A better understanding of corrosion resistance may be possible using a
data-mining tool, according to Penn
State material
scientists. This tool may also aid research in other areas where massive
amounts of information exist.
In data mining computer programs categorize large amounts of data so they
become more useful. Different types of data-mining programs can find
correlations between data on specific subjects, or in different areas of a
single subject. Data mining finds similarities and differences among data
parameters that frequently, in a complex problem, would go unnoticed because
they would not normally be observed by human inspection.
Kamrun Nahar, research associate, Center for Neural Engineering, along with
Mirna Urquidi-Macdonald, professor of engineering science and mechanics, used
data mining to find the most relevant information about the corrosion-resistant
properties of Alloy 22, an alloy candidate for nuclear-waste canisters. They
reported their findings in Corrosion
Science.
“Data is collected when a phenomenon is poorly understood and
laboratory experiments are carried out,” said Nahar. “Large amounts
of data exist everywhere. Every area of study has terabytes of information that
could be used better by using data mining techniques to extract valuable
information from data.”
Alloy 22 is known for its corrosion-resistant properties and is most
commonly used where resistance to rust and damage is crucial, such as in
radioactive waste containment. Alloy 22 also is used in waste incinerators,
pollution control, nuclear-fuel reprocessing, and chemical manufacturing.
Alloys are mixtures of metals combined for their specific traits. An alloy
usually has different properties than its components and is engineered to
produce a material with the desired properties.
“We looked at corrosion properties,” said Nahar. “What are
the factors, what are the problems with corrosion, and what can we focus on? If
you use this alloy for different applications, what are the effects in a
certain time period? In how many years will you see corrosion and will it not
fade?”
The alloy data came from other researchers’ work on Alloy 22. Nahar and
Urquidi-Macdonald used statistical techniques to clean the data and put it into
a unified format. The data was fed into the computational model the researchers
developed for this project. They used an artificial neural network—ANN, one
type of data-mining system that works similarly to a human brain, asking
questions, answering them, finding patterns, and learning from previous
conclusions.
Data mining is most often used for mapping consumers’ behaviors, like
patterns of purchases, television viewing or Internet use. This work enforces
the idea that data mining is applicable to science.
Using the data from other experiments on Alloy 22, the researchers predicted
future corrosion patterns of the alloy when put under similar environmental
conditions to those in the study. Weight loss numbers were successfully
calculated by the data mining system to estimate how much corrosion of a
certain material would likely take place. The neural network model learned the
functions necessary to map such variables as corrosion rates.
“What comes from this work and the parallel work is that if you manufacture
a cylinder or vessel, you can predict its life depending on the environment
that the vessel is in contact with,” said Nahar. “For example, if you
put it under this environment it is going to last this many years.”
