OIM

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.

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).

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

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:

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

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.

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.

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.

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).

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.

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.

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.

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.


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!

Real Problems – Smart Solutions!

Frank Cumbo, Director of Sales and Marketing EDAX

Our mission at EDAX is to advance the understanding of materials for the benefit of science and society worldwide.  As Director of Sales and Marketing, my aim is to accomplish this by marrying a deep understanding of market needs with advanced technology to solve our customers’ material science problems at the nanoscale.  Once our technical team demonstrates a promising new technology, our job is to commercialize these technologies to bring the most advanced products to market whether it be in telecommunications, information technology, biomedical, energy or aerospace and automotive.  This blog will explore just a few markets and the new technologies we are working on to bring the next generation of cost effective solutions to our customers, so that they can produce products that make everyone’s life a little better.

Light alloy
Both consumer demands and legislative requirements are compelling automotive manufacturers to develop more fuel-efficient vehicles. A primary method of improving automotive mileage performance is to reduce vehicle weight. Energy Dispersive Spectroscopy (EDS) and crystallographic structure via Electron Backscatter Diffraction (EBSD) enable users to better understand material microstructure and control material properties. Analysis of microstructure is important for optimizing joining parameters in order to provide a consistent quality final result. Information from these techniques includes: crystal orientation, phase content and distribution, grain size and shape, grain boundary character and plastic strain distribution.

Oil and gas
Several emerging microanalysis technologies will be very useful for oil and gas exploration and operation. It is important to understand the microstructure of shale and sandstone in reservoirs which contain trapped oil and gas. Information on porosity and permeability can be generated using high speed, 3D EBSD synchronized with multiple EDS detectors for chemical analysis and Large Area Mapping. Throughput of the combination of EDS and EBSD enables fast, affordable analysis of important factors that drive yield and extraction costs in reservoirs. Another technique that helps increase speed of analysis by quickly focusing in specific areas of interest is a new product EDAX offers called Pattern Region of Interest Analysis System (PRIAS). PRIAS is a new “virtual Forward Scatter Detector” with software that enables rapid pattern collection (~1500fps) prior to performing a full OIM scan. EDAX is also exploring development of an easy and affordable Wavelength Dispersive Spectroscopy (WDS) based technique to help analyze the clay and ground soil looking for trace elements that have a big impact on the ability to extract gas from the ground.

Semiconductor
Perhaps the semiconductor industry is the most reliant on advanced microanalysis solutions to keep driving Moore’s law where low cost manufacturing at the nanoscale is required. Recently, commercialization of large area, windowless EDS detectors has enabled improved X-ray detection sensitivity, which is indispensable for high quality elemental mapping at atomic resolution. These detectors are excellent for measuring trace elements required for failure analysis and new product development. We are now testing the use of dual detectors to provide useful tomography information. Also, the EBSD technique is useful for grain structure and boundary analysis of TSV to minimize the applied stress in order to improve reliability and optimize manufacturing processes for 3D integrated circuits. Particle analysis is another application used extensively in semiconductor and the related field of hard disk drive read/write head manufacturing. Three dimensional imaging is very useful for particle analysis and EDAX is continually working on ways to bring more physical and chemical quantification of the materials in the region of interest.

A summary of markets, applications, market drivers and microanalysis solutions is presented below.

Market Applications Market Drivers Microanalysis Solutions
Electronics
  • Defect analysis
  • Failure analysis
  • Process development
  • Product development
  • Shrinking geometries
  • Increasing complexity
  • Light element sensitivity
  • Fast element mapping
  • Atomic-level resolution
Natural Resources
  • Exploration
  • Production
  • Rising global demand
  • High throughput
  • Stable resolution
  • Large area 3D mapping
Materials Science
  • Research
  • Failure analysis
  • Quality control
  • Product development
  • Nanoscale development
  • Infrastructure in Asia
  • Spatial/Spectral resolution
  • Quant mapping
  • Peak deconvolution
  • Orientation precision
  • 3D quantification
Life Science
  • Cell biology research
  • Structural biology
  • Drug research
  • Particle analysis
  • Pathology
  • Medical devices
  • Maximum signal capture
  • Trace element ID
  • Spatial resolution
  • Automated phase analysis

