OIM

EBSD in a vacuum

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

I recently co-authored a paper with my colleagues Will Lenthe and Matt Nowell that focused on our parent grain reconstruction tool in OIM Analysis™ [1]. As part of that paper, we show the results from a little round-robin we did. I also showed some results in my webinar on parent microstructure reconstruction in January 2021.

Participating in a round-robin is always a bit unnerving as you are never completely sure how your work will stand up relative to others – especially for those well-recognized experts. This was not an officially moderated round-robin, but rather, me asking other researchers in the area that I happen to have had the good fortune of interacting with in the past if they would be willing to contribute. For the round-robin, the same input EBSD dataset was used for each algorithm. The EBSD dataset was obtained from a low-carbon steel rolled-sheet sample with a fully transformed ferrite body-centered cubic (bcc) microstructure, as shown in Figure 1.

a) Crystal orientation (IPF) map for a ferrite microstructure in a low carbon steel, b) color scheme for the IPF map.

Figure 1. a) Crystal orientation (IPF) map for a ferrite microstructure in a low carbon steel, b) color scheme for the IPF map.

This dataset was used as the input to the parent reconstruction tool in OIM Analysis, as well as several other reconstruction tools. Figure 2 shows the reconstruction results.

Figure 2. IPF Maps of the parent austenite microstructure reconstructed using a) OIM Analysis [2], b) Merengue [3], c) Graph Cutting [4] and d) ROPA [5].

Generally, the results are in reasonable agreement, e.g., the grain sizes and orientations (colors) are in general agreement. The results suggest that if these algorithms were applied to the input dataset obtained from a larger area, then the textures and grain size statistics would all be expected to be quite similar. The differences tend to be in the details, particularly at the boundaries between grains. Our paper discusses some of the nuances of the different algorithms that lead to the differences in reconstruction results.

In the paper, we briefly acknowledge each of those who were kind enough to provide us with the reconstruction results using the different algorithms. However, I want to add a little more detail about the contributors.

The original dataset came to me via Stephen Cluff when he was a Ph.D. researcher in Professor David Fullwood’s group at Brigham Young University working on austenite reconstruction (https://scholarsarchive.byu.edu/etd/9051/). Stephen is now a Materials Engineer at the U.S. Army Research Lab.

The original dataset was collected and shared by Matt Merwin at U. S. Steel. Matt and I co-organized a symposium on EBSD analysis of steel for the 2009 TMS meeting.

The dataset was used in a paper by Chasen Ranger and co-workers on austenite reconstruction (Ranger, C., Tari, V., Farjami, S., Merwin, M.J., Germain, L. and Rollett, A., 2018. Austenite reconstruction elucidates prior grain size dependence of toughness in a low alloy steel. Metallurgical and Materials Transactions A, 49, pp.4521-4535.).

Anthony Rollett is the last author on this paper. I have known Tony for many years – he was my ‘boss’ in my first job out of school as a post-doc at Los Alamos National Lab and is now the U.S. Steel Professor of Metallurgical Engineering and Materials Science at Carnegie Mellon University. I reached out to Tony for data from the paper, and he kindly supplied reconstruction results on the austenite dataset obtained using Lionel Germain’s Merengue code. Lionel is at the University of Lorraine in France, which is where the next ICOTOM will be held (https://icotom20.sciencesconf.org/).

I saw a presentation on parent reconstruction using Graph Cutting by Stephen Niezgoda of Ohio State University (OSU), so I asked if he would apply his algorithm to this dataset. He kindly responded, and his student Charles Xu supplied me with the results from their algorithm. I have known Stephen for many years and have had the opportunity to visit his research group at OSU.

I was also aware of an algorithm from Goro Miyamoto called ROPA. I asked my Japanese colleagues Seichii Suzuki and Tatsuya Fukino of TSL Solutions KK, who are familiar with the ROPA software, if they would run the same dataset, and they kindly obliged. I’ve had the good fortune of enjoying many trips to Japan to visit with my colleagues at TSL Solutions and had the opportunity to host them in Utah.

Why the shameless “name-dropping”?

First, it is good to see the agreement between the different algorithmic approaches to the reconstruction problem. While there are certainly differences between the results, the overall reconstructed microstructures are quite similar.
Second, I have interacted with many of these researchers through a shared interest in EBSD and personal connections that started during my graduate school research under Professor Brent L. Adams. I did a Master of Science degree at BYU and a Ph.D. at Yale University under Brent’s guidance, both of which focused on EBSD. Many of the researchers listed here have worked and published with Brent Adams.

So, my second point is to emphasize that while EBSD is performed in a vacuum – science is much more fruitful and enjoyable when not performed in a vacuum. The connections we build through our interactions with others in the research community are essential to moving science forward – it is good to attend conferences again after the COVID-enforced hiatus.

References

  1. Wright, S.I., Lenthe, W.C., Nowell, M.M. Parent Grain Reconstruction in an Additive Manufactured Titanium Alloy, Metals, 2023, 14, 51. DOI: https://doi.org/10.3390/met14010051.
  2. Ranger, C.; Tari, V.; Farjami, S.; Merwin, M.J.; Germain, L.; Rollett, A. Austenite reconstruction elucidates prior grain size dependence of toughness in a low alloy steel. Metall Mater Trans A 2018, 49, 4521-4535. DOI: https://doi.org/10.1007/s11661-018-4825-7.
  3. Germain, L.; Gey, N.; Mercier, R.; Blaineau, P.; Humbert, M. An advanced approach to reconstructing parent orientation maps in the case of approximate orientation relations: Application to steels. Acta Mater 2012, 60, 4551-4562. DOI: https://doi.org/10.1016/j.actamat.2012.04.034.
  4. Brust, A.; Payton, E.; Hobbs, T., Sinha, V.; Yardley, V.; Niezgoda, S. Probabilistic reconstruction of austenite microstructure from electron backscatter diffraction observations of martensite. Microsc Microanal 2021, 27, 1035-1055. DOI: https://doi.org/10.1017/S1431927621012484.
  5. Miyamoto, G.; Iwata, N.; Takayama, N.; Furuhara, T. Mapping the parent austenite orientation reconstructed from the orientation of martensite by EBSD and its application to ausformed martensite. Acta Mater 2010, 58, 6393-6403. DOI: https://doi.org/10.1016/j.actamat.2010.08.001.

