applications

Building an EBSD Sample

Matt Nowell, EBSD Product Manager, EDAX

Father’s Day is this weekend, and I like to think my kids enjoy having a material scientist for a father. They have a go-to resource for math questions, science projects are full of fun and significant digits, and when they visit the office they get to look at bugs and Velcro with the SEM. I’m always up to take them to museums to see crystals and airplanes and other interesting things as we travel around. That’s one way we have tried to make learning interactive and engaging. Another activity we have recently tried is 3D printing. This has allowed us to find or create 3D digital models of things and then print them out at home. Here are some fun examples of our creations.
At home we are printing with plastics, but in the Material Science world there is a lot of interest and development in printing with metals as well. This 3D printing, or additive manufacturing, is rapidly developing as a new manufacturing approach for both prototyping and production in a range of industries including aerospace and medical implants. Instead of melting plastics with a heated nozzle, metal powders are melted together with lasers or electron beams to create these 3D shapes that cannot be easily fabricated by traditional approaches.

In these applications, it is important to have reliable and consistent properties and performance. To achieve this, the microstructure of the metals must be both characterized and understood. EBSD is an excellent tool for this requirement.

The microstructures that develop during 3D printing are very interesting. Here is an example from a Ni-based superalloy created using Selective Laser Melting (SLM). This image shows a combined Image Quality and Orientation (IQ + IPF) Map, with the orientations displayed relative to the sample normal direction. Rather than equiaxed grains with easily identifiable twin boundaries, as are common with many nickel superalloys, this image shows grains that are growing vertically in the structure. This helps indicate the direction of heat flow during the manufacturing process. Understanding the local conditions during melting and solidification helps determine the final grain structure.
In some materials, this heating and cooling will cause not only melting, but also phase transformations that also affect the microstructure. Ti-6Al-4V (or Ti64) is one of the most common Titanium alloys used in both aerospace and biomedical applications, and there has been a lot of work done developing additive manufacturing methods for this alloy. Here is an IQ + IPF map from a Ti64 alloy built for a medical implant device.
At high temperatures, this alloy transforms into a Body-Centered Cubic (or BCC) structure called the Beta phase. As the metal cools, it transforms into a Hexagonal Closed Pack (HCP) structure, called the Alpha phase. This HCP microstructure develops as packets of similarly oriented laths as seen above. However, not all the Beta phase transforms. Here is an IQ + Phase EBSD map, where the Alpha phase is red and the Beta phase is blue. Small grains of the Beta phase are retained from the higher temperature structure.
If we show the orientations of the Beta grains only, we see how the packets relate to the original Beta grains that were present at high temperatures.
The rate of cooling will also influence the final microstructure. In this example, pieces of Ti64 were heated and held above the Beta transition temperature. One sample was then cooled in air, and another was quenched in water. The resulting microstructures are shown below. The first is the air-cooled sample.
The second is the water-cooled sample.

Clearly there is a significant difference in the resulting structure based on the cooling rate alone. As I imagine the complex shapes built with additive manufacturing, understanding both the local heating and cooling conditions will be important for optimization of both the structure and the properties.

A Little Background on Backgrounds

Dr. Stuart Wright, Senior Scientist EBSD, EDAX

If you have attended an EDAX EBSD training course, you have seen the following slide in the Pattern Indexing lecture. This slide attempts to explain how to collect a background pattern before performing an OIM scan. The slide recommends that the background come from an area containing at least 25 grains.

Those of you who have performed re-indexing of a scan with saved patterns in OIM Analysis 8.1 may have noticed that there is a background pattern for the scan data (as well as one of the partitions). This can be useful if re-indexing a scan where the raw patterns were saved as opposed to background corrected patterns. This background pattern is formed by averaging 500 patterns randomly selected from the saved patterns. 500 is a lot more than the minimum of 25 recommended in the slide from the training lecture.

