiphase sample, multiple symmetries are present. I need to know which phase a pixel is, to know which legend to use.
Being aware of these two simple ideas alone can help you to better present your data to any audience. The fewer the questions about how you got the data, the more time you will have to answer more meaningful questions about what the data actually means!
I was recently asked to write a “Tips & Tricks” article for the EDAX Insight Newsletter as I had recently done an EDAX Webinar (www.edax.com/news-events/webinars) on Texture Analysis. I decided to follow up on one item I had emphasized in the Webinar. Namely, the need for sampling enough orientations for statistical reliability in characterizing a texture. The important thing to remember is that it is the number of grain orientations as opposed to the number of orientations measured. But that lead to the introduction of the idea of sub-sampling a dataset to calculate textures when the datasets are very large. Unfortunately, there was not enough room to go into the kind of detail I would have liked to so I’ve decided to use our Blog forum to cover some details about sub-sampling that I found interesting
Consider the case where you not only want to characterize the texture of a material but also the grain size or some other microstructural characteristic requiring a relatively fine microstructure relative to the grain size. According to some previous work, to accurately capture the texture you will want to measure approximately 10,000 grains  and about 500 pixels per average grain in order to capture the grain size well . This would result in a scan with approximately 5 million datapoints. Instead of calculating the texture using all 5 million data points, you can use a sub-set of the points to speed up the calculation. In our latest release of OIM Analysis, this is not as big of a concern as it once was as the texture calculations have been multithreaded so they are fast even for very large datasets. Nonetheless, since it is very likely that you will want to calculate the grain size, you can use the area weighted average grain orientation for each grain as opposed to using all 5 million individual orientation measurements for some quick texture calculation. Alternatively, a sub-set of the points through random or uniform sampling of the points in the scan area could be used.
Of course, you may wonder how well the sub-sampling works. I have done a little study on a threaded rod from a local hardware store to test these ideas. The material exhibits a (110) fiber texture as can be seen in the Normal Direction IPF map and accompanying (110) pole figure. For these measurements I have simply done a normalized squared difference point-by-point through the Orientation Distribution Function (ODF) which we call the Texture Difference Index (TDI) in the software.
This is a good method because it allows us to compare textures calculated using different methods (e.g. series expansion vs binning). In this study, I have used the general spherical harmonics series expansion with a rank of L = 22 and a Gaussian half-width of = 0.1°. The dataset has 105,287 points with 92.5% of those having a CI > 0.2 after CI Standardization. I have elected only to use points with CI > 0.2. The results are shown in the following figure.
As the step size is relatively coarse with respect to the grain size, I have experimented with using grains requiring at least two pixels before considering a set of similarly oriented points a grain versus allowing a single pixel to be a grain. This resulted in 9981 grains and 25,437 grains respectively. In both cases, the differences in the textures between these two grain-based sub-sampling approaches with respect to using the full dataset are small with the 1 pixel grain based sub-sampling being slight closer as would be expected. However, the figure above raised two questions for me: (1) what do the TDI numbers mean and (2) why do the random and the uniform sampling grids differ so much, particularly as the number of points in the sub-sampling gets large (i.e. at 25% of the dataset).
The pole figure for the 1000 random points in the previous figure certainly captures some of the characteristics of the pole figure for the full dataset. Is this reflected in the TDI measurements? My guess is that if I were to calculate the textures at a lesser rank, something like L = 8 then the TDI’s would go down. This is already part of the TDI calculation and so it is an easy thing to examine. For comparison I have chosen to look at four different datasets: (a) all of the data in the dataset above (named “fine”), (b) a dataset from the same material with a coarser step size (“coarse”) containing approximately 150,000 data points, (c) sub-sampling of the original dataset using 1000 randomly sampled datapoints (“fine-1000”) and (d) the “coarse” dataset rotated 90 degrees about the vertical axis in the pole figures (“coarse-rotated”). It is interesting to note that the textures that are similar “by-eye” show a general increase in the TDI as the series expansion rate increases. However, for very dissimilar textures (i.e “coarse” vs “coarse-rotated”) the jump to a large TDI is immediate.
