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Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition IMECE2013
November 13-21, 2013, San Diego, California, USA
IMECE2013-65220
TOWARDS THE SYNTHESIS OF PRODUCT KNOWLEDGE ACROSS THE LIFECYCLE
Paul Witherell, Boonserm Kulvatunyou, Sudarsan Rachuri
National Institute of Standards and Technology Gaithersburg, MD, USA
ABSTRACT
Product lifecycle management is an important aspect of
today’s industry, as it serves to facilitate information exchange
and management between most, if not all, stages of a product’s
existence. As exchanged product information is inevitably
subjected to multiple transformations and derivations,
information transparency between lifecycle stages can be
difficult to achieve. Synthesizing representations of product
information across the lifecycle, by creating a lifecycle-stage-
independent platform, can provide transparent access to
information for both upstream and downstream applications.
In this paper, we review previous and ongoing efforts using
ontologies as a means to support information integration and
interoperability throughout the lifecycle of a product. We
propose that existing efforts can be leveraged to create an
upper-tiered ontology for product information. The resulting
ontology, a core model for product lifecycle information, would
support the synthesis and exchange of product information
across lifecycle stages, improving access to this information
and facilitating lifecycle thinking.
We discuss the use of ontologies as a means to create and
link paradigm-independent representations. We discuss the
translations that product information may face when integrated
through ontologies, and the extent to which the integrity of the
information can be preserved across the lifecycle. We
investigate the role of information quality in the exchange and
evolution of product information across the lifecycle. Finally,
we discuss the application of an upper-tiered ontology,
particularly the advantages offered by increased transparency
and interoperability, as a means to support lifecycle thinking for
mitigating a product’s sustainability impact.
LEVERAGING PRODUCT LIFECYCLE MANAGEMENT
The product lifecycle connects distinct stages of a
product’s existence across a lifespan. Common expressions
used to refer to the span of the product lifecycle are “cradle to
grave” and “cradle to cradle.” Each of these refers to the
lifespan of a product beginning at conception and finishing at
“end of life,” where end of life may be disposal or renewal,
through means such as recycling or remanufacturing.
Traditionally, lifecycle management techniques have
allowed companies to reduce costs by organizing and
dissipating product-specific information at different stages of a
lifecycle [1]. Lifecycle management began with a focus on
data, in the form of Product Data Management, or PDM
systems. The idea of data management has long since extended
into knowledge management. As noted in [2], “Unlike PDM
systems which focus on managing data, Product Lifecycle
Management (PLM), at its core, is a process which supports
capture, organization and reuse of knowledge throughout the
product lifecycle.” PLM is influenced by knowledge from
various stakeholders, helping manufacturers to manage “stages
of existence” as a product progresses through its lifecycle [3].
PLM “seeks to fill the gap between enterprise business
processes and product development processes [2].”
In today’s industry, advances in information management
systems such as PLM have allowed manufacturers to better
communicate with their supply chain, within their own
companies, and across lifecycle stages. The capacity to which
information can be managed across lifecycle stages is
influenced by the accessibility of product information at each
stage. As information becomes more accessible, the ability to
manage information across the lifecycle increases. Significant
factors that influence the accessibility of information across the
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lifecycle include how information is structured and
represented..
Given the importance of information and information
exchange during the lifetime of a product, information
requirements cannot be satisfied by a single standardized
representation. The methods used to capture and communicate
information vary between the stages of a product’s lifecycle.
Information representations are often tailored to the information
needs of a specific lifecycle stage or stakeholder. For instance,
information representations may vary based on the application
objective, the standard adopted, the model employed, or any
custom stakeholder requirements. The many information
representations employed across a lifecycle can make the
synthesis of information, and as a result information
transparency, difficult to attain.
TOWARDS INFORMATION TRANSPARENCY
Information transparency refers to a mechanism by which
the uncertainties in information are managed to better
coordinate external and reverse flows [4, 5]. In other words, it
addresses our ability to obtain a value and the certainty at
which the value can be obtained. Information transparency
between lifecycle stages can be difficult to achieve, as product
information is inevitably subjected to multiple transformations
and derivations. We envision transparency can be attained
through a holistic understanding of product lifecycle
information exchange: what information is being recorded;
when information is being recorded (lifecycle stage); where, if
any, information exchange occurs between lifecycle stages;
why (for what purpose) is the information being exchanged;
and how (what, if any, is the transformation) the information is
exchanged.