EBSD Analysis Between a Rock and a Hard Place

Travis Rampton, Applications Engineer EBSD


As an applications engineer at EDAX, I have the opportunity to work with many different people on a regular basis. Each person I meet has his or her own unique reason for using our microanalysis systems. At one recent event I was able to work with Dr. Nigel Kelly, an assistant professor from Colorado School of Mines. Besides bringing his great Australian accent he also brought an interesting geological specimen to study via EBSD: a single embedded Zircon grain. While this might not seem exciting to someone with a background dealing with polycrystalline metals, the remainder of this post will use EBSD data to demonstrate otherwise.

To begin understanding why the Zircon specimen was fascinating enough to deserve its own blog post, I will provide a brief background on the sample. Before being extracted, mounted, and polished, the observed grain (BSE and IPF image shown above) was subjected to the presence of Uranium by natural causes. The exposure to radiation caused crystallographic changes in the grain which were manifested as deformation within the grain. Crystallographic deformation has long been observable by EBSD and so this particular specimen seemed well suited for the technique.

EBSD offers many metrics for measuring and quantifying deformation in a crystalline material. Research has shown the usefulness of both image quality (IQ) and local measures of misorientation (kernel average misorientation (KAM), grain average misorientation, etc.) in obtaining this information via EBSD. Each of these metrics is important depending on the magnitude and length scale of the deformation present in a specimen.
Image quality is a metric that looks at the intensity of the Kikuchi bands in an EBSD pattern at each point in a scan. Mathematically it is defined as:

where N is the number of measured Kikuchi bands used and H is the value of the Hough transform located at peak (p_i,θ_i). Typically maps of IQ are used like images obtained from traditional metallographic etching under a light microscope to reveal grain structure (see below).


The darkest areas are indicative of grain boundaries. This occurs due to mixing of patterns at grain boundaries which causes a reduction in the intensity of all reflected Kikuchi bands. While this phenomenon is manifested at grain boundaries it can also happen for other reasons. Those causes are generally on the length scale of the electron interaction volume, so anything that affects the crystal structure with a ~100 nm radius of the scan point could manifest itself in the IQ map. Below is the IQ map of just the Zircon grain coupled with a few EBSD patterns (96 x 96 pixels) from indicated regions.


Conversations with Dr. Kelly revealed that the variations in image quality in this sample point to metamictization within the grain. Metamictization occurs in the grain due to the presence of Uranium which causes the crystal to become amorphous. As the crystal structure degrades so do the EBSD patterns and associated IQ which can be seen above.

The metamictization seen via the IQ maps is not the only detectable deformation mechanism that is present in this specimen. Other measures of local misorientation show subtle changes in orientation at discrete zones in the grain. For this specimen two conventional measures were used along with one novel metric. The first misorientation map below shows the KAM which has been a traditional measure of plastic deformation in crystals since the early days of EBSD (i.e. before my time). In the KAM map there are a few distinct regions of higher values which indicate subtle changes in crystallographic orientation often associated with increased dislocation content. The second maps show the grain reference orientation deviation (GROD) angle which is displays the misorientation angle of a point from the grain’s average orientation. With this map we get a different perspective of the deformation in the sample. Additionally, the misorientation angles are all less than two degrees so the visible plastic deformation is really quite small. Finally, the new metric used on this sample was a GROD axis plot. Previously, deformation only focused on the angle of misorientation since these measures could be readily correlated to % plastic strain values. However, the addition of mapping the axis reveals the deformation zoning in a sample which might not otherwise be visible with traditional measures.


While the KAM and GROD angle maps provide quantitative values on the degree of deformation in the Zircon grain the GROD axis map shows the subgrain structure. In the words of Dr. Kelly, “The GROD maps show nicely the sub-grain rotation that is occurring. But what I find cool is that it is radial – the higher U core compared with rim causes differential expansion of the two domains, the core expanding greater than the rim causing radial fractures” (now reread that in your mind with an Australian accent for better effect). These radial fractures can be best seen in the BSE image at the beginning of this blog and the GROD axis map.

