1. DFM TOC
Proceedings of DETC’03
ASME 2003 Design Engineering Technical Conferences and
Computers and Information in Engineering Conference
Chicago, Illinois, USA, September 2-6, 2003
DETC2003/DFM-48140
IMPROVING LIFE CYCLE ASSESSMENT BY INCLUDING SPATIAL, DYNAMIC AND PLACEBASED MODELING
John Reap and Bert Bras1
Systems Realization Laboratory
The George W. Woodruff School of Mechanical
Engineering
Georgia Institute of Technology
Atlanta, GA 30332-0405
USA
ABSTRACT
Drawing from the substantial body of literature on life
cycle assessment / analysis (LCA), the article summarizes the
methodology’s limitations and failings, discusses some
proposed improvements and suggests an additional
improvement. After describing the LCA methodology within
the context of ISO guidelines, the article summaries the
limitations and failings inherent in the method’s life cycle
inventory and impact assessment phases. The article then
discusses improvements meant to overcome problems related to
lumped parameter, static, site-independent modeling. Finally,
the article suggests a remedy for some of the problems with
LCA. Linking industrial models with spatially explicit,
dynamic and site-specific ecosystem models is suggested as a
means of improving the impact assessment phase of LCA.
Keywords: Life Cycle Assessment, Life Cycle Analysis, Life
Cycle Impact Assessment, LCA, Environmental Impact
Assessment, Ecological Modeling
1. Introduction
Efforts by firms in the late 1960s and early 1970s to
quantify the direct and indirect material and energy consumed
during product manufacture precipitated the creation of life
cycle assessment methodologies [1]. These early inventories
would later grow to encompass the entire product life cycle from “cradle to grave.” The environmental impacts of products
ranging from milk to petrol have been compared using current
LCA methods [2, 3]. With the passage of time LCA became an
“…important tool for environmental policy, and even for
industry” [4].
1
Patrick J. Newcomb and Carol Carmichael
Environmentally Conscious Design and
Manufacturing Program
Manufacturing Research Center
Georgia Institute of Technology
Atlanta, GA 30332-0560
USA
Despite extensive application and importance to both
government and industry, LCA is a tool beset by problems in
practice and theory. For example, Ayres finds fault with the
data sources used and mass balances created by many LCA
practitioners [4]. And, at international LCA workshops, issues
that influence the LCA methodology such as spatial resolution,
temporal resolution and site specificity are topics of discussion
[5].
These last three topics are of special significance for this
article. Current life cycle assessments use lumped parameter,
static, site-independent models to estimate environmental
impact. The previous decade’s advances in computing and
developments in systems ecology provide an opportunity to
address the issues of space, time and site specificity using
modeling. Linking industrial models of the resource extraction
and manufacturing life cycle phases to ecosystem models may
reduce errors caused by the spatial, temporal and site
assumptions in current life cycle assessments.
2. LCA Within the Context of ISO 14041
To understand the advantages of linked models, one must
first understand the current state of the art in life cycle
assessment. With the advent of ISO 14040, the LCA
methodology has started to consolidate. The ISO 14040 through
14043 standards deal with the Life-Cycle Assessment (LCA)
procedure. The LCA approach described in ISO 14040-14043
is essentially the same as the one promoted by the Society of
Environmental Toxicology and Chemistry (SETAC). Consoli
and coauthors present a SETAC framework similar to the ISO
14040 framework [6]. The two only differ in choices of impact
categories and weighting schemes.
Corresponding author.
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Copyright © 2003 by ASME

2. A conventional LCA consists of the following steps, which
are outlined in the ISO 14040 - ISO 14043 standards and in
SETAC’s ‘Code of Practice’ [6, 7]:
1. Goal definition and scoping - ISO 14040
2. Inventory analysis - ISO 14041
3. Impact assessment - ISO 14042
4. Improvement Assessment (SETAC term)/Interpretation
(ISO term) - ISO 14043.
Although the actual ISO 14040-14043 standard documents
are relatively short, interpreting them can prove problematic.
Cost effectiveness can also be a problem; a study of 34 Fortune
500 companies by Gloria and coworkers shows that just
implementing the LCA standards costs between $15,000 to
$30,000 per product [8].
