materials characterization

What’s in a Name?

Matt Nowell, EBSD Product Manager, EDAX

The Globe Theatre

I recently had the opportunity to attend the RMS EBSD meeting, which was held at the National Physics Lab outside of London. It was a very enjoyable meeting, with lotsof nice EBSD developments. While I was there, I was able to take in a bit of London as well. One of the places I visited was the Shakespeare’s Globe Theater. While I didn’t get a chance to see a show here (I saw School of Rock instead), it did get me thinking about one of the Bard’s more famous lines, “What’s in a name? That which we call a rose by any other word would smell as sweet” from Romeo and Juliet.

I bring this up because as EBSD Product Manager for EDAX, one of my responsibilities is to help name new products. Now my academic background is in Materials Science and Engineering, so understanding how to best name a product has been an interesting adventure.

TSL

The earliest product we had was the OIM™ system, which stood for Orientation Imaging Microscopy. The name came from a paper introducing EBSD mapping as a technique. At the time, we were TSL, which stood for TexSem Laboratories, which was short for Texture in an SEM. Obviously, we were into acronyms. We used a SIT (Silicon Intensified Target) camera to capture the EBSD patterns. We did the background processing with a DSP-2000 (Digital Signal Processor). We controlled the SEM beam with an MSC box (Microscope System Control).

Our first ‘mapped’ car.

For our next generator of products, we branched out a bit. Our first digital Charge-Coupled Device (CCD) camera was called the DigiView, as it was our first digital camera for capturing EBSD patterns instead of analog signals. Our first high-speed CCD camera was called Hikari. This one may not be as obvious, but it was named after the high-speed train in Japan, as Suzuki-san (our Japanese colleague) played a significant role in the development of this camera. Occasionally, we could find the best of both worlds. Our phase ID product was called Delphi. In Greek mythology, Delphi was the oracle who was consulted for important decisions (could you describe phase ID any better than that?). It also stood for Diffracted Electrons for Phase Identification.

Among our more recent products, PRIAS™ stands for Pattern Region of Interest Analysis System. Additionally, though, it is meant to invoke the hybrid use of the detector as both an EBSD detector and an imaging system. TEAM™ stands for Texture and Elemental Analysis System, which allowed us to bridge together EDS and EBSD analysis in the same product. NPAR™ stands for Neighbor Pattern Averaging and Reindexing, but I like this one as it sounds like I named it because of my golf game.
I believe these names have followed in the tradition of things like lasers (light amplification by stimulated emission of radiation), scuba (self-contained underwater breathing apparatus), and CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). It generates a feeling of being part of the club, knowing what these names mean.

Velocity™ EBSD Camera

The feedback I get though, is that our product names should tell us what the product does. I don’t buy into this 100%, as my Honda Pilot isn’t a self-driving car, but it is the first recommendation on how to name a product (https://aytm.com/blog/how-to-name-a-product-10-tips-for-product-naming-success/). Following this logic, our latest and world’s fastest EBSD camera is the Velocity™. It sounds fast, and it is.

Of course, even when using this strategy, there can be some confusion. Is it tEBSD (Transmission EBSD) or TKD (Transmission Kikuchi Diffraction)? Does HR-EBSD give us better spatial resolution? Hopefully as we continue to name new products, we can make our answer clear.

Picture postcards from…

Dr. Felix Reinauer, Applications Specialist, EDAX

Display of postcards from my travels.

…L. A. – this is the title of a popular song from Joshua Kadison which one may like or dislike but at least three words in this title describe a significant part of my work at EDAX. Truth be told I’ve never been to Los Angeles, but as an application specialist traveling in general is a big part of my job. I´m usually on the move all over Europe meeting customers for trainings or attending exhibitions and workshops. This part of my job gives me the opportunity to meet with lots of people from different places and have fruitful discussions at the same time. If I am lucky, there is sometimes even some time left for sightseeing. The drawback of the frequent traveling is being separated from family and friends during these times.

Nowadays it is easy to stay in touch thanks to social media. You send a quick text message or make phone calls, but these are short-term. And here we get back to the title of this post and Joshua Kadison´s pop song, because quite some time ago I started the tradition of sending picture postcards from the places I travel to. And yes, I am talking about the real ones made from cardboard, documenting the different cities and countries I get to visit. Additionally, these cards are sweet notes highly appreciated by the addressee and are often pinned to a wall in our apartment for a period of time.

Within the last couple of years, I notice that it is getting harder to find postcards, this is especially true in the United States. Sometimes keeping on with my tradition feels like an Iron Man challenge. First, I run around to find nice picture postcards, then I have to look for stamps and the last challenge is finding a mailbox. Finally, all these exercises must be done in a limited span of time because the plane is leaving, the customer is waiting, or the shops are closing. But it is still worth it.

