Fibre Sizing
Fig 1: Typical fibre segmentation
Introduction:
Man-Made Vitreous Fibres (MMVFs) are used in thermal and acoustic insulation materials
such as glass wool. The measurement of the diameter and length of these MMVFs is useful
for the insulation industry, and serves as a quality control tool for process development.
Hundreds of measurements are necessary to obtain useful information about the material,
but manual measurements of the diameter and length of so many fibres is long and tedious
(and unreliable in the case of insufficiently skilled or tired operators). This motivated
the development of an automatic measurement process, which can be carried out using image
analysis.
The majority of MMVFs used in the insulation industry appear as relatively regular
elongated cylinders, with a diameter varying between about 0.2 - 20 microns and a length
varying from about 1 - 1000 microns or more. A significant minority of these fibres are in
some way irregular: non cylindrical fibres (cone portions), fused fibres, broken fibres,
etc. Due mainly to the small diameter of the thinnest fibres, the use of electron
microscopy techniques is a necessity to obtain high-quality images of such materials. Due
to the extreme range of fibres lengths, it is difficult to measure both the diameter and
the length of each fibre with accuracy in reasonable times. Here, we briefly present one
method for measuring the diameter of MMVFs automatically using image analysis techniques.
This method involves a flat-bed sample preparation method. We will also indicate how to
obtain some information on the length of the fibres when possible.
1:Sample preparation:
Fig 2: Sample preparation technique
A tuft of binder-free MMVFs is extracted from the material. A small quantity of fibres
(about 100mg) is obtained with a punching tool. These fibres are dispersed in aqueous
solution and filtered through a track-etched polycarbonate filter. The filter with the
fibres on it is then coated for observation in a SEM in BSE mode. This procedure is
illustrated by Fig.2. A portion of a typical example of an image aquired in this way is
shown on Fig.1(a).
2: Segmentation:
Before any measurement can take place, the image must be segmented, i.e. fibres on the
filter must be logically separated from the background. The complete segmentation method
cannot be described in detail here; we will just show a segmentation result on Fig.3:
Fig 3: A typical segmentation
In this example, large and small fibres are segmented in the same way. The coloured
line in the center of each segmented fibre section indicates which object the section
belongs to. Note that the parallel fibres on the left of the images have been recognized
as two distinct fibres, and that the segmentation procedure is not confused by fibre
crossings.
The segmentation procedure is entirely automated and takes about 30 seconds per image
on a PC-pentium 90 running under Unix/NeXTStep. The images in Fig.3 have been reduced and
compressed. Normal images are 560x510.
3: Measurements:
All measurements are made at a magnification of x1000 on 560x510
images. An individual pixel in these image is 0.162 microns in width and height. Fibre
diameters are measured using the distance transform information, averaged along the
skeleton of the fibres, and fibre length using the skeleton length information. In
addition to the fibre diameter and length, the number of visible fibre extremities of each
fibre is also recorded. As shown in [ref3],
this extra information provides an unbiased diameter histogram as well as the mean length
by diameter class, which in turns allow to build the length, area and volume weighted
histograms.
4: Results:
The reference standard for both diameter and length measurements is the manual
measurement. However, trying to correlate individual fibres measurements does not make
sense because human operators and automated methods do not necessarily ``see'' the same
fibres, and when they do, both give nearly identical results. In other words, it is the
segmentation step which is most important, not the measurement step. To present meaningful
comparisons between human and automated measurements, we have correlated the diameter
class frequencies of several histograms as given by the automated method with the same
diameter class frequencies obtained by manual measurements. Both manual and automated
measurements were made from the same images of a variety of MMVF products. The frequencies
of three different histograms for the flat-bed method (corresponding to about 2000 fibre
measurements) were merged. This result is shown on Fig.4:
Fig 4: Correlation plot, manual vs. automated
measurements
As can be seen, the correlation is generally excellent, except for two points which
correspond to a sample with extremely thin fibres (median diameter below 0.5 microns)
where both the image analysis procedure and the human measurements break down at the
chosen microscope resolution. Results improve when resolution is increased.
A similar correlation can be obtained for length measurements.
Conclusion
We have outlined an entirely automated method for measuring the diameter and length of
man-made vitreous fibres. This method is in use in actual production plants for quality
control and process development purposes. A typical run (diameter and length measured for
1200 fibres) on a product is entirely automated except for the sample preparation, and
takes between 2 and 5 hours depending on the product. This is much faster and just as
accurate as manual measurements.
References:
- [ref1] Talbot, H. Analyse Morphologique de Fibres
Minerales d'Isolation. PhD Thesis, Ecole des Mines de Paris. Oct. 1994. [In French].
- [ref2] Talbot, H. and Jeulin, D. and Hobbs, L.W.. Scanning
Electron Microscope Image Analysis of Fiber Glass Insulation. MAS/EMSA Conference,
Boston, 1992.
- [ref3] Talbot, H. and Jeulin, D. and Hanton, D. Image
Analysis of Insulation Mineral Fibres. Microscopy,Microanalysis and Microstructures,
to be published, 1997.
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