3. Revealing and Confronting the Limitations of LCA
Despite the growing popularity and usefulness of LCA, the
method is not without its drawbacks. Many of the complaints
about LCA focus on the intensive amount of time, data, money
and effort required to perform a detailed LCA. Although
Gloria and coauthors showed that implementing the ISO
14040-14043 LCA standards costs between $15,000 to $30,000
per product, Graedel estimates that it can cost from tens to
hundreds of thousands of dollars to perform just one LCA [8,
9]. Additional complaints commonly lodged against LCA
include [5, 9, 10]:
• Cannot consider temporal and spatial issues
• Focuses on only environmental considerations (not
economic or societal)
• Regards all processes as linear (such as dose-response
curves)
• Is steady state – not dynamic
• Is laden with value judgments and subjectivity
• Requires difficult or impossible to find data
For these (and other) reasons, LCA results have often been met
with skepticism.
The good news is that many LCA
methodology developers and practitioners have been working
to overcome the limitations of LCA. For example, to tackle the
problem that a detailed LCA is too costly and time-intensive,
more abbreviated LCA methods have been developed. These
are usually referred to as streamlined LCA or abridged LCA
[9]. The streamlined LCA has the advantage of being more
efficient and cost effective, and it can be used in the early
stages of design where quantitative data is scarce but design
freedom is greater.
These advantages mean that the
streamlined LCA is more likely to be carried out, as it is not as
frustrating and cumbersome as the detailed LCA [9].
In addition to methods attempting to address the whole
LCA methodology, many efforts are aimed at improving the
individual stages of LCA. For example, the inventory phase is
particularly data intensive and is usually carried out in a
bottom-up analysis of the industrial processes. There is a
“school of thought” that approaches the inventory from the topdown, using the macroeconomic perspective of input-output
analysis. The EIO-LCA approach developed by Hendrickson
and coworkers is one of the better known examples (see also
www.eiolca.net) [11]. Input-output analysis is believed to be
more elegant, more complete, easier to perform, and less data
intensive than process analysis, but it is less specific and less
accurate [12]. Therefore, some LCA researchers suggest using
a hybrid LCA. The hybrid LCA uses both process and inputoutput analysis to develop the life cycle inventory. The hybrid
approach has the advantage of eliminating the need to define
strict production system boundaries, an often controversial task
[12].
An alternative approach is to build the inventory on
financial principles. Emblemsvåg and Bras advocate building a
LCA based upon known financial accounting principles [13].
In particular, they propose to build upon the Activity-Based
Costing framework in which resources are consumed by
activities, which are consumed by (cost) objects such as
products and services [14]. One advantage is that it allows for
multiple product-LCAs to be conducted simultaneously,
analogous to activity-based product costing. Building upon
existing financial frameworks and terminology should lower
the learning curve for beginning LCA practitioners. However,
even with a financial costing framework as the foundation, data
gathering remains a bottleneck because many companies do not
have a costing system that accounts for the inventory details
needed in LCA.
Of all the limitations of LCA, the methods for impact
assessment have traditionally been the most debated [15].
Hofstetter counts life cycle impact assessment (LCIA)
problems as one of the two biggest problems with LCA [16].
In addition to issues with subjectivity, “LCA does not have an
acceptable way to model impacts” [16]. In 1998, a conference
of LCA practitioners and methodologists convened in Brussels
to discuss the issues with LCIA [5]. There was great concern
about the appropriate level of sophistication used in LCIA.
Sophistication is defined as, “…the ability of the model to
accurately reflect the potential impact of stressors” [5]. The
conference identified many simplifications that are made to the
LCIA phase:
• Reduction in spatial discrimination (or ignoring spatial
discrimination)
• Ignoring fate
• Ignoring background levels of pollutants
• Assuming liner dose-response curves
• Eliminating an impact category altogether b/c the
appropriate assessment methodologies do not exist.
Of the problems with LCIA, two of the most decried are a
lack of spatial and temporal considerations [5, 10, 16].
Hofstetter believes that the most common form of the LCA, the
LCA sensu stricto, has no detailed time or location information
(or actually assumed geographical and meteorological
conditions typical of Western Europe), and he even goes so far
as to call place and time “the neglected children in LCA” [16].
The LCA sensu stricto uses a “…very simple model to
represent a dynamic anthroposphere and very complex
ecosphere” [16].
