The development of a sustainable system under uncertainty [Amy Hermann,
ISSS 1998 Paper Session, July 23/98]
These notes are a rough transcription,
prepared as each individual presenter and/or commentator spoke at the ISSS
1998 conference. Gaps and errors have likely occurred. For more accurate
citations, please consult the original presenters. These notes have been
contributed to the ISSS by David Ing, of the IBM Advanced Business Institute
(sabi@systemicbusiness.org).
[Paper session, July 22/98, 3:40 p.m.]
Amy Hermann and Janet Allen, Georgia Tech, with David Rosen
More correctly, a system which promote sustainability under uncertainty
Work focuses on ...
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Rapid Tool Test Bed -- NSF-funded
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Fuzzy arithmetic
Two questions:
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How can automated tool be used?
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How does uncertainty impact tools?
Sustainability as it pertains to design
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Typical design process for an injection-molded product.
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Design product, design mold, rapid prototype, use mold, then iterate until
customer is happy.
System-based design:
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Primary role of designer is to make decisions
Two types of decisions:
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Selection decision
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Given feasible alternatives
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Define principle attributes and relative importance
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Rate alternatives
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Rank feasible alternatives
Usually run into range set of capabilities:
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e.g. anywhere from 7 to 10 on a scale of specifications -- consider using
fuzzy sets.
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Testbed uses Design for Manufacture paradigm fit.
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Part designer has to tell manufacturer the purpose of the design.
First Design:
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Part representation
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Design specification
Then DfM:
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Prelim select materials,
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... rapid tooling technique
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... injection molding technique
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geometric tailoring
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...
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...
Range of capabilities, using fuzzy arithmetic: triangular membership distribution
of min, max and peak
Example problem:
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Rapid prototype of part (rather than the mold, because don't have the mold,
at this point).
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Will develop a simple rib on a plate.
Crisp process selection word formulation:
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Five processes
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Stereo lithography, adding layers
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Identify attributes: build envelope (for vat); relative importance, and
attribute scales.
Fuzzy selection is the same, except use fuzzy sets according to attributes.
Manufacturer's specification as interval scales: high, good/average,
and low.
Performed scenarios for crisp and fuzzy.
Impact: Can reduce number of iterations between distributed design and
manufacturing centers.
Future work: Need to look at fuzzy material selection (which must then
be coupled with process selection, but not all materials work with all
processes).
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