As noted, product information comes in very diverse
forms. Earlier work at the National Institute of Standards and
Technology (NIST) reviewed the coverage of various
information standards across the product lifecycle and from
multiple viewpoints (Product, Process, and Enterprise)[6]. The
motivation behind this work was to understand what
methods/languages were available to represent product
information at different lifecycle stages, and what, if any,
synthesis was possible. While the work discussed in [6]
focused primarily on coverage, later works further addressed
the need for interoperability between these standards. In [7],
Fiorentini et al. discussed the advantages of representing
product information using ontologies. In [8], Fiorentini et al.
discussed the use of ontologies as a means for harmonizing
information between standards.
Related research has noted that semantics, coupled with
open standard representations, are essential to the future of
product knowledge management. In [9], Fenves et al. discuss
the evolution of product data exchange, identifying many of
the demands associated with the exchange of data between
systems. They note that “consensus-based open standards will
form the basis for the future global information exchange in a
seamless manner and that they will need work towards
developing semantics-based approaches.”
As a step towards information transparency, we begin by
reviewing ontological representations of commonly employed
information modeling paradigms used throughout a product’s
lifecycle. We envision a framework that synthesizes these
paradigm-independent representations of product information
into a unified model, and that such a model can lead to
newfound transparency of the exchange of product information
between lifecycle stages.
LITERATURE REVIEW: ONTOLOGY TRANSLATIONS OF PRODUCT INFORMATION REPRESENTATIONS
Traceability of product information through the lifecycle
requires the identification of key product information artifacts,
and an understanding of how information is exchanged through
different stages of the lifecycle. Towards traceability, and our
goal of lifecycle synthesis through standards, we turn to
ontologies and the use of the Semantic Web. Ontologies, as a
means of providing an explicit specification of a
conceptualization [10], have been the focus of many efforts for
providing a neutral platform for the exchange of product data.
In [11], Rachuri et al. explored the use of information
modeling techniques to facilitate information interoperability
between different stages of the product lifecycle. In [8], this
work was revisited in the context of ontologies, specifically
OWL (Web Ontology Language). The authors concluded that
OWL sufficiently supports the practical requirements of PLM
applications. This position was supported in [12], where the
extent to which OWL supports product information was
investigated. Works such as these have supported the growth
and adoption of OWL as a means for information exchange at
and between different stages of the product lifecycle.
This section discusses numerous previous and ongoing
works that have sought to use ontologies for product
information representation. Many of these works are based on
existing standards. Others are based on product information
models or PLM systems. Others are original ontologies that
leverage key concepts from various sources. Here, we review
existing works before proposing that an aggregation of the key
concepts in these standard-based ontologies can be used to
harmonize information flow across the product lifecycle.
Leveraging Existing Standards Initiatives to translate existing standards into ontologies
have come in two forms. Some have taken the approach of
creating full representations of existing standards in an
ontological language. Others have created the profiles needed
to translate information from the standard to an ontological
representation. Each of the standards in Figure 1 has been
translated into, or previously existed in, the Semantic Web’s
OWL.
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Figure 1. Standard coverage with ontology representations across the lifecycle. Overlapping standards (hidden layers) continue on
defined path.
McKenzie et al. [13] recognized the need to share
information between software systems for successful
information management across the product lifecycle. As an
alternative to custom interfaces, they proposed the use of
standard file format-based ontologies to exchange data across
the lifecycle. Their research resulted in a repeatable
methodology for creating an ontology from a native CAD file,
by way of ISO 10303, informally known as the Standard for the
Exchange of Product model data (STEP), with currently
available software. A complex product model is created in 3D
CAD software and exported using the STEPstandard. They
contend that ontologies allow for machine reading and
automatic translation of information. As such, an open source
file converter is used to translate a STEP file from EXPRESS
(which STEP is encoded in) to XML. The XML file is then
converted to the OWL file format by way of an ontology editor,
providing an OWL representation of a STEP model (coverage
in Figure 1).
Similar work by Barbau et al. also sought to create OWL
translations of STEP by means of the tool OntoSTEP [14].
They acknowledge differences between modeling languages at
different lifecycle stages and maintain that, to build a coherent
knowledge base, it is necessary to consolidate product
information encoded in different languages. Unlike the
approach used by McKenzie et al., the OntoSTEP approach
directly translates the STEP schema and its instances to OWL.