Some researchers have attempted to compensate for
LCIA’s shortcomings. Potting and coauthors used a spatially
explicit atmospheric model to develop acidification impact
factors for emissions emanating from specific locations in
Europe [17]. Besides taking advantage of spatially explicit
deposition and concentration data provided by the utilized
atmospheric model, their approach also benefits from the fact
that it requires little extra data than that already collected for a
standard LCA. Unfortunately, the low resolution of the
atmospheric model limits their approach. The focus on
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3. airborne emissions also limits the model to atmospherically
related impacts. Furthermore, it is unclear how changing
ecosystem dynamics affect the proposed emissions factors.
Lindeijer summarizes a number of methods meant to
compensate for the lack of spatial considerations – specifically
land use [18]. These methods uniformly depend upon
aggregated indicators, and many do not explicitly consider
time.
Spatial patterns are important when considering
ecosystem dynamics [19]. Thus, indicators based upon land
use aggregations may not provide sufficient information to
evaluate spatial or location specific environmental impacts.
Tolle uses normalization factors to capture regional and local
sensitivities to particular impacts [20]. This approach partially
compensates for life cycle assessment’s lack of spatial
considerations. But, these aggregated normalization factors do
not account for landscape patterns, and they do not consider
changes in ecosystem function. Operating under a broad
definition of industrial system dynamics, Spath and coauthors
conduct a life cycle analysis for each year of a power plant’s
creation and operation [21]. The approach accounts for the
entire life of a facility and its supporting industries, but it does
not include ecosystem dynamics, ecosystem patterns or sitespecific considerations. The EPA developed a LCA software
called the Tool for the Reduction and Assessment of Chemical
and Other Environmental Impacts (TRACI) [22]. This impact
assessment tool contains data for a number of specific locations
in the United States. Though TRACI accounts for local
variations in environmental impact data where possible, the tool
does not account for the effects of ecosystem patterns or
dynamics.
Many issues arise when the spatial and temporal
dimensions are missing. As a result of its lack of temporal
considerations, LCA cannot be used for predictive modeling
[15]. Contributions of a product or process to the concentration
of pollutions in a specific area cannot be known since the actual
background concentrations are unknown. Even if they were
known, nothing can be said about the sensitivity of the areas
exposed [16]. Lack of spatial information often leads to the
focus of impacts on so-called mid-points instead of endpoints.
There are advantages to making impact assessment models
“…as close as possible to the final endpoints of the
environmental mechanism of the impact categories (e.g.,
quantifying fish kills and trees lost as opposed to the
acidification potential of the substances” [5]. Guinee suggests
that some regionalization is possible, but “LCA does not
provide the framework for a full-fledged local risk assessment
study, identifying which impacts can be expected due to the
functioning of a facility in a specific locality” [10]. This is
precisely what we propose to do: model the interactions of an
actual facility placed in an actual ecosystem.
4. Ecological-Industrial Modeling – A Way Forward
As noted in Section 3, traditional life cycle assessment
disregards spatial distribution, ignores time and treats every
location the same. Proposed modifications and extensions fall
short of fully compensating for these shortcomings. A
conceptually direct, though technically challenging, solution to
these problems involves spatial and temporal modeling of the
ecosystems directly impacted by industrial activities. By
linking these ecosystem models with industrial models, one
would create an eco-industrial model capable of simulating
environmental impacts.
Before contemplating the technical challenges associated
with this solution, consider the benefits of such an approach. A
spatially explicit ecosystem model allows the capture of
spatially explicit impacts. Though simplification is often
desirable, the potential importance of spatially explicit
modeling for the fate of chemical emissions has been
demonstrated [23]. A dynamic ecosystem model permits the
modeling of feedback and changes in the system as it responds
to environmental burdens. Finally, a place-based model gives
one the ability to differentiate between vastly different
environments. Given these valuable benefits, the technical
challenges are worth considering.
4.1 Modeling Industries
To conduct such modeling one would need industrial
models, ecosystem models and a means of linking the two. A
life cycle inventory (LCI) mass and energy balance could play
the role of the industrial model. Life cycle inventories include
flows beyond those typically associated with ‘industrial
activities;’ so, one needs to clearly define the scope of the
industrial modeling one will later link with the ecosystem
models. In the context of this article, industrial activities
include one or more of the following processes: resource
extraction, materials manufacture and product fabrication (See
Fig. 1).
