Method Article

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

DOI:

10.3791/61636

⸱

December 4th, 2020

 , 
1Applied Chemicals and Materials Division, MS647, National Institute of Standards and Technology

In This Article

Summary

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The size and shape of powder particles are not independent quantities. Usual measurement techniques do not measure these intertwined parameters in three dimensions (3D). A 3D measurement/analysis technique is described, based on X-ray computed tomography, which can measure size and shape and classify powder particles according to both parameters.

Abstract

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Measuring the size distribution of the particles in a powder is a common activity in science and industry. Measuring the shape distribution of the particles is much less common. However, the shape and size of powder particles are not independent quantities. All known size/shape measurement techniques either assume a spherical shape or measure the shape in two dimensions only. The X-ray computed tomography (XCT) based method presented here measures both size and shape in 3D without making any assumptions. Starting from a 3D image of particles, the method can mathematically classify particles according to shape, for example particles composed of several smaller particles welded together as opposed to single particles that are not necessarily spherical. Of course, defining a single number as the "size" or "shape" of a random non-spherical particle is not possible in principle, leading to many ways to estimate particle size and shape via various interlinked parameters, which can all be generated from this complete 3D characterization in the form of averages and distributions. The necessary experimental procedures, mathematical analysis, and computer analysis are described and an example is given for a metal powder. The technique is limited to particles that can be imaged by XCT with a minimum of about 1000 voxels per particle volume.

Introduction

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Measuring the size distribution of the particles in a powder is a common activity in science and industry1,2.  Measuring the shape distribution of the particles is less common, but both size and shape, along with the material the particles are made from, determine their properties, either alone or in some kind of matrix material3,4,5,6,7. Materials whose particle size and shape are of interest include portland cement, sand, and gravel8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23, metal powders for powder metallurgy and additive manufacturing24,25,26, lunar soil27,28,29, shredded automotive tires30, crushed waste glass31, stem cells32, and carbon nanotubes and graphene33,34,35,36,37. However, the shape and size of powder particles are not independent quantities26. For example, suppose one has a geometrically regular particle whose "size" is said to be d. Without saying whether this particle is a sphere, a cube, or a thin rod of length d, one does not really know how the size applies to this particle. By saying that the particle is a sphere, cube, or rod, one is really specifying the particle's shape, and without this extra information, the size information is meaningless.

For these three examples, a sphere, cube, or thin rod, particle size can be specified by a single number. But even if the rod had a circular cross-section, one would need to also measure the diameter of this cross-section, so two size parameters would really be needed for the thin rod particle. What about particles shaped like ellipsoids, or rectangular boxes? For each of these, three numbers are needed to specify the size, and still the shape must be given as either an ellipsoid or a rectangular box in order for the three size parameters to have meaning. For a randomly-shaped particle, an infinite number of size parameters (e.g., the length of chords across the particle) would be needed to completely characterize the "size" of the particle, and yet these would be meaningless without a "shape characterization," knowing at what angles relative to the center of mass of the particle these chords were drawn.

There are many techniques used for measuring the size distribution of the particles in a powder, employing different physical principles1,2. What is not usually recognized, however, is that in order to extract particle size, information about particle shape, whether assumed or measured, must be used. Current techniques can be classified as: (I) measurements of three-dimensional (3D) particle size while assuming 3D shape, and (II) measurements of both size and shape but only of two dimensional (2D) projections, using 2D image analysis techniques. For spherical particles, all 2D projections are circles, with the same diameter as the original particles, and all these measurement techniques, both Class I and Class II, within measurement uncertainty, give the same results for perfect spheres. For non-spherical particles, the 2D projections are much less closely related to the original particles. If a particle has internal porosity that does not break the particle surface, these pores will not be measured at all by any of these 3D or 2D measurement techniques. Class I includes laser diffraction, electrical sensing volume (ESV)38, sieve analysis, and sedimentation; and Class II covers transmission and scanning electron microscopy, atomic force microscopy, and dynamic and static image analysis with optical techniques. Neither class accurately measures the size and shape of non-spherical particles in 3D.

