CPT International 4/2019

Figure 9. Detail view of the furnace cooling circuit.
measurement curves for several crucible
campaigns. At the end of the crucible
campaign, the recorded curves are compared
with the actual wear condition of
the crucible and correlated. In this way,
it is possible to derive - from the characteristics
of these curves - the expected
duration of a crucible campaign and the
time at which a reline will become
necessary.
The advancing wear is clearly visualized
in the process screen views, as
shown in Figure 8. The measured frequency
and the wear index are represented
by the length of the green horizontal
bar. When the crucible has been
freshly relined, the green bar is completely
filled. The length of the bar decreases
as the crucible wall becomes thinner.
The green bar disappears completely
when the minimum wall thickness has
been reached.
The information derived from this
signal has always an “integral” meaning.
For physical reasons, the described
method of determining the wall thickness
based on characteristic measured
values always refers to the complete
crucible in the area of the active coil.
Any localized lining wear, such as an
“elephant’s foot” or annular erosions,
are detected during the regular visual
inspections of the empty, red-hot crucible.
In combination with these visual
checks, the “integral” system assures a
reliable and straightforward assessment
of the crucible wear.
Figure 10: Detail picture of a crucible wear diagram (red: course of the frequency,
blue: course of the wear number).
Outlook
While we have already achieved many
important benefits from the consistent
data management so far, there is more
potential available. To tap this potential,
we, the plant operator, work closely
with the plant manufacturer. It
may become possible, for example, to
use the results from the thermal analysis
more efficiently for an enhancement
of the melt quality. It is also conceivable
to develop algorithms from
the available data, which facilitate and
help to further enhance the coordination
between the meltshop and the
mouldmaking staff, making processes
even more efficient. Another conceivable
function would be to determine
where in the process chain specific
quality features are influenced and –
vice versa - to correlate these features
with specific instances of the process
chain. Based on the acquired process
data and the evaluation of tolerances,
deviations in the production process
may be used to identify weaknesses,
for example, by means of automatically
generated algorithms specifically
designed for the requirements of
foundry processes.
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References:
www.cpt-international.com
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