Recently, I was thinking about these two numbers – is 25 really enough, is 500 overkill? With some of the new tools (Callahan, P.G. and De Graef, M., 2013. Dynamical electron backscatter diffraction patterns. Part I: Pattern simulations. Microscopy and Microanalysis, 19(5), pp.1255-1265.) available for simulating EBSD patterns I realized this might be provide a controlled way to perhaps refine the number of orientations that need to be sampled for a good background. To this end, I created a set of simulated patterns for nickel randomly sampled from orientation space. The set contained 6,656 patterns. If you average all these patterns together you get the pattern at left in the following row of three patterns. The average patterns for 500 and 25 random patterns are also shown. The average pattern for 25 random orientations is not as smooth as I would have assumed but the one with 500 looks quite good.

I decided to take it a bit further and using the average pattern for all 6,656 patterns as a reference I compared the difference (simple intensity differences) between average patterns from n orientations vs. the reference. This gave me the following curve:
From this curve, my intuitive estimate that 25 grains is enough for a good background appears be a bit optimistic., but 500 looks good. There are a few caveats to this, the examples I am showing here are at 480 x 480 pixels which is much more than would be used for typical EBSD scans. In addition, the simulated patterns I used are sharper and have better signal-to-noise ratios than we are able to achieve in experimental patterns at typical exposure times. These effects are likely to lead to more smoothing.

I recently saw Shawn Bradley who is one of the tallest players to have played in the NBA, he is 7’6” (229cm) tall. I recognized him because he was surrounded by a crowd of kids – you can imagine that he really stood out! This reminded me that these results assume a uniform grain size. If you have 499 tiny grains encircling one giant grain, then the background from these 500 grains will not work as a background as it would be dominated by the Shawn Bradley grain!

Seeing is Believing?

Dr. René de Kloe, Applications Specialist, EDAX

A few weeks ago, I participated in a joint SEM – in-situ analysis workshop in Fuveau, France with Tescan electron microscopes and Newtec (supplier of the heating-tensile stage). One of the activities during this workshop was to perform a live in-situ tensile experiment with simultaneous EBSD data collection to illustrate the capabilities of all the systems involved. In-situ measurements are a great way to track material changes during the course of an experiment, but of course in order to be able to show what happens during such an example deformation experiment you need a suitable sample. For the workshop we decided to use a “simple” 304L austenitic stainless-steel material (figure 1) that would nicely show the effects of the stretching.

Figure 1. Laser cut 304L stainless steel tensile test specimen provided by Newtec.

I received several samples a few weeks before the meeting in order to verify the surface quality for the EBSD measurements. And that is where the trouble started …

I was hoping to get a recrystallized microstructure with large grains and clear twin lamellae such that any deformation structures that would develop would be clearly visible. What I got was a sample that appeared heavily deformed even after careful polishing (figure 2).

Figure 2. BSE image after initial mechanical polishing.

This was worrying as the existing deformation structures could obscure the results from the in-situ stretching. Also, I was not entirely sure that this structure was really showing the true microstructure of the austenitic sample as it showed a clear vertical alignment that extended over grain boundaries.
And this is where I contacted long-time EDAX EBSD user Katja Angenendt at the MPIE in Düsseldorf for advice. Katja works in the Department of Microstructure Physics and Alloy Design and has extensive experience in preparing many different metals and alloys for EBSD analysis. From the images that I sent, Katja agreed that the visible structure was most likely introduced by the grinding and polishing that I did and she made some suggestions to remove this damaged layer. Armed with that knowledge and new hope I started fresh and polished the samples once more. And I had some success! Now there were grains visible without internal deformation and some nice clean twin lamellae (figure 3). But not everywhere. I still had lots of areas with a deformed structure and whatever I tried I could not get rid of those.

Figure 3. BSE image after optimized mechanical polishing.

Back to Katja. When I discussed my remaining polishing problems she helpfully proposed to give it a try herself using a combination of mechanical polishing and chemical etching. But even after several polishing attempts starting from scratch and deliberately introducing scratches to verify that enough material was removed we could not completely get rid of the deformed areas. Now we slowly started to accept that this deformation was perhaps a true part of the microstructure. But how could that be if this is supposed to be a recrystallised austenitic 304L stainless steel?

