OIM

Intelligent Use of IQ

Dr. Stuart Wright, Senior Scientist, EDAX

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

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

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

Image1

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

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

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

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

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

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

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

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

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

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

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

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

Happy Thanksgiving!

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

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

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

The First OIM – Channel Die Compressed Aluminum – December 1991

The First OIM – Channel Die Compressed Aluminum – December 1991

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

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

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

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

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

Space Rocks Rock!

Matt Nowell, EBSD Product Manager, EDAX

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

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

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

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

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

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

Figure 1 - Large area EBSD map of Gibeon meteorite.

Figure 1 – Large area EBSD map of Gibeon meteorite.

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

Figure 2 - 100X EBSD map of Gibeon meteorite.

Figure 2 – 100X EBSD map of Gibeon meteorite.

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

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

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

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

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

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

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

Figure 6 - 3D EBSD data from Gibeon meteorite.

Figure 6 – 3D EBSD data from Gibeon meteorite.

 

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

Figure 8 – 3D Kamacite lamella grain.

Figure 9 – 3D interface in Gibeon meteorite.

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

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

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

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

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

Fine-Tuning the Microstructure

Dr. Stuart Wright, Senior Scientist, EDAX

Since my blog about piano wires back in November 2014, I’ve continued to think about what it all means in terms of music. As I mentioned in my posting, my friend Keith Kopp provided the wires for me to look at. Keith is very generous with his time. I’ve seen him many times help out with music at various church and neighborhood socials. I’m always impressed that someone like Keith can clearly hear when an instrument is even slightly out of tune and also recognize how to fix the problem. I certainly don’t have the ear for that kind of thing. Keith mentioned to me that he can hear the difference between the two wires he supplied to me. I realized quickly I had no hope of picking that up with my insensitive ears. I then realized that Keith was saying he could hear the difference even when the two wires are tuned. I wondered what it was that he was hearing. I turned to Wikipedia for some insight and stumbled across an entry on “inharmonicity” which is “the degree to which the frequencies of overtones (also known as partials or partial tones) depart from whole multiples of the fundamental frequency”.  I realized that the sound waves are travelling through the wires at slightly different rates due to elastic anisotropy coupled with grain-to-grain differences in orientation. Thus, while the average pitch of the wire will be in tune there will actually be a spread about that pitch. I might be able to estimate that spread using the principles of elastic anisotropy.

For a single crystal the elastic behavior is anisotropic as is illustrated in a plot for the elastic modulus for an iron single crystal below (courtesy of Megan Frary at Boise State University).

Figure 1

The elastic properties of a single crystal can be expressed in terms of a tensor. This is handy, because rotating the property tensor to reflect the grain orientation with respect to a set of samples axes is fairly straight forward (C is a fourth order elasticity tensor and g is the orientation matrix):

equation

The next step was to simply go to a reference volume and find the single crystal elastic constants for fcc and bcc iron, plug them into OIM Analysis and then “Bob’s your uncle”. However, I learned it wasn’t nearly as straightforward as I thought. Once again, some searching on the internet led me to some papers on first principle calculations of elastic constants and I quickly discovered that estimating elastic constants at room temperature is not as simple as I would have thought. I found several papers by Levente Vitos and co-workers. Professor Vitos was kind enough to teach me a little about this field and after some correspondence with him I decided to use the elastic constants for Fe-Mn found in Zhang, H., Punkkinen, M. P., Johansson, B., & Vitos, L. (2012). Elastic parameters of paramagnetic iron-based alloys from first-principles calculations. Physical Review B, 85:  054107-1. My thinking was that while the absolute values of the constants were probably not accurate, the ratios between the components would be constant enough to at least give me a rough idea of the distribution. (I tried some of the other constants in this paper and the results were all pretty similar.)

I then calculated the distribution of elastic moduli parallel to the longitudinal direction of the wire thinking this might give me an idea of the differences in the distribution of pitches one might hear when a piano wire is struck. The results are shown below – on the left for actual elastic moduli and then on the right for how this might translate to a distribution of pitches. However, this second plot is only a schematic to illustrate my thinking – I have no idea how the elastic moduli variations would translate to pitch variations, the horizontal scale could be much wider, i.e. the flats and sharps could be much farther away from the center pitch and it may also not be a linear relationship. Also the choice of C is completely arbitrary – the actual pitch will depend on the diameter and tension on the wire.

Figure 2
The bad wire (in terms of breakage), which according to Keith’s ear has a clearer sound than the wire less prone to breaking, has a narrower distribution of elastic moduli. Of course, I may be completely off-base as a “fuller” sound may correspond to the broader distribution as well. Perhaps what Keith can hear is the fine balance between clarity and fullness. So if my large set of assumptions is correct then, while I may not be able to hear the difference, I can at least see the difference in the texture data.