The OntoSTEP translation can then be integrated with any
OWL ontologies. They noted that semantic models offer
additional benefits such as reasoning, inference procedures, and
queries on enriched legacy CAD models.
Graves has explored integrating SysML with OWL2 [15].
Graves argues that suitably restricted SysML block diagrams
can be translated into OWL2 and maintain the ability to
represent the detailed information necessary to model a system
design. His work aims at a partial unification of SysML and
OWL that is sufficient for modeling the structure of complex
systems (coverage in Figure 1). Though not intended to be a
direct translation from SysML to OWL, any unification
requires similar information artifacts between the two
languages to be identified and mapped.
European researchers have developed a BPMN (Business
Process Model and Notation) ontology within OWL-DL
(Description Logic) (coverage in Figure 1). BPMN provides
the ability to represent the complex process semantics needed
by technical users while maintaining relatively intuitive to
business users [16]. Foundazione Bruno Kessler formalized
the BPMN ontology in OWL-DL as a means for providing a
terminological description of the language and enabling the
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representation of a BPMN process as a set of individuals and
assertions [17]. Given its robust capabilities, BPMN is
available to various stakeholders and multiple lifecycle stages.
There have been several research initiatives aimed at
developing OWL ontologies from the SCOR (Supply Chain
Operations Reference) model. The SCOR model provides a
unique framework that links business processes, metrics, best
practices and technology features into a unified structure to
support communication amongst supply chain partners and to
improve the effectiveness of supply chain management [18].
Supply chain information requirements inherently address
multiple stages of product lifecycle management. Vegetti et al.
[19], Lu et al. [20], Zdravkovic et al. [21], Sakka et al. [22], and
others, have all worked in developing different ontological
versions of the SCOR model (coverage in Figure 1). The result
of their work has provided extended SCOR coverage into the
later stages of the product lifecycle.
There has been some success in developing data exchange
standards completely within OWL. ISO 15926 [6] is a standard
for data modeling and interoperability support with a Semantic
Web specification. ISO 15926 also provides an upper ontology
and a reference data ontology [23]. It was originally developed
for the Oil and Gas industry (originally as an extension of
STEP efforts, ISO 10303-221), but is generic enough that it can
be used for other types of product information exchange and
integration. Unlike many of the other PLM standards discussed
in this review, ISO 15926 (coverage in Figure 1) is specified in,
not translated to, OWL. This coverage mirrors that of STEP.
Both GEIA-HB-927 [24] and MIMOSA OpenO&M [25]
have readily available ontology-based specifications (coverage
in Figure 1), simplifying the translation process. In fact, the
development of GEIA-HB-927 (or GEIA 927) began with ISO
15926, as the basic building block on which data models from
PAS20542 [26] replaced with ISO 10303-233:2012 , Systems
engineering data representation)[27], ISO 10303-212
(electrotechnical design and automation) [28], and ISO10303-
239 (Product Life Cycle Support, PLCS) were integrated [29].
The primary aim of GEIA 927 is to provide lifecycle coverage
through a top-level integration model and unified schema that
integrates the best available schemas for data representation of
modern complex systems [30]. The work outlined in this paper
in some sense very much echoes some of the goals put forth in
GEIA 927, while also building on them.
As seen in Figure 1, OWL translations of existing
standards provide coverage to a significant portion of the
product lifecycle, and across multiple viewpoints. The next
section discusses specialized OWL representations, developed
from information models or PLM system schemas. These
representations both extend and complement the coverage
shown in Figure 1.
Leveraging Product Models and PLM Systems In addition to the standards discussed in the previous
section, initiatives have been taken to represent product
information using elements of the Semantic Web without
directly translating an existing standard, but instead represent
through product models or schemas.
Patil et al. [31] proposed the Product Semantic
Representation Language, or PSRL, as a means of providing
formal representation of product data semantics throughout the
product’s lifecycle. The language, based in OWL, uses the core
product model (CPM) as a foundation, a product model that is
now embedded in many product models and languages[32].
More recently, a similar, CPM-focused model, was developed
at NIST, the Semantic Product Meta-Model.