Table 1. 304/304L stainless steel composition.

Let’s take a look at the composition. In table 1 a typical composition of 304 stainless steel is given. The spectrum below (figure 4) shows the composition of my samples.

Figure 4. EDS spectrum with quantification results collected with an Octane Elite Plus detector.

All elements are in the expected range except for Ni which is a bit low and that could bring the composition right at the edge of the austenite stability field. So perhaps the deformed areas are not austenite, but ferrite or martensite? This is quickly verified with an EBSD map and indeed the phase map below confirms the presence of a bcc phase (figure 5).

Figure 5. EBSD map results of the sample before the tensile test, IQ, IPF, and phase maps.

Having this composition right at the edge of the austenite stability field actually added some interesting additional information to the tensile tests during the workshop. Because if the internal deformation in the austenite grains got high enough, we might just trigger a phase transformation to ferrite (or martensite) with ongoing deformation.

Figure 6. Phase maps (upper row) and Grain Reference Orientation Deviation (GROD) maps (lower row) for a sequence of maps collected during the tensile test.

And that is exactly what we have observed (figure 6). At the start of the experiments the ferrite fraction in the analysis field is 7.8% and with increasing deformation the ferrite fraction goes up to 11.9% at 14% strain.

So, after a tough start the 304L stainless steel samples made the measurements collected during the workshop even more interesting by adding a phase transformation to the deformation. If you are regularly working with these alloys this is probably not unexpected behavior. But if you are working with many different materials you have to be aware that different types of specimen treatment, either during preparation or during experimentation, may have a large influence on your characterization results. Always be careful that you do not only see what you believe, but ensure that you can believe what you see.

Finally I want to thank the people of Tescan and Newtec for their assistance in the data collection during the workshop in Fuveau and especially a big thank you to Katja Angenendt at the Max Planck Institute for Iron Research in Düsseldorf for helpful discussions and help in preparing the sample.

It came from outer space!

Dr. Jens Rafaelsen, Applications Engineer, EDAX

One of the interesting aspects of being in applications is the wide variety of interesting samples that you come across and this one came up when I was looking for a sample for an upcoming webinar, where I needed some ‘pretty’ maps. Our US EBSD applications engineer Shawn Wallace was previously at The Department of Earth and Planetary Sciences at the American Museum of Natural History in New York and consequently he knows quite a bit about space rocks. He handed me a thin section of a meteorite labeled NWA 10296 (more information at https://www.lpi.usra.edu/meteor/metbull.php?code=62421) and it did not disappoint.

There were a lot of interesting features in the sample, but I ended up concentrating on one of the large chondrules shown below.

Figure 1. BSE image

The primary composition of the sample is olivine (magnesium iron silicate) and the maps below show a high concentration of the Mg internal to the chondrule with an outer perimeter low in Mg and Si. The iron within the chondrule is forming particulates with low content of O and some veins of Al is also seen while the outer perimeter is an iron oxide.

Figure 2. Mg, Si and O maps (left to right).

My astronomy classes are long behind me and I can’t claim to be able to extract deep insight as to the formation and origin of this meteor but regardless, there’s something fascinating about looking at some of the early matter of the universe. As I heard Emma Bullock phrase it at the Lehigh Microscopy School, “It might just be an old rock, but it’s an old rock from outer space!”.

Figure 3. Fe (left) and Al (right) maps.

The upcoming webinar is less about space rocks and more about mapping and data representation so if this has your interest, please join us April 11 2018. Click here to register . Alternatively you can always find past webinars on our homepage https://www.edax.com/news-events/webinars

Orbis XRF Analysis of Ceramic Monoliths

Dr. Bruce Scruggs, Product Manager XRF, EDAX

Over the last several months, I’ve had a couple of opportunities to analyze a ceramic monolith. For me, this was interesting because I’ve never analyzed one of these and they have been around for a long time. Ceramic monoliths have been used for decades to support metal catalysts, providing a large surface area for reactants to interact with the catalyst. One of the most common uses is found in the automotive catalytic converter. The car’s engine exhaust passes through the catalytic converter changing environmentally polluting gases (e.g. NOx, CO and residual hydrocarbons) into more innocuous ones. (Well, they used to be more innocuous anyway until some clever person decided that CO2 emissions were problematic as well. But, I digress.) Some quick literature reading suggests there is a renewed interest in these for other areas of application besides automotive emission control.