It runs (or rolls) in the family

Matt Nowell, EBSD Product Manager, Gatan/EDAX

I have two sons graduating this year. My oldest son is graduating college with a Materials Science and Engineering degree and is interested in materials characterization. My middle son is graduating high school and has grown up refining ores in Minecraft, casting characters from Dungeons and Dragons, and 3D printing school projects. I’m glad they are both interested in materials and how they can affect daily living. I’ve also been a little sentimental and nostalgic thinking about how we have tried to learn more about materials in our household.

One activity they have always enjoyed is collecting pressed coins. These machines squeeze a coin between two rollers, one of which has an engraving on its surface that is then imprinted onto the stretched and flattened surface of the deformed coin. We have collected these coins from around the world. One example is shown in Figure 1, which is a pressed coin from Universal Studios. This was the most recent addition to the collection. I decided to press a second coin that we could prepare and characterize with electron backscatter diffraction (EBSD) to see the microstructural developments that occur during the pressing process.

Figure 1. A pressed coin from Universal Studios.

The pressed coin was mechanically polished down to 0.02 µm colloidal silica and then analyzed using the new EDAX Velocity Ultra EBSD detector. This new detector allowed for high-speed data collection at acquisition rates of 6,500 indexed patterns per second. Figure 2 shows the inverse pole figure (IPF) orientation map collected from a 134 µm x 104 µm area with a 100 nm step size, with the coloring relative to the orientations aligned with the sample’s surface normal direction. At these speeds, the acquisition time was less than five minutes. A copper blank was used instead of the traditional penny for this sample. This was noticeable when indexing the EBSD patterns. Since 1982, pennies have been made of zinc coated with copper. Zinc has a hexagonal crystal structure, while the EBSD patterns from this coin were face-centered cubic (FCC). EDS analysis confirmed that the material was copper.

Figure 2. An IPF orientation map collect from a 134 µm x 104 µm area of the pressed coin with a 100 nm step size. The coloring is relative to the orientations aligned with the sample’s surface normal direction.

The IPF map shows a significant amount of deformation. This can be seen in the IPF maps with the color variation within each grain. This is, of course, expected, as the elongation and thinning of the coin are easily observed while watching the machine. EBSD is an ideal tool for characterizing this deformation within the material. While there are several different map types to visualize local misorientations and deformation, Figure 3 shows one of my favorites, the grain reference orientation deviation (GROD) map. In this map, the grains are first calculated by grouping measurements of similar orientation using a 5° tolerance angle. Next, the average orientation of each grain is calculated. Finally, each pixel within a grain is colored according to its misorientation from the average orientation of its grain. The microstructure’s largest GROD angular value is 61.9°, indicating a large spread of orientations. This map also shows the grain boundaries as black lines to indicate the original grain boundary positions.

Figure 3. A GROD map of the pressed coin.

Figure 4 shows a fascinating view of how the material is deformed within a selected grain. This chart was created by drawing a line within a grain and plotting the point-to-point and point-to-origin misorientations along this line. The point-to-point distribution shows that each step is typically a small misorientation value below the grain tolerance angle. The point-to-origin distribution shows an accumulation of misorientations within this grain, with the overall misorientation changing more than 30° over the 25 µm distance within the grain. This type of result always gets me thinking about what a grain really is in a deformed material.

Figure 4. A view of how the material is deformed within a selected grain. This chart was created by drawing a line within a grain and plotting the point-to-point and point-to-origin misorientations along this line.

Figure 5. The (001), (111), and (110) pole figures calculated from the measured orientations.

Figure 5 shows the (001), (111), and (110) pole figures calculated from the measured orientations. These pole figures are incomplete and resemble what is expected for a rolled FCC material. This is due to the small number of grains sampled in this area. A second map was collected over a 1,148 µm x 895 µm area with a 2 µm step size in under a minute to get a better sampling of the entire microstructure. The pole figures for this data are shown in Figure 6. Comparing Figures 5 and 6 shows that the additional sampling within the second scan adds more symmetry to the pole figures.

Figure 6. The pole figures for the second map that was collected over a 1,148 µm x 895 µm area with a 2 µm step size.

This was a fun example to show the different data types that can be derived from EBSD measurements. In materials science, understanding the relationship between materials processing and the resulting microstructure is critical to understanding the material’s final properties. It’s clear that pressing a coin causes significant deformation within the material, which can then be measured and quantified with EBSD. Maybe the next time we go to the zoo, we will vary the speed at which we roll the coins and see what effect that has on the data.