Since around 200239, a new method of particle analysis has been developed40,41,42,43,44,45 that images a 3D particle in 3D, and then uses several forms of mathematical analysis to represent and classify each particle. A 3D image is saved for each individual particle, which can be compared to the geometrical and mathematical information that is also saved for each particle. This mathematical information can be used to re-generate the particle as desired in any kind of 3D model46,47,48,49, at any location and orientation, or to generate virtual particles that are forced to have the same statistics50,51. This particle analysis method is based on XCT scans of particles dispersed in epoxy or some other such medium. The XCT scans are operated on by specialized software that employ the burning algorithm52,53,54,55,56 to identify particles, and then either spherical harmonic series fitting or voxel counting to generate and store particle shape and size, 3D images of the particles, and, in a second step, geometrical information for each particle. Each particle analyzed has a unique alphanumeric label, which is used to track each particle, the information about each particle, and link each particle to its 3D image. During this analysis process, pores that are inside a particle are analyzed and the total porosity in that particular particle is stored, since XCT reconstruction gives a complete 3D view of a sample.

Three (out of many) geometrical size/shape parameters have been found to be particularly useful in analyzing and classifying particles in 3D: the length, L, the width, W, and the thickness, T. L is defined as the longest surface point to surface point distance across a particle, W is defined similarly as L with the additional constraint the unit vector along W must be perpendicular to the unit vector along L, and T is also defined similarly as L with the additional constraint that the unit vector along T must be perpendicular to both the unit vector along L and the unit vector along W12. These three parameters define the minimum rectangular or bounding box that just contains the particle, and the ratios of these three parameters give valuable but approximate shape information about each particle. Distributions can be made of any of these. It is possible that W correlates well with the "sizes" measured with sieve analysis57, while the "sizes" measured with laser diffraction correlate to a mixture of L, W, and T31.

Finally, the 3D images of a test sample of 100-200 of the particles are visually checked to determine where the cutoffs in L/T are that enable the method to distinguish between single, near-spherical (SnS) particles, and non-spherical (NS) particles, which could be multiple particles welded together, or what are clearly single particles but with an odd shape.

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Protocol

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NOTE: The following protocol is written for metal powder particles with size, according to a volume-equivalent spherical diameter (VESD, diameter of sphere with same volume as particle) approximation, between 10 µm and 100 µm. Assume that the metal has a density in units of g/cm3. Gloves should be worn during the sample preparation steps, along with eye protection. It is important to read over all the steps in Protocol 1, as some equipment needs to be ready before starting the Protocol.

1. Preparation of the epoxy-powder mixture

  1. Prepare approximately 25 g of a quick-curing (5 min) epoxy in a small disposable dish. Aluminum foil boats work well for this purpose. A good epoxy to use comes in a bubble wrap, with the resin separated from the hardener, whose cured density is ρe g/cm3.
  2. Use M grams of the powder, from a well-shaken larger sample of powder, where M is designed to give a volume fraction of about 10% once the M grams are mixed into the epoxy. This is designed to avoid the situation where particles are so near to each other that the XCT scan wrongly identifies them as real multi-particles that are firmly attached. The equation defining M is:

    Equation showing mass density ratio in scientific research calculations.
  3. Mix the metal powder vigorously into the epoxy, manually, using a disposable stirring rod - a simple wooden craft stick works fine - for about 30 s, long enough to disperse the powder adequately. This process, done well, makes the image analysis of the individual particles much more straightforward.
    1. After mixing, scrape the viscoplastic mixture into a compact clump with as much vertical extent as possible, to be ready for the next step. It is important to have the following steps prepared ahead of time, as there will not be much time left before the epoxy hardens.
  4. Use a small vacuum pump connected to an 0.5 m long plastic hose, with a nozzle inserted in the open end of the hose that will fit snugly into a polymer straw of about 3 mm interior diameter.
    NOTE: Cocktail straws, easily available in grocery stores, work well for the 3 mm diameter polymer straw and have a length of about 150 mm. For a nozzle, the cut-off end of a disposable 1 mL to 2 mL plastic pipette usually is effective. Where the nozzle fits into the plastic hose, some electrical tape should be stretched tightly around the joint to ensure an airtight seal. The 25 g of epoxy plus powder should be more than enough to fill two complete straws.
  5. Insert the nozzle into the straw, holding the nozzle and the straw end firmly pinched together. Insert the free end of the straw into the compact epoxy-powder clump and turn on the vacuum pump.
    NOTE: Keep the free end of the straw immersed in the epoxy-powder mixture to avoid introducing air bubbles into the straw - some air bubbles will always be present, but this procedure minimizes their presence. The straws should be filled to within 10 mm from the top - the fill line can be seen through the translucent straw. When the first straw is filled, turn off the vacuum pump and remove the straw from the nozzle.
  6. Wipe off the epoxy mixture from the filling end of the straw and push both ends of the straw into a small lump of clay in order to fill both ends of the straw so that no epoxy-powder mixture leaks out during curing. Place the 2nd straw on the nozzle and repeat, first using the mixing rod to gather the epoxy-powder mixture together if necessary.
  7. After the epoxy in the two straws is cured, cut off the straw ends, where the clay sealer was, with a razor and then cut each straw in half to give four samples. Use one straw as sample #1 for the XCT, mounted vertically so that the X-rays will penetrate across the circular cross-section of the straw.