It is not only the picture on the front side, which is interesting, each postcard holds one or more stamps – tiny pieces of artfully designed paper – as well. Postage stamps were first introduced in Great Britain in 1840. The first one showed the profile of Queen Victoria and is called “Penny Black” due to the black background and its value. Thousands of different designs have been created ever since attracting collectors all over the world. Sadly, this tradition might be fading. Nowadays the quick and easy way of printed stamps from a machine with only the value on top seems to be becoming the norm. But the small stamps are often beautiful to look at and are full of interesting information, either about historical events, famous persons or remarkable locations.

A selection of postage stamps from countries I have visited.

For me, as a chemist I was also curious about the components of the stamps. Like a famous painting, investigated by XRF to collect information about the pigments and how the artist used them. For the little pieces of art, the SEM in combination with EDS is predestinated to investigate them in low vacuum mode without damaging them. The stamps I looked at are from my trips to Sweden, Great Britain, the Netherlands and the Czech Republic. In addition, I added one German stamp as a tribute to one of the most important chemists, Justus von Liebig after whom the Justus-Liebig University in Gießen is named, where he was professor (1824 – 1852) and I did my Ph. D. (a few years later).

All the measurements shown below were done under the same conditions using an acceleration voltage of 20 kV, with a pressure of 30 Pa and 40x magnification. With the multifield map option the entire stamp area was covered, using a single field resolution of 64×48 each and 128 frames.

Czech Republic Germany

 

Netherlands Sweden

United Kingdom

The EDS results show that modern paper is a composite material. The basic cellulose fibers are covered with a layer of calcium carbonate to ensure a good absorption of the different pigments used. This can be illustrated with the help of phase mappings. Even after many kilometers of travelling and all the hands treating the postcards all features of the stamps are still intact and can be detected. The element mappings show that the colors are not only based on organic compounds, but the existence of metal ions indicate a use of inorganic pigments. Typical elements detected were Al, S, Fe, Ti, Mn and others. The majority of the analysis work I do for EDAX and with EDAX customers is very specialized and involves materials, which would not be instantly familiar to non-scientists. It was fun to be able to use the same EDS analysis techniques on recognizable, everyday objects and to come up with some interesting results.

“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”

A Lot of Excitement in the Air!

Sia Afshari, Global Marketing Manager, EDAX

After all these years I still get excited about new technologies and their resulting products, especially when I have had the good fortune to play a part in their development. As I look forward to 2019, there are new and exciting products on the horizon from EDAX, where the engineering teams have been hard at work innovating and enhancing capabilities across all product lines. We are on the verge of having one of our most productive years for product introduction with new technologies expanding our portfolio in electron microscopy and micro-XRF applications.

Our APEX™ software platform will have a new release early this year with substantial feature enhancements for EDS, to be followed by EBSD capabilities later in 2019. APEX™ will also expand its wings to uXRF providing a new GUI and advanced quant functions for bulk and multi-layer analysis.

Our OIM Analysis™ EBSD software will also see a major update with the addition of a new Dictionary Indexing option.

A new addition to our TEM line will be a 160 mm² detector in a 17.5 mm diameter module that provides an exceptional solid angle for the most demanding applications in this field.

Elite T EDS System

Velocity™, EDAX’s low noise CMOS EBSD camera, provides astonishing EBSD performance at greater than 3000 fps with high indexing on a range of materials including deformed samples.

Velocity™ EBSD Camera

Last but not least, being an old x-ray guy, I can’t help being so impressed with the amazing EBSD patterns we are collecting from a ground-breaking direct electron detection (DED) camera with such “Clarity™” and detail, promising a new frontier for EBSD applications!
It will be an exciting year at EDAX and with that, I would like to wish you all a great, prosperous year!

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!

Happy Holidays from All of Us at EDAX!

Thank you to all the followers of our blog – we hope that you have been entertained, informed and amused by our posts this year. We will be taking a break until the second week of January 2019, but if you need any extra diversion over the holidays, don’t forget to take a look at the resources we have shared with you during the year and catch up on anything you may have missed.  We wish you a happy and healthy New Year and look forward to talking to you again in 2019.

All our on-demand webinars can be found here.  You can also find us on the following platforms:




Old Eyes?