Random vs Uniform Sampling
The differences between the random and uniform sampling were a bit curious so I decided to check the random points to see how they were positioned in the x-y space of the scan. The figure below compares the uniform and random sampling for 4000 datapoints – any more than this is hard to show. Clearly the random sampling is reasonable but does show a bit of clustering and gaps within the scan area. Some of these small differences show up with higher differences in TDI values than I would expect. Clearly, at L = 22 we are picking up quite subtle differences – at least subtle with respect to my personal “by-eye” judgement. It seems to me, that my “by-eye” judgement is biased toward lower rank series expansions.
Of course, another conclusion would be that my eyesight is getting rank with age I guess that explains my increasingly frequent need to reach for my reading glasses.
 SI Wright, MM Nowell & JF Bingert (2007) “A comparison of textures measured using X-ray and electron backscatter diffraction”. Metallurgical and Materials Transactions A, 38, 1845-1855
 SI Wright (2010) “A Parametric Study of Electron Backscatter Diffraction based Grain Size Measurements”. Practical Metallography, 47, 16-33.
Figure 1. Participants of my first EBSD training course in Grenoble in 2001.
Everybody is learning all the time. You start as a child at home and later in school and that never ends. In your professional career you will learn on the job and sometimes you will get the opportunity to get a dedicated training on some aspect of your work. I am fortunate that my job at EDAX involves a bit of this type of training for our customers interested in EBSD. Somehow, I have already found myself teaching for a long time without really aiming for it. Already as a teenager when I worked at a small local television station in The Netherlands I used to teach the technical things related to making television programs like handling cameras, lighting, editing – basically everything just as long as it was out of the spotlight. Then during my geology study, I assisted in teaching students a variety of subjects ranging from palaeontology to physics and geological fieldwork in the Spanish Pyrenees. So, unsurprisingly, shortly after joining EDAX in 2001 when I was supposed to simply participate in an introductory EBSD course (fig 1) taught by Dr. Stuart Wright in Grenoble, France, I quickly found myself explaining things to the other participants instead of just listening.
Teaching about EBSD often begins when I do a presentation or demonstration for someone new to the technique. And the capabilities of EBSD are such that just listing the technical specifications of an EBSD system to a new customer does not do it justice. Later when a system has been installed I meet the customers again for the dedicated training courses and workshops that we organise and participate in all over the world.
Figure 2. EBSD IPF map of Al kitchen foil collected without any additional specimen preparation. The colour-coding illustrates the extreme deformation by rolling.
In such presentations, of course we talk about the basics of the method and the characteristics of the EDAX systems, but then it always moves on to how it can help understand the materials and processes that the customer is working with. There, teaching starts working the other way as well. With every customer visit I learn something more about the physical world around us. Sometimes this is about a fundamental understanding of a physical process that I have never even heard of.
At other times it is about ordinary items that we see or use in our daily lives such as aluminium kitchen foil, glass panes with special coatings, or the structure of biological materials like eggs, bone, or shells. Aluminium foil is a beautiful material that is readily available in most labs and I use it occasionally to show EBSD grain and texture analysis when I do not have a suitable polished sample with me (fig 2) and at some point, a customer explained to me in detail how it was produced in a double layer back to back to get one shiny and one matte side. And that explained why it produces EBSD patterns without any additional preparation. Something new learned again.
Figure 3. IPF map of austenitic steel microstructure prepared by additive manufacturing.
A relatively new development is additive manufacturing or 3D printing where a precursor powdered material is melted into place by a laser to create complex components/shapes as a single piece. This method produces fantastically intricate structures (fig 3) that need to be studied to optimise the processing.
With every new application my mind starts turning to identify specific functions in the software that would be especially relevant to its understanding. In some cases, this then turns into a collaborative effort to produce scientific publications on a wide variety of subjects e.g. on zeolite pore structures (1, fig (4)), poly-GeSi films (2, fig (5)), or directional solidification by biomineralization of mollusc shells (3).
Figure 4. Figure taken from ref.1 showing EBSD analysis of zeolite crystals.
Figure 5. Figure taken from ref.2 showing laser crystallised GeSi layer on substrate.
Such collaborations continuously spark my curiosity and it is because of these kinds of discussions that after 17 years I am still fascinated with the EBSD technique and its applications.