 

When a picture is worth only a single word….

Matt Nowell – Product Manager EBSD, EDAX

I’ve been at EDAX, and formerly TSL, for 20 years now, and given that OIM makes such beautiful images, one of the more ironic facts about my career is that I am color blind.  That can sometimes make interpreting colored microstructural images a bit more challenging, and I’m very grateful for the flexibility in coloring within OIM Analysis that the software guys have put in for me (although I think they keep the default first 2 colors in phase maps red and green just because I won the last golf Burrito Open).

Occasionally, however, it’s very easy to read the microstructure.  Take this image for example:

Inverse Pole Figure showing crystallographic orientation.

This image is an Inverse Pole Figure (IPF) map showing the crystallographic orientation.  While I’m sure if one were properly motivated, you might find the right vector in sample space to turn this IPF map into a test for colorblindness, even I can see that it spells out DOE.  This very cool example was created by researchers at Oak Ridge National Laboratory, where they used an additive manufacturing process called Electron Beam Melting (EBM) to spatially modify the solidification texture development in a nickel-based superalloy.  One can easily imagine that if you can control the local microstructure, you can then design and engineer the microstructure to optimize properties spatially for specific loads and applications.  You can learn more about the work at Oak Ridge at: http://3dprint.com/19477/ebm-printing-3d-ornl/ or http://web.ornl.gov/sci/manufacturing/research/additive/.

Other approaches have also been used to write into the microstructure, which I guess is the equivalent to changing the font and font size.

Dimes

In this example from the Else Kooi Laboratory, formerly known as the Dimes Technology Center, at the Delft University of Technology (http://www.dimes.tudelft.nl/EKL/Home.php) a laser beam was used to locally induce recrystallization in polycrystalline silicon.  This approach has been used to develop thin film transistors used in things like liquid crystal displays.  The writing is visible in both the OIM image quality (IQ) map(top) and the grain map (bottom), where adjacent measurement pixels of similar orientations are grouped together as grains, and then these resolved grains are randomly colored to show size and morphology.  That approach gives each letter a different color.

OIM has even been used to read the deformation in metals to recover destroyed serial numbers in metal objects like firearms.  In the images below, an “X” has been stamped into a piece of stainless steel (a), and then polished to visually remove the marker (b).

Figure2

Researchers at NIST have then used OIM to map over the area, with the corresponding IQ map shown here:

ImageJ=1.47v unit=um

The residual plastic deformation present in the microstructure causes a lower EBSD IQ value which is used to image the stamped X.  Years ago EDAX was featured on the TV show CSI for our Orbis µXRF product.  With this forensic application, we are finally ready for a sequel.  More information about this application can be found in a paper by Ryan White and Bob Keller in Forensic Science International (R.M. White and R.R Keller, Restoration of firearm serial numbers with electron backscatter diffraction (EBSD) Forensic Science International 249 (2014) pp 266-270) and at http://www.nist.gov/mml/acmd/ebsd-021115.cfm.

While all of these examples have used OIM to visualize the text within the microstructure, my first introduction to this literary metallurgical engineering was observable by eye:

RexGG

This sample was created for the International Conference on Grain Growth (ICGG), held back in 1995.  In keeping with theme of this conference, the characters were placed by locally inhibiting the grain growth while the bulk material was recrystallized.

So, while these pictures many not be worth a thousand words, they do contain at least a thousand grains.  The fact that a few words have been engineered into the microstructure by various means is pretty incredible.

Many thanks to Ryan Dehoff at Oak Ridge National Lab, Ryan White and Bob Keller at NIST, and David Field at Washington State University for allowing the use of their images for this blog.

“You guys are basically all the same”

Dr. Stuart Wright, Senior Scientist

Please click here to read Dr Wright’s blog in Chinese.

At MS&T 2014 in Pittsburgh, I spoke to an EBSD user of one of our competitors’ systems. He commented in regards to the data collection offerings from the different vendors that “you guys are basically all the same”. In the context of our discussion, his statement was not meant negatively, as he was arguing against some of the claims surrounding rectangular versus circular phosphors. Nonetheless, his statement became a proverbial “burr under my saddle”.