Resource
Extraction
Material
Manufacture and
Product
Fabrication
Use
Reuse,
Recycling
and Disposal
Industrial Activities
Figure 1: Product Life Cycle.
However, resorting to a standard LCI would create a static
industrial model. As mentioned in Section 3, the ActivityBased Costing (ABC) models of Emblemsvåg and Bras, which
include mass and energy flows, could also represent industries
[14]. Basic ABC models are also static, but one may use
dynamic process data to augment the model. For example, an
ABC model for a carpet manufacturing facility uses mass and
energy time series data recorded by the accounting department
and gathered by sensors [24]. One may use this data to conduct
historical analyses, or one may use it to calibrate the model to
better predict the resources consumed by and emissions
resulting from future production scenarios. Currently, resource
consumption and environmental burden information reported
by the model is displayed directly to facility managers using a
graphical user interface called a “Dashboard” [25]. In the
future, static and dynamic data contained in the “Dashboard”
database could be passed as anthropogenic inputs to an
ecosystem model.
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4. 4.2 Modeling Ecosystems
Modeling industry falls under the purview of engineers,
but one cannot expect and should not allow an engineer to
model an ecosystem. Thankfully, such models already exist.
Voinov and coauthors created the Patuxent Landscape Model
(PLM) “…to simulate fundamental ecological processes on the
watershed scale” [26].
To achieve a spatially explicit
representation of an ecosystem, a modeled landscape “…is
partitioned into a gird of square unit cells” [26]. A general
ecosystem model (GEM) simulates the ecosystem dynamics
within each cell [27]. For the PLM, the cellular ecosystem
models include a number of modules (See Table 1).
As one can see, the approach is analogous to a structural Finite
Element Analysis where a structure is divided into (simplified)
elements that can be analyzed and are connected using
boundary conditions between the elements.
Table 1: Description of primary modules used in the
Patuxent Landscape Model (Voinov, Costanza et al. 1999)
Module
Unit Cell Hydrology
Nutrients
Macrophytes
Dead Organic
Decomposition
Spatial Surface
Hydrology
Spatial Subsurface
Hydrology
Description
Simulates the vertical flux of water within a
unit cell
Simulates the cycling of nitrogen and
phosphorus compounds within a unit cell
Simulates the growth of plants within a unit
cell
Simulates the decomposition of plant material
within a unit cell
Simulates the flow of surface water and
nutrients (runoff, streams and rivers) among
cells
Simulates the flow of subsurface water and
dissolved nutrients among cells
Software developed by Maxwell and Costanza link the modules
in the GEM and the unit cells together [28]. One models
different locations and ecosystems (i.e. forest, grassland,
swamp, etc.) by changing parameters in the GEM and by
inputting the appropriate data for the unit cell grid. Figure 2
provides a highly abstract and schematic view of the described
ecosystem modeling. It emphasizes the use of non-spatial
ecosystem process models to capture the dynamics, and it
illustrates the use of the unit cell grid in spatial modeling.
Figure 3 displays one of the stock and flow ecosystem process
modules used in each unit cell. The module is implemented
using STELLA modeling software.
Figure 3: Dead Organic Material Ecosystem Process
Module (http://www.uvm.edu/giee/LHEM/)
These models require significant amounts of data. Table 2
gives a truncated list of data needed for the described type of
ecosystem modeling.
Table 2: Truncated List of Required Ecosystem Data
Loosely Grouped by Type [29]
Basic Types
Geographic
Meteorological
Hydrological
Unit Cells
Land Utilization
Ecosystem
Processes
Figure 2: Abstract Ecosystem Model
•
•
•
•
•
•
•
•
•
•
•
•
•
Data Sets
Elevation
Watershed Boundary Data
Shoreline Delineation
Soil Type
Ambient Temperature
Precipitation
Ground Water Elevation
Bathymetry Data
Stream Flow
Surface and Ground Water Quality
Land Cover
Vegetation Index
Growth Coefficients
For the PLM, government agencies, academic institutions,
research programs and regional databases served as data
sources [29]. Costanza and coauthors offer a more detailed list
and discussion of the data sources and types needed for their
ecosystem models [29].