The Semantic Product Meta-Model (SPMM) [33] provides
a core product model to support different stakeholder
viewpoints across the product lifecycle and enable multi-view
engineering simulations. The multilevel product-modeling
framework enables stakeholders to define product models and
relate them to physical or simulated instances. Like PSRL, the
meta-model is based on the earlier work with CPM and CPM2
[34]. There are also plans to extend this work with a semantic
version of the Open Assembly Model (OAM) [35]. SPMM,
like PSRL, provides additional granularity to its respective
coverage area (coverage in Figure 2).
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Figure 2. Extended Coverage through product models and PLM.
At Linkoping University, Pop et al. [36] have explored the
use of OWL for representing the Modelica language. Modelica
is an object-oriented, equation-based language for multi-
domain modeling of large, complex, heterogeneous systems
[37]. While the intent of Modelica is to facilitate
communication between software platforms using multi-
domain models, these platforms exist at various stages of a
product lifecycle, mostly in the early phases. Therefore,
Modelica can become a de facto model for representing product
lifecycle information for users. More recent works have
resulted in the development the Modelica MultiBody OWL
ontology [38]. Similar to the CPM works, Modelica
representations (coverage in Figure 2) offer coverage
alternatives.
Though not built natively in an ontology language, the
Siemens PLM XML schema [39] is an XML representation
openly available for download. While the schema is not in
OWL, the explicit XML tags provide a solid foundation for the
future conversion. The open availability of the schema
highlights the sense of awareness that steps need to be taken to
make product lifecycle information more transparent.
As shown in Figure 2, the representations discussed in this
section complement the coverage of Figure 1. They offer
specializations and alternatives at different stages of the
lifecycle.
Product Lifecycle Ontologies The works discussed in this section have independently
leveraged ontologies to develop product representations for
various applications. Many of these works partially leverage
various existing languages and models. These works again
complement the works discussed in the previous two sections.
Kiritsis et al. [40] [41] have explored the Semantic Web as
a means for “Closed-loop” PLM systems. The FP6 IP 507100
PROMISE project [42] addresses the development of smart or
“intelligent” products using advanced sensors, processing, and
reasoning. Their work leverages several product standards
including those associated with ISO 10303-239, as well as
MIMOSA and ISO 15926. They have identified key
information concepts within these standards that they then use
in the development of an ontology model for PLM. They have
described their work as “the first efforts towards ontology-
based semantic standards for product lifecycle management and
associated knowledge management and sharing.” Further work
by the group [43] discusses the initial efforts and motives for
converting existing PLM models into ontologies and OWL
[44]. They developed an ontology model of the Product Data
and Knowledge Management Semantic Object Model (SOM).
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Figure 3. Enhanced Lifecycle coverage through independent efforts.
Researchers at CRAN (Centre de Recherche en
Automatique de Nancy) [45] [46] have worked towards the
development of the Product Ontology. While they do not
attempt direct translations of existing standards, they advocate
ontology techniques as a means for representing and preserving
product information. They propose a product ontology as a
“common model” for embedding and preserving essential
product information along a lifecycle while minimizing the loss
of semantics. Unlike the “top down” approach discussed in
this paper, their “bottom up” approach identifies key
information through available technical data. While this work
does not live in OWL, its ontology foundations allow for
relatively straightforward translations. This work leverages
both ISO 10303 and IEC 62264 standards. The coverage map
in Figure 3 shows how these efforts complement those
discussed in the previous two sections.
A team from the National Science Foundation’s (NSF)
Center for e-Design at the University of Massachusetts has
developed a comprehensive set of OWL ontologies that cover
several stages of the product lifecycle. The E-Design
Framework was developed to provide a conceptual framework
for representing product knowledge, focusing mostly on early
design stages [47]. The framework consists of multiple
modular ontologies, including ontologies for conceptual design
[48], design analysis[49], and design optimization [50]. While
this work does not leverage existing standards, it does leverage
various sources including publications and other software
representations.
Lee et al. [51] proposed an ontology-based knowledge
framework with three product knowledge types and four layers,
or levels of abstraction. The three knowledge types are axioms,
knowledge maps, and specialized knowledge for a domain. The
four layers consist of a product context model, a product-
specific model, a product-planning model, and a product-
manufacturing model. They developed a system to help
knowledge engineers create, edit, infer, and visualize product
knowledge.
Each of the efforts discussed in this section replicate, to
some extent, representations available through accepted
standards and models. However, each effort offers its own
unique approach. This uniqueness, though providing
alternatives to information representation and exchange, can
also complicate information synthesis.