This fascination also shows during the EBSD operator schools that I teach. The teaching materials that I use slowly evolve with time as the systems change, but still the courses are not simply repetitions. Each time customers bring their own materials and experiences that we use to show the applications and discuss best practices. I feel that it is true that you only really learn how to do something when you teach it.
This variation in applications often enables me to fully show the extent of the analytical capabilities in the OIM Analysis software and that is something that often gets lost in the years after a system has been installed. I have seen many times that when a new system is installed, the users invest a lot of time and effort in getting familiar with the system in order to get the most out of it. However, with time the staff that has been originally trained on the equipment moves on and new people are introduced to electron microscopy and all that comes with it. The original users then train their successor in the use of the system and inevitably something is lost at this point.
When you are highly familiar with performing your own analysis, you tend to focus on the bits of the software and settings that you need to perform your analysis. The bits that you do not use fade away and are not taught to the new user. This is something that I see regularly during the training course that I teach. Of course, there are the new functions that have been implemented in the software that users have not seen before, but people who have been using the system for years and are very familiar with the general operation always find new ways of doing things and discover new functions that could have helped them with past projects during the training courses. During the latest EBSD course in Germany in September a participant from a site where they have had EBSD for many years remarked that he was going to recommend coming to a course to his colleagues who have been using the system for a long time as he had found that the system could do much more than he had imagined.
You learn something new every day.
1) J Am Chem Soc. 2008 Oct 15;130(41):13516-7. doi: 10.1021/ja8048767. Epub 2008 Sep 19.
2) ECS Journal of Solid State Science and Technology, 1 (6) P263-P268 (2012)
3) Adv Mater. 2018 Sep 21:e1803855. doi: 10.1002/adma.201803855. [Epub ahead of print]
If you have attended an EDAX EBSD training course, you have seen the following slide in the Pattern Indexing lecture. This slide attempts to explain how to collect a background pattern before performing an OIM scan. The slide recommends that the background come from an area containing at least 25 grains.
Those of you who have performed re-indexing of a scan with saved patterns in OIM Analysis 8.1 may have noticed that there is a background pattern for the scan data (as well as one of the partitions). This can be useful if re-indexing a scan where the raw patterns were saved as opposed to background corrected patterns. This background pattern is formed by averaging 500 patterns randomly selected from the saved patterns. 500 is a lot more than the minimum of 25 recommended in the slide from the training lecture.
Recently, I was thinking about these two numbers – is 25 really enough, is 500 overkill? With some of the new tools (Callahan, P.G. and De Graef, M., 2013. Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), pp.1255-1265.) available for simulating EBSD patterns I realized this might be provide a controlled way to perhaps refine the number of orientations that need to be sampled for a good background. To this end, I created a set of simulated patterns for nickel randomly sampled from orientation space. The set contained 6,656 patterns. If you average all these patterns together you get the pattern at left in the following row of three patterns. The average patterns for 500 and 25 random patterns are also shown. The average pattern for 25 random orientations is not as smooth as I would have assumed but the one with 500 looks quite good.
I decided to take it a bit further and using the average pattern for all 6,656 patterns as a reference I compared the difference (simple intensity differences) between average patterns from n orientations vs. the reference. This gave me the following curve: From this curve, my intuitive estimate that 25 grains is enough for a good background appears be a bit optimistic., but 500 looks good. There are a few caveats to this, the examples I am showing here are at 480 x 480 pixels which is much more than would be used for typical EBSD scans. In addition, the simulated patterns I used are sharper and have better signal-to-noise ratios than we are able to achieve in experimental patterns at typical exposure times. These effects are likely to lead to more smoothing.
I recently saw Shawn Bradley who is one of the tallest players to have played in the NBA, he is 7’6” (229cm) tall. I recognized him because he was surrounded by a crowd of kids – you can imagine that he really stood out! This reminded me that these results assume a uniform grain size. If you have 499 tiny grains encircling one giant grain, then the background from these 500 grains will not work as a background as it would be dominated by the Shawn Bradley grain!
In interacting with Rudy Wenk of the University of California Berkeley to get his take on the word “texture” as it pertains to preferred orientation reminds me of some other terminologies with orientation maps that Rudy helped me with several years ago.