Being more precise

Dr. Stuart Wright, Senior Scientist, Gatan/EDAX

The precision and accuracy of orientation measurements by electron backscatter diffraction (EBSD) have been of interest since the advent of EBSD [1, 2]. In contrast, reliability (in terms of correctly identifying the orientation at least within 5°) was of greater concern when indexing was first automated (there is a section of my thesis [3] devoted to precision, as well as Krieger Lassen’s thesis [4]). I’ve written a few papers on the subject [5 – 7], and there have been several more by other authors [8 – 11]. High-resolution EBSD (HREBSD) has shown success in markedly improving precision [12]. Now that dictionary indexing (DI) has become more common; there has been a resurgence in papers on the precision that can be achieved using DI [13 – 15]. I know that is a lot of references for a blog post, but I wanted to give you an idea of how many different research groups have studied angular precision in EBSD measurements – the references given are only a sampling; there are certainly more.

Will Lenthe and I have been working hard to improve the dictionary indexing capabilities in the EDAX OIM Matrix™ add-on module to EDAX OIM Analysis™. In addition, Will has added the ability to perform spherical indexing within OIM Matrix [16 – 17] (see Will’s “New Tools for EBSD Data Collection and Analysis” webinar for more information). These new capabilities will be available soon in OIM Analysis 9. I’m excited about the progress we’ve made. You will find OIM Matrix much easier to use and more robust. In addition, we’ve sped up many aspects of OIM Analysis, which will help with the big datasets routinely obtained with the EDAX Velocity™ cameras.

The precision of indexing via spherical indexing has recently been explored [18]. Using OIM Analysis 9, we’ve been exploring what we can achieve in terms of orientation precision with orientation refinement [19 – 21] applied to initial indexing results obtained by Hough transform-based indexing, dictionary indexing, and spherical indexing. We haven’t quantified our results yet. Still, the KAM maps (which indicate the orientation precision) we’ve obtained are so promising that I want to show our preliminary results. Our refinement method is essentially a hybrid of that proposed by Singh, Ram, and De Graef [19] and Pang, Larsen, and Schuh [21]. But for the spherical indexing, we also have implemented an additional refinement in the harmonic frequency space. Figure 1 shows some results I am excited to share.

Figure 1. KAM maps from nickel [22]. (Top row) As-indexed, (middle row) with NPAR for Hough-based indexing and refinement in the spherical harmonics for spherical indexing, and (bottom row) after real-space refinement. The first column is for Hough-based indexing, columns 2 – 4 are for dictionary indexing with different dictionary target disorientations, and columns 5 – 6 are for SI with different harmonic bandwidths.

It is pretty interesting that the KAM maps after refinement are all nearly the same, no matter which type of indexing was used to obtain the initial orientation measurements. We do not expect much plastic strain or permanent deformation in these samples, so the reduced KAM values are more of what we expect for the sample.

Here is another set of results for a silicon single crystal. The scan is approximately 1 x 1 mm with a 30 m step size. You can see the dramatic improvement in these results. Unfortunately, the two points with the largest KAM values are due to some dust particles on the sample’s surface.

Figure 2. KAM maps were constructed using Hough-based indexing, SI, and SI followed by refinement.

We are very excited to get these advancements into your hands and are putting in extra hours to get the software ready for release. We hope you are as precisely excited as we are to apply it to your samples!

[1] Harland CJ, Akhter P, Venables JA (1981) Accurate microcrystallography at high spatial resolution using electron backscattering patterns in a field emission gun scanning electron microscope. Journal of Physics E 14:175-182
[2] Dingley DJ (1981) A Comparison of Diffraction Techniques for the SEM. Scanning Electron Microscopy IV: 273-286
[3] Wright SI (1992) Individual Lattice Orientation Measurements Development and Applications of a Fully Automatic Technique. Ph.D. Thesis., Yale University.
[4] Krieger Lassen NC (1994) Automated Determination of Crystal Orientations from Electron Backscattering Patterns. Ph.D. Thesis, Danmarks Tekniske Universitet.
[5] Wright S, Nowell M (2008) High-Speed EBSD. Advanced Materials and Processes 66: 29-31
[6] Wright SI, Basinger JA, Nowell MM (2012) Angular precision of automated electron backscatter diffraction measurements. Materials Science Forum 702: 548-553
[7] Wright SI, Nowell MM, de Kloe R, Chan L (2014) Orientation Precision of Electron Backscatter Diffraction Measurements Near Grain Boundaries. Microscopy and Microanalysis 20:852-863
[8] Humphreys FJ, Huang Y, Brough I, Harris C (1999) Electron backscatter diffraction of grain and subgrain structures – resolution considerations. Journal of Microscopy – Oxford 195:212-216.
[9] Demirel MC, El-Dasher BS, Adams BL, Rollett AD (2000) Studies on the Accuracy of Electron Backscatter Diffraction Measurements. In: Schwartz AJ, Kumar M, Adams BL (eds) Electron Backscatter Diffraction in Materials Science. Kluwer Academic/Plenum Publishers, New York, pp 65-74.
[10] Godfrey A, Wu GL, Liu Q (2002) Characterisation of Orientation Noise during EBSP Investigation of Deformed Samples. In: Lee DN (ed) ICOTOM 13, Seoul, Korea, Textures of Materials. Trans Tech Publications Inc., pp 221-226.
[11] Ram F, Zaefferer S, Jäpel T, Raabe D (2015) Error analysis of the crystal orientations and disorientations obtained by the classical electron backscatter diffraction technique. Journal of Applied Crystallography 48: 797-813
[12] Wilkinson AJ, Britton TB (2012) Strains, planes, and EBSD in materials science. Materials Today 15: 366-376
[13] Ram F, Singh S, Wright SI, De Graef M (2017) Error Analysis of Crystal Orientations Obtained by the Dictionary Approach to EBSD Indexing. Ultramicroscopy 181:17-26.
[14] Nolze G, Jürgens M, Olbricht J, Winkelmann A (2018) Improving the precision of orientation measurements from technical materials via EBSD pattern matching. Acta Materialia 159:408-415
[15] Shi Q, Loisnard D, Dan C, Zhang F, Zhong H, Li H, Li Y, Chen Z, Wang H, Roux S (2021) Calibration of crystal orientation and pattern center of EBSD using integrated digital image correlation. Materials Characterization 178:111206
[16] Lenthe W, Singh S, De Graef M (2019) A spherical harmonic transform approach to the indexing of electron backscattered diffraction patterns. Ultramicroscopy 207:112841
[17] Hielscher R, Bartel F, Britton TB (2019) Gazing at crystal balls: Electron backscatter diffraction pattern analysis and cross-correlation on the sphere. Ultramicroscopy 207:112836
[18] Sparks G, Shade PA, Uchic MD, Niezgoda SR, Mills MJ, Obstalecki M (2021) High-precision orientation mapping from spherical harmonic transform indexing of electron backscatter diffraction patterns. Ultramicroscopy 222:113187
[19] Singh S, Ram F, De Graef M (2017) Application of forward models to crystal orientation refinement. Journal of Applied Crystallography 50:1664-1676.
[20] Winkelmann A, Jablon BM, Tong V, Trager‐Cowan C, Mingard K (2020) Improving EBSD precision by orientation refinement with full pattern matching. Journal of Microscopy 277:79-92
[21] Pang EL, Larsen PM, Schuh CA (2020) Global optimization for accurate determination of EBSD pattern centers. Ultramicroscopy 209:112876
[22] Wright SI, Nowell MM, Lindeman SP, Camus PP, De Graef M, Jackson MA (2015) Introduction and comparison of new EBSD post-processing methodologies. Ultramicroscopy 159:81-94