TOWARDS A PRODUCT LIFECYCLE UPPER-TIERED ONTOLOGY
In [52], Mostefai et al. discuss ontologies as a means to
integrate product information throughout the lifecycle. Their
approach exploits the idea of “common knowledge concepts”
shared across lifecycle stages. In their paper, they discussed
the challenge of abstracting information with semantics across
lifecycle stages. They find that, at the price of some
comprehension, ontologies can be used to support common
semantics shared by different lifecycle phases. The following
sections discuss a similar approach, but one rooted in existing
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Product Ontology
E-Design
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works, and elaborate on the need to preserve information
quality when exchanging information across the lifecycle.
The concept behind an upper ontology is to provide a
common understanding/ reference for distributed domains. The
most well-known upper ontologies, such as CYC [53] and
SUMO [54], are meant to serve as a means for developing
large-scale ontologies through domain-specific ones. One of
the main challenges faced by upper ontologies is the need for
peer acceptance. They depend on others to both align with
them and/or contribute to them to expand their domains, which,
in general, have shown to create significant challenges. An
upper-tiered ontology based on the works discussed above
would streamline these challenges by developing on pre-
existing product information paradigms and translations.
An upper-tiered ontology to support product lifecycle
management would provide a common platform for mapping
and integrating standard-based information models. In essence,
it would provide a core model for facilitating information
exchange and promoting transparency across a lifecycle.
However, the applications of such an ontology are constrained
by the amount and quality of the information they are able to
support.
To address the quality of information, we now discuss the
notion of information quality, or IQ [55-57]. To address IQ,
here we will adopt the definition derived by Ying and
Zhanming [55]. Table 1 shows the four classifications of
information quality, and the related dimensions.
Table 1. IQ classification and dimensions from Ying and
Zhanming [55].
Classification Dimensions
Syntactic
Conformability
Integrity
Timeliness
Semantic
Complete
Concise
Accuracy
Currency
Pragmatic
Applicability
Clarity
Value
Interactivity
Physical
Accessibility
Security
Maintainability
Speed
For the remainder of this section we will focus on IQ as it
pertains to the Syntactic, Semantic, and Pragmatic
classifications. We address some of the specific challenges
related to attaining information transparency while preserving
information quality. We discuss the importance of maintaining
the quality of information through translations. We discuss
how multiple translations may influence the extent to which
transparency can be achieved.
Preserving Quality of Information
To preserve syntactics and pragmatics, the information
exchange capabilities of any upper-tiered ontology must be
focused, with strict boundaries defined. Core concepts should
be identified based on the primary directives of each
information paradigm, and the resulting overlapping concepts
when different representations are integrated across the
lifecycle. Because an upper-tiered ontology leverages
information paradigms from all stages of the product lifecycle,
these core concepts may not often directly translate in terms of
level of detail, granularity of information, and intent. For
instance, at the early stages a primary directive may be based
on performance requirements, while later stages may focus on
manufacturing or even shipping requirements.
Many of the information paradigms discussed were
developed to represent information artifacts at a particular
lifecycle stage, and not necessarily information from other
lifecycle stages. As a result, issues may arise when accessing
information using translated representations at different
lifecycle stages. These issues may include the inability to
represent the information as initially intended or even loss of
information. Information must maintain some granularity
across translations, so when necessary only the applicable
information is accessed and pragmatics are preserved. This
highlights the need for a very structured, adaptable language as
an intermediate, and addresses why many have chosen to use
ontologies, specifically the Semantic Web.
Ontology as an interlingua can facilitate information
preservation well because semantics between overlapping and
related concepts can be formally specified and therefore
retained. Unlike interlingua developed using syntactical
languages like XML Schemas, developers have the ability to
choose to merge overlapping concepts into a single concept.
This merging leaves some semantics ambiguity to ensure that
the information is more mappable and better preserved between
information translations (while sacrificing semantics).
Preserving Semantics
Different languages use their own syntax and semantics.
By altering the semantics and syntax the information is
represented in, the meaning may also be altered [58]. It is
important to understand that the expressivity of a language can
influence how information is represented and interpreted.
Because of this, an important part of translating between
languages is preserving semantics.