4.3 Linking the Models
Given an industrial model and given the previously
described ecosystem model, one may link the two models to
create an eco-industrial model. The PLM is modular; it
contains multiple physical and ecological modules that simulate
the system’s behavior [26]. And, the software supporting the
PLM is designed to both support and enforce the development
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5. of dynamic simulation modules [28]. One could conceivably
create and insert an industrial module into the landscape using
the software, procedures and tools developed for ecosystem
modeling. Figure 4 simply and abstractly depicts an industrial
module added to one of the grid locations where it is linked
with appropriate ecosystem modules.
Ecosystem
Processes
Industrial
Processes
Figure 4: Abstract Eco-Industrial Model
5. Outlining the Research Effort
An effort to link industrial and ecosystem models is
currently underway in a collaborative effort between the Gund
Institute at the University of Vermont and Georgia Tech.
Though the effort is in its initial stages, three primary research
questions present themselves.
1. Which industrial models are appropriate for combination
with the described type of ecosystem model?
2. How can one connect the two types of models; what
problems wait in the technical details?
3. Can one detect the effects of industrial changes on the
modeled ecosystems? How can one quantify these effects?
These questions are being answered using a case study
format. Initially, a model of a single industry will link with the
ecosystem model (See Fig. 4). The process of selecting the
type of model to link will answer the first question, and the act
of linking will address the second. The next step would involve
the linking of multiple industries to the same ecosystem model.
Linking multiple industrial models will further address
questions concerning the utilized technology. To address the
last question, the simulated ecosystem state with an industrial
model will be compared to the simulated ecosystem state
without an industrial model.
Efforts to answer these three
questions form the basic steps along the path to creating ecoindustrial models.
5.1 Step 1: Know Thy Industrial Models
Selecting the appropriate industrial models to link with the
discussed type of ecosystem model may, on the surface, appear
trivial, but thoughtful consideration leads one to the opposite
conclusion. To illustrate the point, consider one simple
question. Should one link steady state or dynamic industrial
models with dynamic ecosystem models?
Building steady state models requires less time and effort.
Moreover, the cycle time for some industrial dynamics may
occur on time scales far smaller than the response of a
surrounding ecosystem. For example, knowing the exact time
at which a CNC lathe creates waste may not be as important as
possessing an estimate of the number of parts produced per day
and of the waste per part. On the other hand, modeling the
timing of a release may prove exceptionally important for
determining ecosystem impacts.
For example, releasing
materials that cause environmental damage through
photochemical reactions might cause less damage if released at
night when they would have a chance to dilute by morning.
Though these examples are simple, they reveal the importance
of considering what types of models are appropriate for linking.
The research involved with answering the first question
centers on classification. Specifically, a systematic means of
matching ecosystem industry interactions with an industrial
model that captures the detrimental behavior is needed.
5.2 Step 2: Dealing With the Details of Combination
Once one can systematically and appropriately match
industrial models with the described ecosystem models, one
must undertake the task of linking the two types of models.
One can explore the details of linking models by starting with
simple industrial models and simple links, and then, one can
gradually increase the complexity of both.
As a first step, one could represent industry as number of
static or exogenously varying inflows to and / or outflows from
ecosystem process modules such as the one depicted in Figure
3. This configuration would reveal how an industrial facility
affects the environment, but it would not reveal how the
environment affects the facility. If one needs to account for
interactions, one could convert an industrial model into a
STELLA process module, or one could provide C++ code that
represents the interactions. Conceiving and implementing
linking methods such as these form the core of the research
needed to answer the second question.
5.3 Step 3: Assessing Impacts and Validity
Efforts to match and link models prepare the way for the
primary focus of this research – environmental impact
assessment. As mentioned, the detection of impacts will be
explored by comparing a simulated facility in a simulated
ecosystem to the same simulated ecosystem without a facility.
Components of the PLM will be used as the ecosystem
simulation [26]. Additionally, the impacts of a real facility in a
real ecosystem will be compared with a model of the same
facility in a model of its surrounding ecosystem. These two
activities will not only answer the question of detecting impacts
in an ecosystem but will also address the validity of ecoindustrial modeling.
To detect impacts, one must possess a means of qualifying
and quantifying the consequences of environmental burdens.