Reaching Out

Dr. René de Kloe, Applications Scientist, EDAX

2022 was a year of changes. In the beginning, I set up a desk in the scanning electron microscope (SEM) lab where, without truly reaching out, I only needed to turn in my chair to switch from emails and virtual customers on my laptop to the live energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD) system and real data on the microscope. As travel restrictions gradually eased worldwide, we were all able to start meeting “real” people again. After almost two years of being grounded, I finally met people face to face again, discussing their analysis needs, and answering questions do not compare to online meetings. We restarted in-person training courses, and I participated in many external courses, exhibitions, and conferences, reaching out to microscopists all over Europe.

And as always, I try to correlate real life with some nice application examples. And what is similar to reaching out to people in the microanalysis world? Reaching out to things! So, what came to mind are remote thermal sensors, which most of us will have at home in the kitchen: a thermostat in an oven and a wired thermometer that you can use to measure food temperatures. And I just happened to have a broken one that was ready to be cut up and analyzed.

Figure 1. a) A food thermometer and b) an oven thermostat sensor.

On the outside, these two sensors looked very similar; both were thin metal tubes connected to a control unit. Because of this similarity, I was also expecting more or less the same measuring method, like using a thermocouple in both thermometers. But to my surprise, that was not quite the case.

The long tube of the food thermometer was mostly empty. Right at the tip, I found this little sensor about 1 mm across connected to copper wires that led to the control unit. After mounting and careful sectioning, I could collect EDS maps showing that the sensor consisted of a central block of Mn-Co-Fe-oxide material sandwiched between silver electrodes soldered to the copper-plated Ni wires.

Note that in the image, you only see one of the wires, the other is still below the surface, and I did not want to polish it any deeper.

Figure 2. The temperature sensor taken out of the tube of the food thermometer.

Figure 3. A forward scatter SEM image of the polished cross-section showing the central MnCoFe-oxide material and one of the connecting wires.

This was no thermocouple.

Figure 4. The element distribution in the sensor.

Figure 5. The EDS spectrum of the central CoMnFe-oxide area.

Instead, the principle of this sensor is based on measuring the changing resistivity with temperature. The EBSD map of the central Co-Mn-De oxide area shows a coarse-grained structure without any preferred orientation to make the resistivity uniform in all directions.

Figure 6. An EBSD IPF on Image Quality map of the sensor in the food thermometer.

Figure 7. (001) pole figure of the MnCoFe oxide phase, showing a random orientation distribution.

And where the tube of the food thermometer was mostly empty, the tube of the oven thermostat sensor was completely empty. There were not even electrical connections. The sensor was simply a thin hollow metal tube that contained a gas that expands when heated. This expansion would move a small disk with a measurement gauge that was then correlated with a temperature readout. Although this sounded very simple, some clever engineering was needed to prevent the tube from pinching shut when bending and moving it during installation.

I cut and polished the tube, and an EBSD map of the entire cross-section is shown below.

Figure 8. a) EBSD IQ and b) IPF maps of a cross-section through the entire tube of the oven thermostat sensor.

The tube is constructed out of three layers of a Fe-Cr-Ni alloy with fine-grained multiphase chromium phosphide layers in between. This microstructure is what provides corrosion protection, and it also adds flexibility to the tube. And this, in turn, is crucial to prevent cracks from forming that would cause the leaking of the contained gas, which is critical in getting a good temperature reading.

The detailed map below shows a section of the phosphide layer. There are two chromium phosphide phases, and in between, there are dendritic Ni grains that link everything together.

Figure 9. EDS maps showing the composition of one of the phosphide layers.

Figure 10. EBSD IPF maps of the different phases. a) All phases on a PRIAS center image, b) CrP, c) Fe matrix, and d) Ni dendrites, Cr3P.

