Matt Nowell, EBSD Product Manager, EDAX

I firmly believe that one of the factors that has helped EBSD advance as a microanalytical technique is that it makes beautiful pictures. Of course, these images are packed with valuable information regarding the microstructure of materials. But in addition to this scientific content, they catch your eye. In our lab, we have taken advantage of this by hanging the covers of different journals and publications that feature EBSD images collected with EDAX equipment (Figure 1). Some of these are images we have collected internally, and others are from our customers. It is a fun reminder of interesting work that has been done over the years.

We have had an exciting past 18 months with the EBSD product line at EDAX. We launched our Velocity™ high-speed CMOS camera, which delivers greater than 4,500 indexed points per second. We released the APEX™ Software for EBSD, our new data collection platform with powerful analytical capability coupled with an easy-to-use interface. We introduced our groundbreaking Clarity™ EBSD Analysis System, which is the first commercial direct detection system designed for EBSD. As part of the development, testing, and marketing of these new products, I have used these products to collect thousands of images, some of which are utilized to highlight the performance of these new tools.

So how do you choose what makes a good EBSD image? The first step is often picking an interesting sample, but interesting is in the eye of the beholder. Some examples are selected because they use specific materials, like aluminum, magnesium, or steel. I like samples that have interesting microstructures. Sometimes, this is from a novel processing approach, like friction stir welding or equal channel angular processing. Sometimes, it is from a multi-phase microstructure, where structure and chemistry can be characterized simultaneously with EDS-EBSD. Sometimes, it is application focused. In this example, I have selected a sample because it is an additively manufactured nickel alloy. Additive manufacturing is a market with growing interest, and the microstructure is important because it influences the final properties of the material.

Figure 2 shows an Inverse Pole Figure (IPF) map of this material, collected with the Velocity Super at >4,500 indexed points per second. This IPF map is colored relative to the surface normal direction, and I have included a (001) pole figure to show the crystallographic texture and a colored IPF key to help decipher the relationship between the colors and the crystal orientations, which is good practice. This image is interesting because it shows a (001) fiber texture, which explains why many of the grains are shaded red. This helps researchers understand how these grains were growing during the additive manufacturing process. But is it visually appealing? That’s a question I often ask as I share these images for different possible uses.

One possible approach to improving the visual appeal of this map is to superimpose it with a grayscale image derived from other EBSD measurement metrics. Figure 3 shows the same IPF map combined with an Image Quality (IQ) map and a PRIAS™ (center) map. The IQ value is derived from measuring the brightness and sharpness of the diffraction bands within the EBSD patterns. The PRIAS map is calculated from the intensity of the signal onto an ROI positioned within the center of the EBSD detector. Both signals show microstructural contrast and add supplemental information to the IPF map.

How about the colors, though? Is it too red? I hear that sometimes, but I wonder if it is because of the rivalry between the University of Utah (red – where I went to school) and Brigham Young University (blue – where some of my co-workers went to school). What can I do about this? One approach is to specify the IPF map relative to a different direction than the surface normal direction. Figure 4 shows an IPF map where I have selected a [111] sample vector. While it is harder to relate this to the fundamental additive manufacturing process, it does show how you are not limited to specific sample directions. This can be useful if, for example, the thermal gradient present during processing it not aligned with the sample normal direction. In this case, it gives us a different color distribution representing the same microstructure. Is this better?

I have been looking at these maps for 25+ years now, so sometimes it is the new and novel that catches my eye. Figure 5 shows the same microstructure colored using a Quaternion Misorientation scheme. Here a reference orientation is used as a baseline, and the misorientation from this reference is used for coloring. Our OIM Analysis™ software has a wide range of different methods for visualizing microstructures. I personally really like the way this one looks. It is as much art as science.

When images meet those aesthetic criteria, they can be used for marketing, publications, covers, and even clothing. Figure 6 shows a scarf printed using an IPF from a skutterudite material. The crystallization of this material looks a bit like exploding fireworks. I have heard plenty of times that we should be in the tie or T-shirt business with the array of stunning images we can produce. I am always amazed that beyond visual appearance, the information on orientation, grain size and shape, deformation, and phase, among other things, that can be easily represented with EBSD. I hope to continue to find interesting examples to share with you. Special thanks to Tara Nylese for sharing the photo.