The maps used in this blog to show various modes of deformation are a subset of all possible measures. PRIAS imaging was also used on this sample and showed several types of information including compositional difference, topography, and orientation/deformation contrast (see below). With all of the types of deformation that can occur in a sample it is nice to know that EBSD provides several metrics for extracting each bit of that information.

Cooking with EBSD – In-Situ Heating Experiments

Matt Nowell, EBSD Product Manager

OIM is an ideal tool for characterizing and investigating the effects of thermo-mechanical processing on polycrystalline materials.  Keyword analysis of recent publications indicates that recrystallization, grain growth, and phase transformations are of strong interest to the EBSD community.  One approach for investigating these phenomena is to dynamically characterize microstructural development using an in-situ heating stage combined with OIM measurements.

For me personally, in-situ heating stage work has been a fun, challenging, and rewarding application.  My initial introduction to this was using a heating stage to investigate the recrystallization and grain growth of copper damascene structures used in microelectronic integrated circuits (Field and Nowell, 4th International Conference on Recrystallization and Related Phenomena, 1999).

This early work was done with an analog SIT camera, where we were lucky if we could collect 4 points per second.  For comparison, the Hikari XP camera can collect up to 1000 points per second.  Obviously, this faster collection capability allows for better temporal resolution of dynamic events.  In addition to faster cameras, a number of software features have been implemented to make in-situ work easier, including a remote API to set up and capture multiple OIM scans, a batch processor to analyze multiple datasets easily and consistently, and an alignment tool to correct for small spatial shifts in data due to physical and thermal drift effects.

There are a couple of different approaches I’ve used with an in-situ heating stage.  The first is to heat the sample to a given temperature, and hold it there while multiple OIM scans are collected to capture the dynamic microstructural evolution.  The first data series was collected from an OFHC copper sample that had been Equal Channel Angular Processed (ECAP) to introduce significant strain and produce a small grain size.  It is this internal plastic strain that provides the driving force for recrystallization, which is the formation of new strain-free grains.  The sample was held at ≈165°C and OIM scans were collected from a ≈30µm x 30µm region with a 200 nm step size at a rate of 1 scan per minute once the target temperature was reached.  The following movie shows the combined Image Quality and IPF Orientation maps collected at each scan.


You can easily see the nucleation and growth of the recrystallized grains from within the initially deformed matrix.  Significant twinning is also observed within the recrystallized grains.  The Local Orientation Spread (LOS) measurement can be used to identify and differentiate deformed and recrystallized regions.  In this next movie, the combined Image Quality and LOS are shown, with the LOS colored on a thermal scale where blue indicates recrystallized, lower plastic strain regions, and warmed colors indicate the presence of plastic deformation.


The movies of course are very fun to watch and show, but it is important to note that each scan representing a section of time during the dynamic experiment is available for comprehensive analysis.  Additionally it is easy within OIM Analysis to compare different time sections, which can allow correlation of recrystallization nucleation with the initial deformation microstructure or measurement of grain boundary migration rates.

Another approach is to drive the sample to the desired temperature, collect OIM data, and then either increase or decrease the temperature to the next desired value.  This approach was used to monitor the phase transformation between BCC ferrite and FCC austenite in low carbon steel.  Patterns from Ferrite (collected at 600°C) and Austenite (collected a 920°C) are shown here:

Pattern from Ferrite Pattern from Austenite

As you can see, even at higher temperature, high-quality EBSD patterns can be collected.  The phase maps collected at different temperatures are shown here:

Phase maps created at different temperatures

The transformation cycle from initial ferrite to austenite to final ferrite is captured during this temperature sequence.  The orientation maps at each step are shown here:

Orientation maps

A few features of interest are apparent.  First, there is a significant amount of twinning present in the austenitic phase structure.  Second, the initial and final ferrite phase structures a very different, indicating that this is not a fully reversible transformation.

Obviously there is a wealth of information contained within these related datasets, and full analysis and interpretation could be used to drive a number of publications.  For this blog however, I just wanted to convey a sense of analytical possibilities that using an in-situ heating stage brings to EBSD.

Finally, I’d like to thank Seiichi Suzuki from TSL Solutions for both designing and building wonderful heating stages for EBSD work and sharing the low carbon steel data as well as David Field from Washington State University for introducing me to and encouraging me with in-situ measurements.