Figure 1: Raw and background corrected pattern

Figure 1: Raw and background corrected pattern

As luck would have it, shortly after that conversation I received some EBSD data from one of our customers who also has one of our competitors’ EBSD systems. The customer had collected a dataset on the competitor’s system with purposefully poor imaging conditions – high gain and low exposure time as would be used for maximizing data collection speed. This was done to create a dataset and accompanying patterns for testing a newly developed indexing method1. As expected, the noisy patterns lead to low quality indexing results despite the material being typically straightforward for EBSD – recrystallized nickel. The customer actively sought out a representative of the competitor in order to verify that the optimal software settings were used when indexing the noisy patterns. In view of my previous conversation I was eager to take advantage of this opportunity to compare the results of our indexing algorithms on these noisy patterns against those obtained by our competitor to see if we as vendors are really “all the same” or not.

The patterns were recorded at 80×60 pixels. I cropped out a 60×60 circular pattern and applied background correction to each using a background formed by averaging all of the patterns in the dataset together. No additional image processing was performed. Standard operating parameters for the Hough transform and the indexing algorithm were used (10 bands were specified).

IPF maps as obtained from the competitor’s indexing results and the results from OIM DC are shown in Figures 2a and 2b. The competitor was only able to index 30.4% of the patterns. The indexing success rate from my OIM based rescan using the patterns is 90.3% – nearly three times better.  Obviously, the different EBSD systems are not “basically all the same”.

Figure 2: IPF Maps constructed from data from (a) the competitor, (b) OIM DC (CI > 0.1) and (c) the dictionary method.

Figure 2: IPF Maps constructed from data from (a) the competitor, (b) OIM DC (CI > 0.1) and (c) the dictionary method.

When I presented these results, the improvement was so dramatic that it was assumed I had performed a clean-up process to achieve such good results. The indexing rate was calculated by first performing CI standardization2 followed by excluding points with CI values over 0.1. I emphasize that the CI standardization process does not perform any modifications to the orientation data – it only upgrades the CI values. In order to verify the validity of our indexing success rate metric, I compared my indexing results on a point-by-point basis against results obtained using a dictionary method1 pioneered through a collaborative effort between groups at the University of Michigan (A. Hero), Carnegie Mellon (M. De Graef), AFRL (J. Simmons) and BlueQuartz Software (M. Jackson). The IPF map in Figure 2c shows the extremely high fidelity of this approach even with these noisy patterns. While the dictionary method does an excellent job it should be noted that it is very computationally intensive relative to standard indexing methods. The good news from my point of view is that 89.8% of the orientations obtained by OIM DC match those obtained by the dictionary method whereas the competitor’s data matches only 30.3%. This confirms both the proficiency of the OIM DC indexing routines as well as the validity of our indexing success rate metric.

Image Quality Map

Figure 3: Image Quality Map


As a sideline, it is clear that there is a horizontal band near the bottom of the scan and a vertical band near the right edge where the points are difficult to index – particularly in the overlapping region of the two bands. This is due to poorer quality patterns in these regions as is evident in the following IQ map. The scan was part of a montage of overlapping scans. The overlapping regions have additional hydrocarbon contamination leading to poorer quality patterns.

While I was pleased with our results relative to our competitor’s, it should be noted that this single dataset does not represent a test of the full EBSD system performance. However it does provide insight into the relative capability of EDAX’s indexing routines. I was happy to verify that our triplet indexing method and our implementation of the Hough transform are both clearly very robust.

A special thanks to Michael Jackson of BlueQuartz Software (www.bluequartz.net) for providing the dictionary method results.
1. Y.H. Chen, Park S.U., D. Wei, G. Newstadt, M. Jackson, J.P. Simmons, M. De Graef, and A.O. Hero. (2015) “A dictionary approach to the EBSD indexing problem”. Microscopy and Microanalysis, under review.
2.  Nowell, M. M. and S. I. Wright (2005). “Orientation effects on indexing of electron backscatter diffraction patterns.” Ultramicroscopy 103: 41-58.

Tuning the Microstructure

Dr. Stuart Wright – Senior Scientist EBSD

My friend, Keith Kopp is a great clarinet player and piano tuner.  He is responsible for more than one hundred pianos in the School of Music at Brigham Young University. We got talking about what I do for a living one day and he started wondering if I could answer a question he had about piano wires. It turns out that Keith is using wires from two different manufacturers and found that one wire was breaking more frequently than the other. It made me curious as well so I offered to take a look at the microstructure of the two wires.

The first thing I did was get on Wikipedia to try and learn a little bit about piano wires. It turns out that piano wire is made from tempered high-carbon steel. Remarkably, the oldest recorded use of wire for musical instruments is 1351 in Augsburg. Today’s wires are high tensile polished wires made by cold drawing.

