
Intelligent Manufacturing August 1995 Vol. 1
No. 8
According to Paul Sheng, director of the CGDM, "We're looking now
to develop real-world ways to apply the tools we have developed. And
we eventually would like to come out with something we could license
for use in the manufacturing arena. But right now, we're just getting
all the technologies into place."
Real world uses for CGDM's research have already been established at
Sun Microsystems (Mountain View, Calif.), a computer manufacturer.
CGDM developed a recycling process for the computer chassis Sun
Microsystems uses. Thus far, the company has already incorporated
certain aspects of the recycling process in its next-generation
products. CGDM is now looking at Sun Microsystems' suppliers in
regard to manufacturing waste. "We're modeling the environmental
aspects of Sun Microsystems' suppliers to get an idea of how their
processes influence design," said Sheng.
The CGDM is also taking part in a three-year project with Ford Motor
Co. (Dearborn, Mich.) to study environmentally conscious machining.
Sheng noted that beyond their work with Sun Microsystems and Ford,
CGDM is also looking into the manufacturing processes behind
injection molded parts and printed circuit board fabrication and
assembly.
In addition to its work directly in the manufacturing arena, the CGDM
has developed the Green Manufacturing Shell - a software tool
designed to evaluate the environmental impact of different
manufacturing processes. The software achieves this through a
multi-objective analysis of different output dimensions such as
Process Energy, Process Time, Mass of Waste Streams and Quality
parameters.
The software consists of three parts: Green Machining Analyzer,
Health Hazard Scoring Module and Materials Database. While the three
modules have independent functionalities at this point, the CGDM
plans for considerable interaction between them in the future with
the final goal of providing robust process planning decisions that
incorporate environmental factors. The manufacturing processes
currently supported by the Green Manufacturing Shell include
drilling, end milling, face milling, broaching and grinding. Access
to the software is currently restricted to partners and affiliates of
the CGDM.
Emissions Monitoring
Just trying to wade through the morass of regulations can be a
daunting task, let alone trying to comply with them. One of the more
sweeping requirements of the 1990 Clean Air Act, for instance, places
strict limits on the gaseous emissions of manufacturing plants,
particularly in regard to nitrogen oxide (NOx), which is believed to
be a major cause of acid rain. This has led to a need for
manufacturing plants to be able to monitor and predict such gaseous
emissions. Although monitoring of this type can seem daunting, neural
networks - a computer-based technology that uses adaptive techniques
that mimic the way the human brain works - have proven to be
particularly well-suited to the task.
Pegasus Technologies (Painesville, Ohio), for instance, has developed
a neural network-based system for on-line heat rate improvement and
reduction of NOx issuing from its coal-fired furnaces. By optimizing
the processes involved in the area of combustion, the nonlinear
relationships between a large number of parameters can be correlated.
The system has reportedly found better nominal operating conditions
and counter-intuitive methods for improving the process even
further.
At Eastman Chemical Co. (Kingsport, Tenn.), a neural network was
developed from a database of monitored emissions picked up at
one-minute intervals over a two-week period. The neural software then
trained itself to predict emissions. The end result was a system,
dubbed the Software CEM (continuous emissions monitor), which models
processes that produce air emissions and predicts emissions based
solely on the behavior of those processes. The system meets relative
accuracy test audit (RATA) requirements.
A third company, S3 Technologies (Columbia, Md.), has developed a
time-delay neural network (TDNN) for NOx and carbon monoxide (CO)
prediction in fossil fuel plants. The TDNN model was trained on data
obtained from a coal-burning fossil fuel plant. Predictions by the
TDNN after training have proven to be significantly more accurate
than those obtained by conventional means.
What About Congress?
Despite a flurry of activity on Capitol Hill and many harsh words for
the increasingly stringent federal regulations placed on
manufacturers over the past 25 years, it's a safe bet to assume that
a good portion of existing regulations will stay in place. Numerous
polls, even those conducted by pro-business publications, have shown
again and again that consumers not only want but are willing to pay
more for green products. And it follows that these same consumers who
comprise the voting populace are willing to cast their ballots in
favor of the politician who helps assuage their environmental
conscience.
So, while the more fringe requirements trotted out by the new
Congress as examples of overregulation may actually be curtailed
somewhat, the manufacturing community should take advantage of the
research and pro-environmental applications being put to use in a
variety of industries as a means of planning and readiness to comply
with current and future environmental regulations.