When you look at the microstructure of both sensors in detail, it is possible to determine how they work, and you can appreciate why they have been designed as they are. The two devices are efficient and tailored to their intended use. The oven thermostat is designed to be mounted in a fixed position to be secure so that it can be used for a very long time. The food thermometer is very flexible and can easily be moved around.

In that respect, I feel there is another similarity between these sensors and the different kinds of meetings between people we have experienced over the past year. It does not matter how you do it; you can always reach out and feel some warmth.

I wish everybody a very happy and peaceful 2023.

Stimulating Simulations

Dr. Stuart Wright, Senior Scientist, EDAX

It has been an interesting experience to build our OIM Matrix™ software package. As you may know, OIM Matrix is partially a front-end user interface to the EMsoft package developed by Professor Marc De Graef’s group at Carnegie Mellon University to make it convenient to use within the framework of OIM Analysis™. I have learned a lot in the process and am grateful for Marc’s patience with my many questions. Will Lenthe recently joined the EBSD group at EDAX. Will worked as a Post-Doc in Marc’s group, and his additional insights have been invaluable as we are striving to build the second generation of OIM Matrix. It will be easier to use, more robust, and provide some significant speed gains.

While our initial focus for OIM Matrix was on helping users improve the indexing of EBSD patterns from difficult-to-index materials, I’ve been surprised by how useful it has been for testing our software. It has also helped us in developing some of our new features. Having well-simulated patterns for known orientations and EBSD/SEM geometries is very helpful.

I used OIM Matrix for a study on feldspars. According to Wikipedia:

“Feldspars are a group of rock-forming aluminum tectosilicate minerals containing sodium, calcium, potassium, or barium. The most common members of the feldspar group are the plagioclase (sodium-calcium) feldspars and the alkali (potassium-sodium) feldspars. Feldspars make up about 60% of the Earth’s crust and 41% of the Earth’s continental crust by weight.”

Given that feldspars are relatively common, we are frequently asked to help index them. They are difficult, as a poster at the 2019 Quantitative Microanalysis (QMA) conference detailed [1]. I thought it might be interesting to see what we could learn about the limits of EBSD in characterizing these materials. I won’t give you all that we learned in that little study, but what I thought was an interesting snapshot. Figure 1 shows a phase diagram for the feldspar group of minerals.

Figure 1. Phase diagram for the feldspar group.

To start, I looked in the American Mineralogist Crystal Structure Database (AMCSD) for all the relevant entries I could find. There are a lot of variants. Here is a table:

Table 1. Number of entries in AMCSD for each feldspar.

I enjoy seeing pattern simulation results, but producing 149 master patterns [2] would take more patience than I have (each master pattern calculation can take several hours for these low-symmetry materials). So, I selected one entry for each mineral type. I tried to find one that seemed most representative of all the other entries in the set. After calculating the eight master patterns, I simulated one individual pattern at the same orientation for each mineral, as shown in Figure 2. Note that they are all similar, with the most deviation coming from the anorthite and sanidine end members of the series.

Figure 2. Patterns were simulated at Euler angles of (30°, 30°, 30°) for each feldspar.

To quantify the differences, I calculated the normalized dot-products [3] for all pattern pairs to get the following table. A value of “1” indicates the patterns are identical. As expected by the initial observation, the biggest difference is the sanidine to albite pair of patterns.

Table 2. Normalized dot products.

Of course, the next step would be to see how this holds up to experimental patterns and dictionary indexing [4]. I hope to eventually do this with samples Professor Rudy Wenk of Stanford University kindly gave me. Rudy has been one of the major contributors to the entries in the AMCSD for feldspars.

There was one more virtual experiment I thought would be interesting. I wanted to ascertain how much the chemical species in the feldspar series influenced the patterns. To do this, I created an average structure instead of using the lattice parameters for each feldspar. I then populated these structures with atoms to maintain the chemical composition ratios specified for each series. A master pattern for each ideal structure was calculated. Three hundred forty patterns were simulated uniformly, covering orientation space with a spacing of approximately 30° between orientations. The average normalized dot products were calculated for each pattern against the albite pattern at the same orientation. Figure 3 shows the results.

Figure 3. The normalized dot product of simulated patterns for idealized structures against the albite simulated patterns.

Clearly, the dot products are all very near 1, indicating that the differences in the simulated patterns due to chemical composition are small for these chemical species. This suggests that coupling EBSD with EDS is critical when trying to differentiate the different feldspar minerals. While this small study has not changed the world of feldspar indexing, it has, at least, been a stimulating study of simulating for me.

[1] B Schneider, and J Fournelle (2019) “Using Quantitative and Qualitative Analysis to Confirm Phase Identities for Large Area EBSD Mapping of Geological Thin Sections” Poster at Microanalysis Society Topical Conference: Quantitative Microanalysis, University of Minnesota, Minneapolis MN, June 2019.

[2] PG Callahan, and M De Graef (2013) “Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations” Microscopy and Microanalysis, 19, 1255-1265.

[3] S Singh, and M De Graef (2016) “Orientation sampling for dictionary-based diffraction pattern indexing methods” Modelling and Simulation in Materials Science and Engineering, 24, 085013.

[4] K Marquardt, M De Graef, S Singh, H Marquardt, A Rosenthal, and S Koizuimi (2019) “Quantitative electron backscatter diffraction (EBSD) data analyses using the dictionary indexing (DI) approach: Overcoming indexing difficulties on geological materials” American Mineralogist: Journal of Earth and Planetary Materials, 102, 1843-1855.

