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How Predict Works Foundrymen
have known the effect of the chemistry for years. The carbon and silicon have
such a strong effect that a great deal of effort was placed on controlling
them. The problem has been trying to determine the effects of all of the
minor changes that are always occurring. The chromium goes up making the iron
stronger while the titanium goes up as well making the it weaker - what's
the end result? To make matters even worse, if the carbon and silicon change a
little at the same time, out guessing the strength can almost be impossible. The
computer's ability to remember and rapidly deal with numerous variables is a
very logical answer to this problem. PREDICT
starts by using a multiple regression analysis pertaining specifically to the
operation and grade of iron in question. It provides the computer with the
analog for handling all the subtle changes that occur during the operation. A
natural question is, why should a multiple regression analysis be performed
when that information is already available in the literature?
The answer is that the published information just doesn't perform well
enough. There are many possible
reasons for the lack of correlation with that data.
The most obvious simply being a bias in the operation spectrometer as
compared to the chemical analysis tools used to develop the information in the
literature. It
should be noted that in some cases, the multiple regression analysis provides
results that are contradictory to what we as metallurgists believe we know.
Such contradictions can be explained by considering those elements
which are not tested. As an
example, a foundry may purchase steel scrap that has lower chromium than
normal but has a higher nitrogen content than normal.
The nitrogen isn't tested for but the chromium is.
Since the increase in nitrogen increases the strength more
substantially than the reduced chromium lowers the tensile strength, the
analysis may indicate reducing chromium increases the strength.
While that isn't what happens, as long as the relationship in the
purchased scrap is maintained, the answer comes out right. Of
course, once the relationship is developed between the chemistry and tensile
strength, it's not difficult to make mathematical manipulations so that an
equation yields the necessary amount of an element to achieve a desired
tensile strength. From there,
knowing the amount of iron being treated and compositions of the alloys being
used, it's an easy step for the computer to develop the amount of the alloys
that need to be added. Target
Adjustment The
above was the thinking behind the original versions of the program.
However, we found that it wasn’t good enough.
We found, for unknown reasons, the results would drift from the answers
predicted. In order to compensate
for that the program tracks the difference between actual and prediction.
It averages that difference for the last ten readings and adjusts the
tensile target accordingly. As an
example, if the average of the last ten tests showed the actuals were 500 psi
over the predicted, the additions would be calculated towards a target that
was 500 psi lower. Compensation
for Recoveries Since
we don't have the final chemistries until it's too late, we must make our
predictions from base iron samples. Again,
it’s a very logical starting point and was how the program started.
As we began to analyze the results, we noticed, at times, there was a
definite bias between the results from base samples as compared to final
samples. While some elements show
the expected loss from being in the liquid state longer, others, unexpectedly,
showed increases even though nothing was added.
(It's
our supposition that the inoculation process may be changing the sample in
some way causing the spectrometer to read slightly different.) No
matter what the cause, we found our results improved if we would take into
account the expected recovery of all of the elements.
PREDICT now calculates the recoveries of all elements as part of the
process. When making the
calculations of additions from the base samples, it averages the recoveries
from the last ten recorded results and adjusts the base readings accordingly. Step
by Step Another
way of looking at how PREDICT works is to look at it step-by-step as it goes
through the process. 1. The user enters the
base data. 2. PREDICT looks up the target desired and adjusts it on the basis of the last ten tests difference between actual and predicted. 3. The program then calculates the recoveries for all of the elements from the last ten tests. 4. The program, using the calculated recoveries, results from the multiple regression analysis, and base iron information, calculates the alloy additions necessary to achieve the adjusted target. 5. The program then checks all of this information to determine if any of the entries or any calculated results are statistically unusual. 6. If a test is to be taken that will be entered later, the user then enters what additions will actually be made and the program stores the data in a file. 7.
Once the tensile results have been
obtained, the user will enter the appropriate information into that part of
the program. 8.
PREDICT re-calculates the expected
tensile properties from the final chemistries results and makes a number of
comparison calculations -- tensile/hardness ratio, comparison of laboratory
hardness reading to foundry's, ultimate strength/yield strength ratio, etc. 9.
It once again compares all of the entries and calculated results to
previous entries to ascertain if anything is statistically unusual. 10.
It stores all of the information in a format that is readable by Excel
and most other spreadsheet programs.
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