The ecosystem model described in Section 4.2 can potentially
improve qualification and quantification in the standard LCA
impact categories of land use, biotic resources, eutrophication,
and eco-toxicity on the regional and local scales. Land use in
the ecosystem model alters in response to changes in hydrology
and nutrient flows [29]. One could evaluate the environmental
impact in a rough, qualitative way by simply comparing
vegetation maps with industrial activity to the same maps
without industrial activity.
A number of metrics for
quantifying landscape composition and configuration are
available [19]. One could quantify land use impacts by using
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6. these metrics. Spatial patterns influence ecosystem processes
[19, 30]. So, quantifying these patterns should also provide
information about impacts on biotic resources. Deriving
ecologically significant measures, such as net primary
productivity, from the numerical data would provide another
quantitative measure of environmental impact on the local and /
or regional biotic community. Nutrient data would support
more traditional impact measures such as eutrophication
potential. The model would indicate the location and time of
detrimental concentrations of nutrients, and if an ecosystem
process modules for an aquatic ecosystem was available, one
could simulate the degradation of the system. Though not
currently modeled, one may even be able to include toxic
materials in a manner similar to the way in which nutrients are
modeled.
If linking proves to be feasible and valid, one could
simulate the impact of industrial activity. Environmental
impact comparisons of industries operating separately with
industries operating as an industrial ecosystem will become
possible. The simulation would output a series of snapshots of
the landscape changing with time, as well as a large quantity of
supporting numerical data.
The previous discussion focuses on the resource extraction
and manufacturing phases of a product’s life cycle. This focus
comes from a need to bound the research currently underway –
not from an identified limitation. With intellectual and
technical effort, the proposed type of modeling may apply to
the use and disposal phases as well, though computational
limitations may present a problem.
modeling errors are all research worthy challenges that need to
be overcome before the approach can be applied.
Computational requirements may limit the use of the
augmentation to locales and regions. And, the absence of
modules for toxic materials mandates continued reliance on
traditional LCA methods of evaluating eco-toxicity.
6. Challenges and Limitations
Unfortunately, spatially explicit, dynamic, place-based
modeling of a linked industrial and ecological system comes at
a high cost, and it is not without limitations. Standard life cycle
assessment is data intensive; including eco-industrial modeling
will exacerbate the situation. Installing a suite of process
sensors to obtain environmentally related dynamic data for a
facility can prove costly and time consuming [31].
Topographic, climatic, ecological, biological and other types of
data are necessary inputs to the discussed ecosystem models.
Since a significant portion of this data is site specific, each new
ecosystem model requires extensive data acquisition and
calibration. Even when calibrated, substantial error in the
ecosystem models often remains. For the PLM’s spatial
hydrology model, Costanza and coauthors report percent errors
ranging from less than 1% to 41%, and for some nutrient
concentrations in the ecosystem process modules, errors range
from 16-91% [29]. Computational demands also present a
challenge. During calibration of the PLM for example, the
model’s spatial resolution was reduced from 200 m to 1 km
square unit cells [29]. So, modeling areas larger than river
valleys may prove computationally prohibitive. Furthermore,
the described type of ecosystem model does not include toxic
substances. Only anthropogenic nutrient loading is captured,
the addition of modules for toxic substances may mitigate this
limitation.
These costs, challenges and limitations prevent the
proposed approach from being immediately practical, and they
potentially limit the scale and scope of its application.
Developing the link between industrial and ecosystem models,
overcoming the data obstacles and reducing ecosystem
REFERENCES
7. Closure
As noted and discussed, life cycle assessment is not
without limitations. Significant among these are the use of
lumped parameters, static models and site independent data.
Linking spatially explicit, dynamic, place-based ecosystem
models with industrial models holds the potential to
compensate for the mentioned limitations. However, two main
problems limit the effectiveness of this augmentation for LCA.
The discussed ecosystem model requires significant amounts of
data not collected in a standard LCA, and it does not model the
fate and transport of toxic materials. Nevertheless, the spatial,
temporal and location specific properties of this type of
modeling coupled with its adaptable modular architecture make
it worth developing.
ACKNOLWEDGEMENTS
We gratefully acknowledge the support from the National
Science Foundation and Georgia Tech’s Manufacturing
Research Center. This paper is a result from the work
sponsored by the National Science Foundation under NSF
grants DMI-0085253 and DMI-0225871. John Reap is
supported by grant DMI-0085253.
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