Hats Off/On to Dictionary Indexing

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

Recently I gave a webinar on dynamic pattern simulation. The use of a dynamic diffraction model [1, 2] allows EBSD patterns to be simulated quite well. One topic I introduced in that presentation was that of dictionary indexing [3]. You may have seen presentations on this indexing approach at some of the microscopy and/or materials science conferences. In this approach, patterns are simulated for a set of orientations covering all of orientation space. Then, an experimental pattern is tested against all of the simulated patterns to find the one that provides the best match with the experimental pattern. This approach does particularly well for noisy patterns.

I’ve been working on implementing some of these ideas into OIM Analysis™ to make dictionary indexing more streamlined for datasets collected using EDAX data collection software – i.e. OIM DC or TEAM™. It has been a learning experience and there is still more to learn.

As I dug into dictionary indexing, I recalled our first efforts to automate EBSD indexing. Our first attempt was a template matching approach [4]. The first step in this approach was to use a “Mexican Hat” filter. This was done to emphasize the zone axes in the patterns. This processed pattern was then compared against a dictionary of “simulated” patterns. The simulated patterns were simple – a white pixel (or set of pixels) for the major zone axes in the pattern and everything else was colored black. In this procedure the orientation sampling for the dictionary was done in Euler space.
It seemed natural to go this route at the time, because we were using David Dingley’s manual on-line indexing software which focused on the zone axes. In David’s software, an operator clicked on a zone axis and identified the <uvw> associated with the zone axis. Two zone axes needed to be identified and then the user had to choose between a set of possible solutions. (Note – it was a long time ago and I think I remember the process correctly. The EBSD system was installed on an SEM located in the botany department at BYU. Our time slot for using the instrument was between 2:00-4:00am so my memory is understandably fuzzy!)

One interesting thing of note in those early dictionary indexing experiments was that the maximum step size in the sampling grid of Euler space that would result in successful indexing was found to be 2.5°, quite similar to the maximum target misorientation for modern dictionary indexing. Of course, this crude sampling approach may have led to the lack of robustness in this early attempt at dictionary indexing. The paper proposed that the technique could be improved by weighting the zone axes by the sum of the structure factors of the bands intersecting at the zone axes.
However, we never followed up on this idea as we abandoned the template matching approach and moved to the Burn’s algorithm coupled with the triplet voting scheme [5] which produced more reliable results. Using this approach, we were able to get our first set of fully automated scans. We presented the results at an MS&T symposium (Microscale Texture of Materials Symposium, Cincinnati, Ohio, October 1991) where Niels Krieger-Lassen also presented his work on band detection using the Hough transform [6]. After the conference, we hurried back to the lab to try out Niels’ approach for the band detection part of the indexing process [7].
Modern dictionary indexing applies an adaptive histogram filter to the experimental patterns (at left in the figure below) and the dictionary patterns (at right) prior to performing the normalized inner dot-product used to compare patterns. The filtered patterns are nearly binary and seeing these triggered my memory of our early dictionary work as they reminded me of the nearly binary “Sombrero” filtered patterns– Olé!
We may not have come back full circle but progress clearly goes in steps and some bear an uncanny resemblance to previous ones. I doff my hat to the great work that has gone into the development of dynamic pattern simulation and its applications.

[1] A. Winkelmann, C. Trager-Cowan, F. Sweeney, A. P. Day, P. Parbrook (2007) “Many-Beam Dynamical Simulation of Electron Backscatter Diffraction Patterns” Ultramicroscopy 107: 414-421.
[2] P. G. Callahan, M. De Graef (2013) “Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern Simulations” Microscopy and Microanalysis 19: 1255-1265.
[3] S.I. Wright, B. L. Adams, J.-Z. Zhao (1991). “Automated determination of lattice orientation from electron backscattered Kikuchi diffraction patterns” Textures and Microstructures 13: 2-3.
[4] Y.H. Chen, S. U. Park, D. Wei, G. Newstadt, M.A. Jackson, J.P. Simmons, M. De Graef, A.O. Hero (2015) “A dictionary approach to electron backscatter diffraction indexing” Microscopy and Microanalysis 21: 739-752.
[5] S.I. Wright, B. L. Adams (1992) “Automatic-analysis of electron backscatter diffraction patterns” Metallurgical Transactions A 23: 759-767.
[6] N.C. Krieger Lassen, D. Juul Jensen, K. Conradsen (1992) “Image processing procedures for analysis of electron back scattering patterns” Scanning Microscopy 6: 115-121.
[7] K. Kunze, S. I. Wright, B. L. Adams, D. J. Dingley (1993) “Advances in Automatic EBSP Single Orientation Measurements.” Textures and Microstructures 20: 41-54.

From Collecting EBSD at 20 Patterns per second (pps) to Collecting at 4,500 pps

John Haritos, Regional Sales Manager Southwest USA. EDAX

I recently had the opportunity to host a demo for one of my customers at our Draper, Utah office. This was a long-time EDAX and EBSD user, who was interested in seeing our new Velocity CMOS camera, and to try it on some of their samples.

When I started in this industry back in the late 90s, the cameras were running at a “blazing” 20 points per second and we all thought that this was fast. At that time, collection speed wasn’t the primary issue. What EBSD brought to the table was automated orientation analysis of diffraction patterns. Now users could measure orientations and create beautiful orientation maps with the push of a button, which was a lot easier than manually interpreting these patterns.

Fast forward to 2019 and with the CMOS technology being adapted from other industries to EBSD we are now collecting at 4,500 pps. What took hours and even days to collect at 20 pps now takes a matter of minutes or seconds. Below is a Nickel Superalloy sample collected at 4,500 pps on our Velocity™ Super EBSD camera. This scan shows the grain and twinning structure and was collected in just a few minutes.