Ron Witt of EBSD analytical was kind enough to prepare cross-sections of the wire for EBSD and ran a couple of OIM scans from two different wires that Keith gave me: 1) a “good” wire and (2) a “bad” wire – the good being the wire that is less prone to breakage. The first thing we noticed was the small grain size of the wires. The first few scans drifted quite a bit most likely from hydrocarbon contamination due to the small step size used to characterize the fine microstructures. We elected to run the OIM scans with 30nm step sizes. With a little effort, Ron was able to get me a couple of good scans with only minimal drift.

The other thing we discovered is that the two wires contained both a face-centered cubic (FCC) phase and a body-centered cubic phase (BCC) which we presumed were austenite and ferrite. It is possible that the bcc phase was actually martensite which is body-centered tetragonal (BCT) but difficult to distinguish from the BCC phase as it generally exhibits only a few percent of tetragonality. Phase maps of the two wires are shown below with the bad wire on the left and the good wire on the right.
Stu November 1

The phase fraction for the good wire is BCC/FCC = 21.4/78.6 and 13.4/86.6 for the bad wire. While these scans represent relatively small snap shots of the full microstructure of the wire, the results were consistent across the other scans we collected not shown here.

Because of the difficulties in obtaining good data from the materials with such small grain sizes, I have elected to filter out the suspect data. This was done by first running a grain CI standardization clean-up on both sets of data. The grain parameters used were 5° for the grain tolerance angle, 2 pixels for the minimum grain size but with the requirement that the grain must extend across at least two rows of the OIM scan. Then I filtered out data with CIs less than 0.1 and grains with equivalent grain diameters less than 50nm. In the case of the good wire the filtered data contained only 11% of the original scan data and 27% in the bad wire case. The larger fraction of high quality data from the bad wire appears to be attributable to a larger grain size.

The textures between the two wires also differ. The following IPF maps (showing the crystal direction aligned with the longitudinal axis of the wire) show some interesting features. First, the BCC phase tends to be dominated by [110] crystal axes parallel to the longitudinal axis (colored green) whereas the FCC phase tends to have [001] axes parallel to the longitudinal axis of the wire (red) with some [111] (blue).

Stu November 2

For Keith’s benefit (and piano technicians everywhere) the phrasing above is not as complicated as it sounds. A metal wire is made up of many small crystallites. These constituent crystallites all have an orientation associated with them with respect to the principle axes of the wire. The distribution of these crystallite orientations we term crystallographic texture. The following schematic shows how the crystallites (in general) are oriented within the wire. In this schematic, the crystals shown can actually be rotated about the horizontal axis in the figure to any other orientation. When the crystallites have one axis aligned with a particular axis of the sample it is called a fiber texture. The distribution of all these oriented crystals will affect the properties of the wire such as the tendency of one wire to break more quickly than another and likely the vibrational response when the wire is struck. The texture arises from the thermo-mechanical processing used to form the wire.

Stu November 5

Another noteworthy feature in these maps is that there seem to be clusters of similarly oriented material in the FCC case. I checked to see if this remains true when considering an IPF map from another direction – in this case parallel to the vertical direction in the maps. It does – the clustering is consistent in both maps. This suggests to me that these are subgrains arising from fragmentation of larger grains during the wire forming process.

Stu November 3

The following pole figures show the textures of the drawn wire. One interesting thing to note is that the textures are not fully axisymmetric. This may be due to the lack of sampling statistics but may also provide some indication of asymmetry in the wire drawing process or possibly a remnant of the pre-drawing texture in the material. This is more pronounced in the case of the bad wire – particularly in the FCC phase. However, conclusions from such subtleties in textures obtained from so few orientations should be considered with a good deal of skepticism.

Stu November 4

It should be noted that the measured textures did not have the [110] crystal directions in the BCC phases exactly aligned with the sample normals. This required a rotation of the data by as much as 6°. However, this is not unexpected. It is unlikely that the wires were mounted with the longitudinal axes of the wires perfectly vertical in the sample mounts. In fact, being off by only 6° shows why I asked Ron to help me with the sample prep.

Comparing the IPF maps with the textures, it is surprising that the intensity of the (110) peak in the BCC phase is not as high as the (001) peak in the FCC phase. However, crystal direction maps show the following volume fractions (within 10° of the given [uvw] parallel to the wire longitudinal axis (WLA)) which are more in line with the impressions given from the IPF maps that the bad wire has a strong [110] texture with about double the strength of the [110] texture in the good wire and the BCC [110] fiber textures are considerably stronger than the [001] and [111] fiber textures in the FCC phase.

Table_November
While there are some notable differences in the microstructures of the materials, I can’t really offer Keith much help in keeping his wires from breaking. Nevertheless, it was interesting to compare two samples from a materials application that I had not previously thought about. Sadly, I would also point out that knowing more about piano wires has not expanded my repertoire beyond chopsticks!