2. The XCT instrument 

NOTE: These steps assume familiarity with the XCT instrument chosen by the user.

  1. Use a high voltage, usually 100 kV or higher, as there is good contrast between the epoxy matrix and the metal powder particles and there is no need to resolve anything inside the epoxy matrix, which would normally require low voltages around 40 kV. Use a full 360o scan, with a voxel size of about 1 µm. The ratio between the smallest particle size considered and the voxel size used should be a minimum of 8-1039.
  2. Take enough FOVs, over more than one sample, to give enough particles for analysis. If the sample preparations guidelines are followed, 2 to 8 FOVs will be sufficient. Typically, a minimum of 1000 particles is enough for a valid shape/size analysis, but more particles result in smoother distribution curves and better statistics. The reconstructed slices are numbered from 0 to nz-1, from bottom to top of the FOV, where nz is the total number of reconstructed slices.
    1. Save the vertical cross-section slices for each FOV individually, in 8bit format (e.g., tiff) noting the pixel size of each image set (nx x ny), the number of these slices (nz), and the voxel size in micrometers (v). 8bit format is adequate for these kinds of simple metal particle-epoxy matrix gray scale images, which are easy to segment.

3. Assembly of the slices belonging to each FOV into a 3D ASCII microstructure

NOTE: The C program that is used at NIST is called tiff2array.c and is most often used with tiff files but can handle other 8bit formats. It can be compiled as is, with the executable named tiff2array. This program reads in each image, from the bottom up, converts them into ascii format (0 to 255 gray scale) and then stacks them at the end of a master file.