Map reconstructed form EBSD data showing the crystal orientation parallel to the sample surface normal
Joe Michael of Sandia National Lab has commented to me a couple of times his objection to the term “IPF map”. As you may know, the term is commonly used to describe a color map reconstructed from OIM data where the color denotes the crystallographic axis aligned with the sample normal as shown below. Joe points out that the term “orientation map” or “crystal direction map” or something similar would be much more appropriate and he is absolutely right.
The reason behind the name “IPF map”, is that I hi-jacked some of my code for drawing inverse pole figures (IPFs) as a basis to start writing the code to create the color-coded maps. Thus, we started using the term internally (it was TSL at the time – prior to EDAX purchasing TSL) and then it leaked out publicly and the name stuck – my apologies to Joe. We later added the ability to color the microstructure based on the crystal direction aligned with any specified sample direction as shown below.
Orientation maps showing the crystal directions aligned with the normal, rolling and transverse directions at the surface of a rolled aluminum sheet.
The idea for this map was germinated from a paper I saw presented by David Dingley where a continuous color coding schemed was devised by assigning red, green and blue to the three axes of Rodrigues-Frank space: D. J. Dingley, A. Day, and A. Bewick (1991) “Application of Microtexture Determination using EBSD to Non Cubic Crystals”, Textures and Microstructures, 14-18, 91-96. In this case, the microstructure had been digitized and a single orientation measured for each grain using EBSD. Unfortunately, I only have gray scale images of these results.
SEM micrograph of nickel, grain orientations in Rodrigues-Frank space and orientation map based on color Rodrigues vector coloring scheme. Source: Link labeled “Full-Text PDF” at www.hindawi.com/archive/1991/631843/abs/
IPF map of recrystallized grains in grain oriented silicon steel from Y. Inokuti, C. Maeda and Y. Ito (1987) “Computer color mapping of configuration of goss grains after an intermediate annealing in grain oriented silicon steel.” Transactions of the Iron and Steel Institute of Japan 27, 139-144. Source: Link labeled “Full Text PDF button’ at www.jstage.jst.go.jp/article/isijinternational1966/27/4/27_4_302/_article
We didn’t realize it at the time; but, an approach based on the crystallographic direction had already been done in Japan. In this work, the stereographic unit triangle (i.e. an inverse pole figure) was used in a continues color coding scheme were red is assigned to the <110> direction, blue to <111> and yellow to <100> and then points lying between these three corners of the stereographic triangle are combinations of these three colors. This color coding was used to shade grains in digitized maps of the microstructure according to their orientation. Y. Inokuti, C. Maeda and Y. Ito (1986) “Observation of Generation of Secondary Nuclei in a Grain Oriented Silicon Steel Sheet Illustrated by Computer Color Mapping”, Journal of the Japan Institute of Metals, 50, 874-8. The images published in this paper received awards in 1986 by the Japanese Institute of Metals and TMS.
AVA map and pole figure from a quartz sample from “Gries am Brenner” in the Austrian alps south of Innsbruck. The pole figure is for the c-axis. (B. Sander (1950) Einführung in die Gefügekunde der Geologischen Körper: Zweiter Teil Die Korngefüge. Springer-Vienna) Source: In the last chapter (Back Matter) in the Table of Contents there is a link labeled “>> Download PDF” at link.springer.com/book/10.1007%2F978-3-7091-7759-4
I thought these were the first colored orientation maps constructed until Rudy later corrected me (not the first, nor certainly the last time). He sent me some examples of mappings of orientation onto a microstructure by “hatching” or coloring a pole figure and then using those patterns or colors to shade the microstructure as traced from micrographs. H.-R. Wenk (1965) “Gefügestudie an Quarzknauern und -lagen der Tessiner Kulmination”, Schweiz. Mineralogische und Petrographische Mitteilungen, 45, 467-515 and even earlier in B. Sander (1950) Einführung in die Gefügekunde Springer Verlag. 402-409 . Sanders entitled this type of mapping and analysis as AVA (Achsenvertilungsanalyse auf Deutsch or Axis Distribution Analysis in English).
Such maps were forerunners to the “IPF maps” of today (you could actually call them “PF maps”) to which we are so familiar with. It turns out our wanderin’s in A Search for Structure (Cyril Stanley Smith, 1991, MIT Press) have actually not been “aimless” at all but have helped us gain real insight into that etymologically challenged world of microstructure.