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

I was recently asked to write a “Tips & Tricks” article for the EDAX Insight Newsletter as I had recently done an EDAX Webinar (www.edax.com/news-events/webinars) on Texture Analysis. I decided to follow up on one item I had emphasized in the Webinar. Namely, the need for sampling enough orientations for statistical reliability in characterizing a texture. The important thing to remember is that it is the number of grain orientations as opposed to the number of orientations measured. But that lead to the introduction of the idea of sub-sampling a dataset to calculate textures when the datasets are very large. Unfortunately, there was not enough room to go into the kind of detail I would have liked to so I’ve decided to use our Blog forum to cover some details about sub-sampling that I found interesting

Consider the case where you not only want to characterize the texture of a material but also the grain size or some other microstructural characteristic requiring a relatively fine microstructure relative to the grain size. According to some previous work, to accurately capture the texture you will want to measure approximately 10,000 grains [1] and about 500 pixels per average grain in order to capture the grain size well [2]. This would result in a scan with approximately 5 million datapoints. Instead of calculating the texture using all 5 million data points, you can use a sub-set of the points to speed up the calculation. In our latest release of OIM Analysis, this is not as big of a concern as it once was as the texture calculations have been multithreaded so they are fast even for very large datasets. Nonetheless, since it is very likely that you will want to calculate the grain size, you can use the area weighted average grain orientation for each grain as opposed to using all 5 million individual orientation measurements for some quick texture calculation. Alternatively, a sub-set of the points through random or uniform sampling of the points in the scan area could be used.

Of course, you may wonder how well the sub-sampling works. I have done a little study on a threaded rod from a local hardware store to test these ideas. The material exhibits a (110) fiber texture as can be seen in the Normal Direction IPF map and accompanying (110) pole figure. For these measurements I have simply done a normalized squared difference point-by-point through the Orientation Distribution Function (ODF) which we call the Texture Difference Index (TDI) in the software.


This is a good method because it allows us to compare textures calculated using different methods (e.g. series expansion vs binning). In this study, I have used the general spherical harmonics series expansion with a rank of L = 22 and a Gaussian half-width of  = 0.1°. The dataset has 105,287 points with 92.5% of those having a CI > 0.2 after CI Standardization. I have elected only to use points with CI > 0.2. The results are shown in the following figure.

As the step size is relatively coarse with respect to the grain size, I have experimented with using grains requiring at least two pixels before considering a set of similarly oriented points a grain versus allowing a single pixel to be a grain. This resulted in 9981 grains and 25,437 grains respectively. In both cases, the differences in the textures between these two grain-based sub-sampling approaches with respect to using the full dataset are small with the 1 pixel grain based sub-sampling being slight closer as would be expected. However, the figure above raised two questions for me: (1) what do the TDI numbers mean and (2) why do the random and the uniform sampling grids differ so much, particularly as the number of points in the sub-sampling gets large (i.e. at 25% of the dataset).

TDI
The pole figure for the 1000 random points in the previous figure certainly captures some of the characteristics of the pole figure for the full dataset. Is this reflected in the TDI measurements? My guess is that if I were to calculate the textures at a lesser rank, something like L = 8 then the TDI’s would go down. This is already part of the TDI calculation and so it is an easy thing to examine. For comparison I have chosen to look at four different datasets: (a) all of the data in the dataset above (named “fine”), (b) a dataset from the same material with a coarser step size (“coarse”) containing approximately 150,000 data points, (c) sub-sampling of the original dataset using 1000 randomly sampled datapoints (“fine-1000”) and (d) the “coarse” dataset rotated 90 degrees about the vertical axis in the pole figures (“coarse-rotated”). It is interesting to note that the textures that are similar “by-eye” show a general increase in the TDI as the series expansion rate increases. However, for very dissimilar textures (i.e “coarse” vs “coarse-rotated”) the jump to a large TDI is immediate.

Random vs Uniform Sampling
The differences between the random and uniform sampling were a bit curious so I decided to check the random points to see how they were positioned in the x-y space of the scan. The figure below compares the uniform and random sampling for 4000 datapoints – any more than this is hard to show. Clearly the random sampling is reasonable but does show a bit of clustering and gaps within the scan area. Some of these small differences show up with higher differences in TDI values than I would expect. Clearly, at L = 22 we are picking up quite subtle differences – at least subtle with respect to my personal “by-eye” judgement. It seems to me, that my “by-eye” judgement is biased toward lower rank series expansions.


Of course, another conclusion would be that my eyesight is getting rank with age ☹ I guess that explains my increasingly frequent need to reach for my reading glasses.

References
[1] SI Wright, MM Nowell & JF Bingert (2007) “A comparison of textures measured using X-ray and electron backscatter diffraction”. Metallurgical and Materials Transactions A, 38, 1845-1855
[2] SI Wright (2010) “A Parametric Study of Electron Backscatter Diffraction based Grain Size Measurements”. Practical Metallography, 47, 16-33.