  1. If the reconstructed images are in tiff format, for example, and are numbered consecutively from the bottom up, use the following syntax in a line terminal command: tiff2array *.tiff. This master file, or microstructure file, is a 3D representation of the FOV. If the variable a(i,j,k) is the gray scale at position (i,j,k), where k is the number of the slice, k = 1 to nz, and (i,j) is the pixel in the kth slice, where i is measured from left to right and j is measured from top to bottom.
    NOTE: All software needed in the Protocol is available in the Supplemental Information section of this paper via a link to a NIST database located at https://doi.org/10.18434/M32265.
  2. For P FOVs, make a small file called particle-class-sysconfig.dat, with P lines, where each line reads, for the pth (p=1,P) FOV:
    Filename nx ny nz v b c
    where Filename = a 12-character identification for a particular FOV, b = 1 for an interior scan and 0 for an exterior scan, and c = the number of phases present in the images. When an interior scan is done, there are usually three (c=3) phases present: 1) the epoxy, 2) the black regions indicating the outside of the circular scan and air bubbles, and 3) the brighter particles. Sometimes four phases might appear to the eye to be a more reasonable assumption and so c = 4. Values for c of 3 or 4 are the only two choices. The value of c tells the particle analysis software which Otsu automatic segmentation algorithm, originally written for two-phase images but easily extended to any number of phases, to use to automatically segment the images in a particular FOV26,58. The microstructure files that will be used in the next software program must have 12-character names that are exactly the same as the Filename listed in the FOV-name-sysconfig.dat file, followed by the extension .mic.
  3. Run the particle analysis software pp-Otsu.f, using as input the particle-class-sysconfig.dat file and the various microstructure files, Filename.mic. Make only two changes to this program for a new system, all of which are marked by a comment "USER" in the Fortran source: the filenames for the general output files (change the general particle class name) and the number of FOVs listed in particle-class-sysconfig.dat. The program pp-Otsu.f is in Fortran, is scalar, and is usually compiled in Fortran 77, although Fortran 90 should work fine. It, and all other Fortran programs described below, should be compiled in double precision (-r8) for accurate results.
    1. In addition, since pp-Otsu.f works with large files, always add the parameters (or their equivalent) -mcmodel=medium and -Mlarge_arrays to the compilation. An auxiliary file, gauss120.dat, contains the weights and points for a 120-point Gaussian quadrature used extensively in pp-Otsu.f, and must be in the same directory as pp-Otsu.f. All the programs described below are written in Fortran 77 except for the MPI programs, which are written in Fortran 90.
  4. View the particle files that are the main outputs from pp-Otsu.f, which can take several hours to run on a single processor if there are thousands of particles to be analyzed. These include files with names such as Particle-class-name-anm-particle-number.dat, which contain the list of the complex coefficients (n = 0,26), with units of micrometers or in whatever units v is, for the particles judged to be adequately star-shaped39 and thus capable of being expanded in spherical harmonics functions (called SH particles). Particle files also include files like Particle-class-name-part-particle- number.dat, which contain the number of voxels in the particle and all the voxel positions (in voxel coordinates) for particles that are not capable of being described by spherical harmonic expansions (called nonSH particles).
  5. View the two files, one for the SH and one for the nonSH particles, which give the porosities of all the particles found, even if the porosity is zero, with the phrase porosity in the filenames. An additional program, porosity-analyze.f, should be given the number of lines in each porosity file and their filenames - the file names to be changed are at the beginning of the source code. The output from this program are the two files Particle-class-intern-poros-analysis.txt and Particle-class-intern-poros-list.txt. The analysis file generates the information shown in Table 2 in the Representative Results section, and the list file gives the information needed to generate Figure 5 in the Representative Results section.
  6. View the three tiff image files showing one slice of the first FOV considered. The first slice (OriA-0500.tiff) shows the k = 500 slice of the first FOV in the particle-class-sysconfig.dat file, without any image processing, and the second image file shows the same image but now segmented and thresholded (PixA-0500.tiff). If any limited watershed splitting is applied, then the third image file shows the results of this algorithm (LWSA-0500.tiff). Usually this image processing step is not applied, so the third image file is the same as the second. These images are generated as an error-checking step on the original assembly of the 3D microstructure and the automatic Otsu image segmentation. A general output file (particle-class-name-particles-data.dat) is created listing all the auxiliary information for the processing of each particle. This file is only used as a reference, but the voxel volumes and number labels of all particles processed are written at the end of this file.
  7. For each processed particle, whether SH or nonSH, view the 3D VRML image file, with naming convention particle-name-particle-number.wrl. For SH particles, this VRML image file contains two images side-by-side, a voxel image of the original particle, and a smoother rendered image using the SH coefficients. For the nonSH particles, only the voxel image is stored.

4. Generate geometrical information for all SH and nonSH particles

  1. Prior to further processing, make a list of the Particle-class-name-anm-particle-number.dat file names, called anm.lis, and a list of the nonSH Particle-class-name-part-particle-number.dat particles, called nonSH.lis. Run the small program number.f, having first been edited to have the correct number of files in anm.lis. This changes the anm.lis file to have the number of the particle on each line of the list file, as well as the filename, replacing the previous file.
  2. Use the program part-lwt-listnum-unitvector.f to generate and evaluate geometrical information for the SH particles. This is an MPI parallel program, since there can be thousands of SH particles to be evaluated, and only doing one particle at a time can take days. The only changes in this program that need to be made when processing a new particle class are the Ntot number of Particle-class-name-anm-particle number.dat files, and the particle class naming information for the output (Particle-class-name-un-geom-len.dat) file. The program has comments (USER) at the few places that need to be changed for a new particle type. The output file Particle-type-info-un-SH-geom-len.dat unites particles from every FOV, and each line has the following structure.

    SH coefficients filename, x1, x2, y1, y2, z1, z2, volume, surface area, SA ratio, curv, ratio, nnn, gauss, placeholder, L, W, T, L/T, W/T, T/T, moment of inertia tensor components, L unit vector, theta, phi angles, W unit vector, theta, phi angles, T unit vector, theta, phi angles