Figure 1: Nickel Superalloy

Of course, now that we have improved from 20 pps to 4,500 pps, it’s significantly easier to get a lot more data. So the question becomes, how do we analyze all this data? This is where OIM Analysis v8™ comes to the rescue for the analysis and post processing of these large data sets. OIM Analysis v8™ was designed to take advantage of 64 bit computing and multi-threading so the software can handle large datasets. Below is a grain size map and a grain size distribution chart from an Aluminum friction stir weld sample with over 7 Million points collected with the Velocity™ and processed using OIM Analysis v8™. This example is interesting because the grains on the left side of the image are much larger than the grains on the right side. With the fast collection speeds, a small (250nm) step size could still be used over this larger collection area. This allows for accurate characterization of grain size across this weld interface, and the bimodal grain size distribution is clearly resolved. With a slower camera, it may be impractical to analyze this area in a single scan.

Figure 2: Aluminum Friction Stir Weld

In the past, most customers would setup an overnight EBSD run. You could see the thoughts running through their mind: will my sample drift, will my filament pop, what will the data look like when I come back to work in the morning? Inevitably, the sample would drift, or the filament would pop and this would mean the dreaded “ugh” in the morning. With the Velocity™ and the fast collection speeds, you no longer need to worry about this. You can collect maps in a few minutes and avoid this issue in practice. It’s a hard thing to say in a brochure, but its easy to appreciate when seeing it firsthand.

For me, watching my customer see the analysis of many samples in a single day was impressive. These were not particularly easy samples. They were solar cell and battery materials, with a variety of phases and crystal structures. But under similar conditions to their traditional EBSD work, we could collect better quality data much faster. The future is now. Everyone is excited with what the CMOS technology can offer in the way of productivity and throughput for their EBSD work.

Back to Basics

Dr. René de Kloe, Applications Specialist, EDAX

When you have been working with EBSD for many years it is easy to forget how little you knew when you started. EBSD patterns appear like magic on your screen, indexing and orientation determination are automatic, and you can produce colourful images or maps with a click of a mouse.

Image 1: IPF on PRIAS™ center EBSD map of cold-pressed iron powder sample.

All the tools to get you there are hidden in the EBSD software package that you are working with and as a user you don’t need to know exactly how all of it happens. It just works. To me, although it is my daily work, it is still amazing how easy it sometimes is to get high quality data from almost any sample even if it only produces barely recognisable patterns.

Image 2: Successful indexing of extremely noisy patterns using automatic band detection.

That capability did not just appear overnight. There is a combination of a lot of hard work, clever ideas, and more than 25 years of experience behind it that we sometimes just forget to talk about, or perhaps even worse, expect everybody to know already. And so it is that I occasionally get asked a question at a meeting or an exhibition where I think, really? For example, some years ago I got a very good question about the EBSD calibration.

Image 3: EBSD calibration is based on the point in the pattern that is not distorted by the projection. This is the point where the electrons reach the screen perpendicularly (pattern center).

As you probably suspect EBSD calibration is not some kind of magic that ensures that you can index your patterns. It is a precise geometrical correction that distorts the displayed EBSD solution so that it fits the detected pattern. I always compare it with a video-projector. That is also a point projection onto a screen at a small angle, just like the EBSD detection geometry. And when you do that there is a distortion where the sides of the image on the screen are not parallel anymore but move away from each other. On video projectors there is a smart trick to fix that: a button labelled keystone correction which pulls the sides of the image nicely parallel again where they belong.

Image 4: Trapezoid distortion before (left) and after (right) correction.

Unfortunately, we cannot tell the electrons in the SEM to move over a little bit in order to make the EBSD pattern look correct. Instead we need to distort the indexing solution just so that it matches the EBSD pattern. And now the question I got asked was, do you actually adjust this calibration when moving the beam position on the sample during a scan? Because otherwise you cannot collect large EBSD maps. Apparently not everybody was doing that at that time, and it was being presented at a conference as the invention of the century that no EBSD system could do without. It was finally possible to collect EBSD data at low magnification! So, when do you think this feature will be available in your software? I stood quiet for a moment before answering, well, eh, we actually already have such a feature that we call the pattern centre shift. And it had been in the system since the first mapping experiments in the early 90’s. We just did not talk about it as it seemed so obvious.

There are more things like that hidden in the software that are at least as important, such as smart routines to detect the bands even in extremely noisy patterns, EBSD pattern background processing, 64-bit multithreading for fast processing of large datasets, and efficient quaternion-based mathematical methods for post-processing. These tools are quietly working in the background to deliver the results that the user needs.
There are some other original ideas that date back to the 1990’s that we actually do regularly talk about, such as the hexagonal scanning grid, triplet voting indexing, and the confidence index, but there is also some confusion about these. Why do we do it that way?

The common way in imaging and imaging sensors (e.g. CCD or CMOS chips) is to organise pixels on a square grid. That is easy and you can treat your data as being written in a regular table with fixed intervals. However, pixel-to-pixel distances are different horizontally and diagonally which is a drawback when you are routinely calculating average values around points. In a hexagonal grid the point-to-point distance is constant between all neighbouring pixels. Perhaps even more importantly, a hexagonal grid offers ~15% more points on the same area than a square grid, which makes it ideally suited to fill a surface.

Image 5: Scanning results for square (left) and hexagonal (right) grids using the same step size. The grain shape and small grains with few points are more clearly defined in the hexagonal scan.