Ceramic monolith with hexagonal channels.

Ceramic monoliths can be made from a variety of ceramics or minerals depending on the application. While it’s true in some cases that the ceramic material is inactive, there are reactions where the ceramic substrate influences the catalytic reaction. Hence, material selection is important. Application of the catalytic metals onto the monolith is another critical step which influences the overall performance of the catalyst. In one typical application process, the untreated monolith is dipped into a liquid slurry of catalytic precursors and then calcined to activate the catalyst.

Ceramic monolith with square channels assembled in an external housing.

The initial goal for Orbis micro-XRF analysis was to analyze the metal distribution within the channels of the monolith. The monoliths were cross-sectioned to expose the interior of a plane of channels and the starting question was to look at the distribution of applied metals along the length of the channels. This is easy enough to do and we can clearly see distributions as we measure from the channel entrance to the center of the channel. It’s what you would expect when dipping a narrow tube in a slurry. But, we could also see distributions across the width of the channel as well. It’s not something I immediately thought about, but it makes sense as the slurry pools in the corner of the channels where two channel walls meet. As we discussed the results we had so far, the question of quantification came up. (Questions about quantification always come up!) As we discussed quantification methodologies, I was measuring at different points within a single channel and noticed that light element signals from the substrate (e.g. MgK or AlK) were sometimes present in the spectrum and sometimes not. This was a surprising result as the belief was that the catalytic wash coat was thick enough to completely absorb these signals. So, we also learned that mass coverage of the catalyst treatment was not as heavy as expected and this also provided some valuable insight into how to go about quantifying the catalytic distributions within the monolith.

If the Orbis micro-XRF analysis can provide data on how well the catalyst is distributed throughout the monolith channel, then this could potentially enable improvements in application techniques, which in turn may lead to dramatic improvements in catalyst efficiency. Overall, I thought that wasn’t bad for a couple of hours of instrument time!

EBSD in China

Sophie Yan, Applications Engineer, EDAX

EBSD in China is a big topic and it may sound as though I am not qualified to judge or to summarize the current situation. However, as I have worked with EBSD applications for several years, I have personal experience to share. More than ten years ago, I didn’t know about EBSD when I was studying the microstructure of materials. I was in Shanghai at that time and the environment was kind of open. It is probably that at that time in China: very few people knew about EBSD. Today the situation has changed enormously after just after 10+ years. Most researchers now try to put EBSD on their microscope. Microscopes including EDS and EBSD capability are standard in Chinese universities.

As an Applications Engineer, I visit research organizations, companies, and factories. I meet customers from many different backgrounds. Some of them are experts but more are new to microanalysis, especially students from science and engineering universities. They may each have a different focus, but they all have high expectations of EBSD. The professors care about the functions which can solve their issues. If there is currently no such function, then they often ask if we can add it. Entry level users prefer to learn how to operate the microscope and detectors quickly so that they get their results as soon as possible. The most frequent question asked is, what can EBSD do? Then I begin my introduction and I see that they become more and more interested. Sometimes they have high expectations. For example, when I demonstrate stress/strain analysis, I am often asked how to get stress value. This is a common misunderstanding because as an indirect way technique, EBSD can show the strain trend of materials, but it is beyond it to measure stress value.

My routine work includes introduction and training. Over a period of time, I can see a newcomer becoming more experienced and getting his own results, which makes me proud as a supporter. Whereas I care about the EBSD technology itself, the customers are more interested in learning how to use it in their work to solve some of their analysis challenges. They often give me new ideas and make me aware of other areas besides pure technology, for example, how to remove the users’ initial fear for EBSD. As a student majoring in material science I thought crystallography was very different from the reality I now understand. As a ‘teacher’ I am not focused on how to keep our users’ interest on EBSD and reminding to them to use it regularly. Fortunately, social media has improved the speed and consistency of our communication. When issues are solved quickly, people think the EBSD technique is less difficult. Effective communication contributes to the technology transfer.