    x1 is the minimum x value on the surface of the particle and x2 is the maximum and similarly for y and z. These define an "extent box" that just encloses the particle in its as-measured orientation42. The extent box is used in other applications46,47,48,49. SA ratio is the surface area of the particle divided by the surface area of the volume-equivalent sphere. Curv is the integrated mean curvature inverted and normalized so that it is equal to the diameter when the particle is a perfect sphere. Ratio is the trace of the moment of inertia tensor divided by the trace of the moment of inertia tensor for the volume-equivalent sphere. nnn is the maximum number of SH coefficients (n=0, nnn) that should be used when working with the given particle. Gauss is the integrated Gaussian curvature, divided by 4π, which should equal 1 for a closed object. The point at which Gauss varies from unity by more than 5% defines the maximum number of SH coefficients (n= nnn) that should be used when recreating the particle. L, W, T are the length, width, and thickness of the particle, and were defined in the Introduction section. The independent components of the moment of inertia tensor are listed as I11, I22, I33, I13, I23, I12. Finally, the unit vector for L is listed, in the order of the x,y, and z coordinates, followed by the spherical polar angles θ (angle from the positive z-axis) and Φ (angle of rotation around the z-axis, defined to be zero at the positive x-axis and is positive counter-clockwise). The parameters for W and T follow, listed in the same way.
  3. Use the program nonSH-lwt-un-scalar.f to compute the L, W, and T parameters for the nonSH particles, operating on the nonSH.lis list of filenames, and also record the associated unit vectors. The only changes that need to be made in this file for a new particle class is the number of filenames in nonSH.lis, and the output filenames. The main output from this program, named Particle-name-info-nonSH-len.dat, has each line in the format:

    Filename volume L W T a1 a2 a3 (LWT unit vectors and angles)

    ​where a1, a2, and a3 are the final angles (in degrees - 90o) between the unit vectors for L and W, W and T, and L and T, which are included as an error check for the L, W, and T computation algorithm, since these angles should all be zero for a perfect computation. The L, W, and T unit vectors and angles are in the same format as for the SH particles.

5. Select a subset of SH and nonSH particles to visually determine SnS and NS L/T cutoffs

NOTE: The SH particles, in general, comprise single spherical particles, single non-spherical (ellipsoidal or broken in some way or else a random shape) particles, double particles, and multiple (more than two particles joined together) particles. The particles making up the multiple particles can be spherical or non-spherical. The nonSH particles generally have a few single spherical particles, although mainly with large pores that have broken through to the surface, and the rest are mostly double and multiple particles26. This is determined by viewing a random sample of both kinds of particles with values of L/T from 1 to 2. Such a visual inspection becomes an important step to enable the SnS and NS classification.

  1. Run program (VRML-select-multi-single.f) that reads the Particle-type-info-SH-geom-len.dat and Particle-name-info-nonSH-len.dat files, and picks out 10 particles in each L/T interval of size 0.1, i.e., (1,1.1), (1.1,1.2), etc. This stores up to 100 SH particles with L/T ranging from 1 to 2, and up to 100 nonSH particles with the same L/T range. Two text files are generated (*SH-VRML-list.txt and *nonSH-VRML-list.txt) listing the L/T values and the root names of the VRML image files found. These should be put into a spreadsheet of some kind and ordered according to the L/T value.
  2. Examine the 3D images of each of these particles visually to determine the overall range of morphologies, starting from the lowest L/T value particles. The particles are assessed in terms of whether they are broken particles, double particles, multiple particles, irregular (e.g., not very spherical), and whether they have satellites, which are much smaller particles, attached to the main particle. A satellite is judged to make the main particle a double or multiple particle if the satellite(s) are more than 1/5 the diameter of the main particle. The approximate value of L/T is found that separates single, near-spherical (SnS) particles from multiple and very non-spherical particles (NS), which can be a bit different for the SH and nonSH particles. The first double or multiple particle found determines the cutoff value for both SH and nonSH particles.
    NOTE: The Supplementary Material, located at https://doi.org/10.18434/M32265, includes a spreadsheet file for the particles examined, to see how these cutoff values were determined. There is some uncertainty and a degree of subjectivity in these numbers, which can be assessed by choosing a different set of 100 SH and 100 nonSH particles, with L/T between 1 and 2, to evaluate the cutoff values. Recent work found that this uncertainty was small26 and did not affect the results significantly.

6. Generate 2D projection data from the 3D particles

NOTE: The only current commercial particle analyzers that measure particle shape at all do so with 2D projections. The XCT data can be analyzed to give arbitrary 2D projections, generating data that can be quantitatively matched to the results of these commercial instruments. The 2D projections are made from both the SH and nonSH particles and are combined, with no attempt to classify into 2D SnS and NS categories, since it is not known at present how to define these classes for 2D projections.