This potentially allows improvements in imaging resolution and sometimes I feel a little surprised that a hexagonal imaging mode is not yet available on SEMs.
The triplet voting indexing method also has some hidden benefits. What we do there is that a crystal orientation is calculated for each group of three bands that is detected in an EBSD pattern. For example, when you set the software to find 8 bands, you can define up to 56 different band triangles, each with a unique orientation solution.

Image 6: Indexing example based on a single set of three bands – triplet.

Image 7: Equation indicating the maximum number of triplets for a given number of bands.

This means that when a pattern is indexed, we don’t just find a single orientation, we find 56 very similar orientations that can all be averaged to produce the final indexing solution. This averaging effectively removes small errors in the band detection and allows excellent orientation precision, even in very noisy EBSD patterns. The large number of individual solutions for each pattern has another advantage. It does not hurt too much if some of the bands are wrongly detected from pattern noise or when a pattern is collected directly at a grain boundary and contains bands from two different grains. In most cases the bands coming from one of the grains will dominate the solutions and produce a valid orientation measurement.

The next original parameter from the 1990’s is the confidence index which follows out of the triplet voting indexing method. Why is this parameter such a big deal that it is even patented?
When an EBSD pattern is indexed several parameters are recorded in the EBSD scan file, the orientation, the image quality (which is a measure for the contrast of the bands), and a fit angle. This angle indicates the angular difference between the bands that have been detected by the software and the calculated orientation solution. The fit angle can be seen as an error bar for the indexing solution. If the angle is small, the calculated orientation fits very closely with the detected bands and the solution can be considered to be good. However, there is a caveat. What now if there are different orientation solutions that would produce virtually identical patterns? This may happen for a single phase where it is called pseudosymmetry. The patterns are then so similar that the system cannot detect the difference. Alternatively, you can also have multiple phases in your sample that produce very similar patterns. In such cases we would typically use EDS information and ChI-scan to discriminate the phases.

Image 8: Definition of the confidence index parameter. V1 = number of votes for best solution, V2 = mumber of votes for 2nd best solution, VMAX= Maximum possible number of votes.

Image 9: EBSD pattern of silver indexed with the silver structure (left) and copper structure (right). Fit is 0.24″, the only difference is a minor variation in the band width matching.

In both these examples the fit value would be excellent for the selected solution. And in both cases the solution has a high probability of being wrong. And that is where the confidence index or CI value becomes important. The CI value is based on the number of band triangles or triplets that match each possible solution. If there are two indistinguishable solutions, these will both have the same number of triangles and the CI will be 0. This means that there are two or more apparently valid solutions that may all have a good fit angle. The system just does not know which of these solutions is the correct one and thus the measurement is rejected. If there is a difference of only 10% in matched triangles between alternative orientation solutions in most cases the software is capable of identifying the correct solution. The fit angle on its own cannot identify this problem.

After 25 years these tools and parameters are still indispensable and at the basis of every EBSD dataset that is collected with an EDAX system. You don’t have to talk about them. They are there for you.

“Strained” Friendship

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

Don’t just read the title of this post and skip to the photos or you might think it is some soap opera drama about strained relations – instead, the title is, once again, my feeble attempt at a punny joke!

I was recently doing a little reference checking and ended up on the website for Microscopy and Microanalysis (the journal, not the conference). On my first glance, I was surprised to see my name in the bottom right corner. Looking closer, I noticed that the paper Matt Nowell, David Field and I wrote way back in 2011 entitled “A Review of Strain Analysis Using Electron Backscatter Diffraction” is apparently the most cited article in Microscopy and Microanalysis. I am pleased that so many readers have found it useful. I remember, at the time, that we were getting a lot of questions about the tools within OIM Analysis™ for characterizing local misorientation and how they relate to strain. It was also a time when HREBSD was really starting to gain some momentum and we were getting a lot of questions on that front as well. So, we thought it would be helpful to write a paper that hopefully would answer some practical questions on using EBSD to characterize strain. From all the citations, it looks as though we actually managed to achieve what we had strived for.

My co-authors on that paper have been great to work with professionally; but I also count them among my closest personal friends. David Field joined Professor Brent Adams’ research group at BYU way back in 1987 if my memory is correct. We both completed master’s degrees at BYU and then followed Brent to Yale in 1988 to do our PhDs together. David then went on to Alcoa and I went to Los Alamos National Lab. Brent convinced David to leave and join the new startup company TSL and I joined about a year later. David left TSL for Washington State University shortly after EDAX purchased TSL.

Before, I joined TSL, Matt Nowell* had joined the company and he has been at TSL/EDAX ever since. Even with all the comings and goings we’ve remained colleagues and friends.

I’ve been richly blessed by both their excellent professional talents and their fun spirited friendship. We’ve worked, traveled and attended conferences together. We’ve played basketball, volleyball and golf together. I must also brag that we formed the core of the soccer team to take on the Seoul National University students after ICOTOM 13 in Seoul. Those who attended ICOTOM 13 may remember that it was held shortly after the 2002 World Cup hosted jointly by Korea and Japan; in which Korea had such a good showing – finishing 4th. A sequel was played at SNU where the students pretty much trounced the rest of the world despite our best efforts 😊. Here are a few snapshots of us with our Korean colleagues at ICOTOM 13 – clearly, we were always snappy dressers!

* Don’t miss Matt’s upcoming webinar: “Applications of High-Speed CMOS Cameras for EBSD Microstructural Analysis”. 

Common Mistakes when Presenting EBSD Data

Shawn Wallace, Applications Engineer, EDAX

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!