The level of adoption of EBSD hardware in China is excellent, but the usage of and research into the technique is still in its infancy. I have spoken to many people about this issue. The interesting thing is that outsiders tend to give an optimistic perspective. An Australia professor told me several years ago that we should be taking a longer-term view and that there would probably be, a tremendous change in the next ten years. Quantitative results make a qualitative change. I hope he is right!

Fortunately, EBSD usage in China has increased greatly and continues to increase, which shows us a promising future.

 

 

 

Water, Sand and Salt, and Why We Care About Compounds

Tara Nylese, Global Applications Manager, EDAX

Somewhere around the age of five years old, many of us learn that another way to identify water is by the molecular name, H2O. This usually leads to more questions like: ‘What is H?’, ‘What is O?’, ‘How does that make water?’, ‘Why should I care?’. Over the years, we grow into more advanced chemistry students exploring topics like compound formulas, and we learn that the world we live in is made up of complex associations of combined atoms. A chemical compound is a substance that is composed of two or more chemical elements. The reason that we should care about compounds is that an element such as Oxygen (O) can be very different if it is associated with Hydrogen into H2O to make water, or as SiO2, which is Silicon Dioxide that makes up sand on a beach, or as Fe2O3, which is ferric oxide, loosely known as rust on steel. Therefore, as microanalysts, we should pay close attention to compounds because the elements alone do not always tell us the complete nature of the material we’re analyzing.

Once we grow into an “expert scientist,”* we become deeply entrenched in the details of microanalysis, and we often forget to take a step back to see the big picture. For example, as an EDS analyst, I look at the spectrum below and I think “what a nice sodium peak” or “hmm, am I picking up Al due to scatter at variable pressure?” But unless I’m using it for an introduction to a microscopy and microanalysis student lecture I don’t often simply call it what it is, and that is NaCl, or salt.

Next, we look at the electron image at very low mag and that gives us a better contextual understanding that it is a grain of salt.

When we look back at the spectrum again with a big picture view, we recognize that the main elements present in the spectrum are Na and Cl, and that they make up the compound NaCl, or salt.

In follow up to my recent webinar, I received a lot of questions asking “What are CompoMaps?” and “How can I use CompoMaps?” I was glad to see so much interest in such a valuable routine, and I do hope that users of every level can use this “Compound” view to understand their materials more deeply. To answer the first question, “CompoMaps” is a sophisticated software routine that creates a display of the elemental composition of each pixel. That is, the intensity of the pixel display color is a direct representation of the peak intensity of an element. It is helpful when there is a trace amount of an element, because the routine separates the peak from the background, removing the noise and intensifying the signal. It is perhaps most useful for separating element peaks where there is ambiguity whether there is one element, or another. In the example shown below, I was collecting this data when I happened to get a chance to web connect with an earth sciences professor. After he saw the before and after, he commented that the “after” made much more sense because those two elements would not likely be in combination together in any mineral.

The results here show that Phosphorus in green and Zirconium in purple are definitely located in two different phases.

Before CompoMaps:
After CompoMaps:
Superimposed into one image:
What we didn’t see in the webinar was the Oxygen map, shown here for the first time:
The display shows both with (right) and without (left) the Phosphorus and Zirconium superimposed, and this gives us a better understanding about the compound, since Oxygen is present with these elements. After full investigation of all element maps, we find that the two phases are Ca5(PO4)3F, or fluorapatite and ZrSiO4, or Zircon.

Finally, the answer to the question, “How can I use CompoMaps?”, is easy. This is a routine that EDAX has had in all of our software packages from Genesis to TEAM™ (as Net Maps) and now in APEX™. The routine has been optimized for APEX™ with 64-bit architecture and advanced processing capability, along with an easy to use workflow for results in live-time. So, give it a try and see what you can find!

*My personal opinion is that we should never let ourselves call ourselves experts, lest we forget that there is always something new to learn.