  1. Use the two programs, (proj-mpi-SH-LWT.f) for the SH particles and (proj2D-nonSH-LWT.f) for the nonSH particles, to generate three orthogonal projections for each particle, along the direction of the three LWT unit vectors, and then generate Fourier coefficients for the outline of the projection. These coefficients are used to compute various 2D quantities such as area, perimeter, and various lengths and aspect ratios.
  2. A series of (x,y) points are generated and stored for each projection, labelled with the particle file name and 1 for the projection along the L unit vector, 2 for the projection along the W unit vector, and 3 for the projection along the T unit vector. Input these into any graphing program that accepts this input, making sure that the axis limits of x and y should be the same, and each axis is the same physical length.
  3. Store the Fourier coefficients with a similar file naming convention, but this feature is turned off, by default, using comments. The only changes made in either of these programs (program position marked with "USER") is the total number of particles to be considered (Ntot), and the input and output filenames, which should reflect the particle class being analyzed.
  4. The main output from running (proj-mpi-SH-LWT.f) and (proj2D-nonSH-LWT.f) are projection data files, with naming convention Particle-class-info-SH-proj.dat and Particle-class-info-nonSH-proj.dat. A variety of 2D quantities can be computed, including some used by two different commercial instruments (Horiba Camsizer59 and Malvern MORPHOLOGI G360). In both cases, the programs compute a 2D version of L and W, called L2D and W2D.
  5. The output file structure lists, for each particle, area, perimeter, Xcmax, Xcmin, Fermax, Fermin, W2D, L2D, WM, and LM, where Fermax and Fermin are the maximum and minimum Feret diameters and Xcmax and Xcmin are defined from the chord diameters taken in various directions59. It can be shown theoretically that L2D is the same quantity as Xcmax, which can also be seen in the data files. The parameters LM and WM are versions of L2D and W2D defined slightly differently in the Malvern MORPHOLOGI G3 manual60. The file format is: Particle name/number, area, perimeter, Fermin, Fermax, Xcmin, Xcmax, W2D, L2D, theta, WM, LM, slo, and nnnF, where nnnF is the number of Fourier coefficients used to represent the particle projection. The quantity theta is the angle, in degrees, that the axis that defines WM and LM makes with the x-axis60. The quantity slo is just the ratio of the sine of theta to the cosine of theta. Neither quantities are used in the particle analysis described here and are only included as a check on the computation. The data for each particle comes in sets of three, one line for each of the three projections, (1,2,3) = (L,W,T).

7. Processing 3D and 2D particle geometrical data to produce various graphs

  1. Use any software liked by the user to process the particle size and shape data. Whatever software is used, a certain procedure must be followed.
  2. For the 3D data, combine the SH and nonSH data into SnS and NS lists, using the previously-determined L/T cutoff values for each particle class. Distributions and averages should be computed for the SnS and NS particles separately, and also for the combined SnS and NS data, for all geometrical parameters of interest, such as L, W, T, L/T, W/T, L/W, volume-equivalent spherical diameter, and others.
  3. For the 2D projection data, combine the SH and nonSH data. There are three 2D projections for each 3D particle, taken along the unit vectors for L, W, and T. Combining all three of these projections should give results similar to an experimental technique that randomly rotates the particles before making a projection. Using only the L unit vector data, for instance, simulates an experimental technique in which the particles are roughly aligned along their longest direction, perpendicular to the dropping direction and parallel to the strobe light/projection direction in a typical device.

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Results

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ASTM has initiated a proficiency testing program (AMPM, Additive Manufacturing Powder Metallurgy) for metal powder used for laser powder bed fusion, where participants carry out a battery of standard metal powder tests and ASTM compiles the statistical distribution of these results in a report to the participants61. Samples of metal powder are distributed twice per year to all participants. NIST personnel serve as some of the technical advisors to this program, and so have received similar metal p...

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Discussion

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The XCT-based method for characterizing the 3D size and shape of metal particles has more possible applications but also some limitations. The limitations will be addressed first.

A fast-curing epoxy is used so that the viscosity of the epoxy is high enough to prevent the powder from settling under gravity while the epoxy is curing, or at least reducing the time during which settling could happen and the initial well-spaced dispersion degraded. Some settling can still take place, especially fo...

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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The authors would like to acknowledge the long-term support of NIST for 3D powder analysis.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
EpoxyEllsworth Adhesives https://www.ellsworth.com/products/adhesives/epoxy/hardman-doublebubble-extra-fast-set-epoxy-red-package-3.5-g-packet/Hardman Part # 4001case of 100

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