Because we are discussing the use of an interlingua
between many different information paradigms across a
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product’s lifecycle, preserving semantics is essential. However,
it is also a significant challenge. Many of the languages
discussed here are founded on different platforms, often
impeding interoperability. Meaning is often lost in
transformations between these languages, even without an
intermediate language.
The preservation of semantics requires that certain
information about source or target representations be captured
and made accessible during the translation process. Translation
mechanisms should be able to preserve knowledge such as
information about naming, namespaces, structure, granularity
(element vs. attribute), ordering, or even value representation.
The expressiveness of the interlingua used in translation will
directly affect how well semantics are preserved.
OWL is based on Description Logic (DL), which is a
subset of First Order Logic (FOL). Many of the information
paradigms discussed earlier, such as the EXPRESS modeling
language, are more powerful than OWL in terms of
expressiveness. However, OWL does have many advantages. It
has a flexible data structure, as all information is broken down
into triples representation. Its data types are based on the
internationally accepted XML standard allowing it to carry
information in multiple formats and locales. OWL operates
under the open world assumption, which is useful when
addressing potential unknowns during lifecycle integration. For
instance, the open world allows conflicting, yet translatable,
information to pass through OWL intermediary without
creating conflicts. These abilities, and the expressiveness of
DL, have shown to provide ample means for preserving
semantics in translations. Ultimately, however, the preservation
of semantics will depend heavily on the extent to which they
were preserved during existing translations.
SUSTAINABILITY IMPLICATIONS
The previous section discussed the challenges of
maintaining the quality of information as it passes through
different representations across the lifecycle. The extent to
which quality is preserved directly affects the applications in
which any knowledge can be used. This section addresses the
role of information transparency in the context of sustainability
applications.
PLM systems are still in their infant stages as far as
realizing potential sustainability evaluations they can provide
[59]. Designing for sustainability means the entire product
lifecycle should be taken into consideration. However, the
heterogeneity of sustainability-related product information is a
result of the fundamental differences between many of the
stages, such as manufacturing, use, and disposal. In addition,
sustainable implications may come from many directions,
including product design, process design, or supply chain. To
measure sustainability impact as a totality, the impacts resulting
from each stage must be independently evaluated and
subsequently made available for upstream decision-making.
This requires information transparency and a meaningful
formal representation of product data semantics throughout the
product’s lifecycle [31].
By leveraging existing ontological works, an upper tiered
ontology can synthesize a general product structure for
providing information traceability across a lifecycle. In the
context of sustainability, material information becomes of
particular interest [60]. We believe the synthesis of material
information can facilitate the development of a material “meta-
model,” [61] in essence, a “model of material models.” As the
proposed upper ontology would comprise of multiple different
modeling paradigms across the product lifecycle, by identifying
only the properties associated with the transition of material
information across the lifecycle, a meta-model can essentially
be created for material information (Figure 4).
Figure 4. Synthesized material model.
A “material meta-model” could act as a guide for
identifying how material information will be represented when
exchanged through the product lifecycle. By referencing a
“material meta-model” when entering material information at
different stages of the product lifecycle, one could gain insight
into information availability as it propagates through different
lifecycle stages. Such insight during data entry could lead to
more robust material information, and as a result improved
decision making when considering sustainability.
In [57], Ameta et al. find that, in general, there is room to
improve IQ for sustainability. However, they find that the IQ
insufficiencies mostly relate to insufficient support for
sustainability-specific metrics and uncertainty. As such, the IQ
of our upper-tiered ontology approach parallels what is
currently available for sustainability IQ across the lifecycle.
The upper-tiered ontology approach, however, offers a unique
advantage as it can be expanded as new standards are adopted
and developed. Such an ontology can provide a foundation for
extending lifecycle information with sustainability-specific
information, such as that available in standards. This is shown
by D’Alessio et al.[62] when mapping sustainability standards
to product information, and again by Eddy et al.[63] as a means
to incorporate sustainability information into early design time.
SUMMARY
Product lifecycle management is an important aspect of
today’s industry, as it serves to facilitate information exchange
and management between most, if not all, stages of a product’s
existence. Synthesizing representations of product information
across the lifecycle, by creating a lifecycle-stage independent
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public release; distribution is unlimited.
9
platform, can provide transparent access to information for both
upstream and downstream applications.
In this paper, we reviewed previous and ongoing efforts
using ontologies as a means to support information integration
and interoperability throughout the lifecycle of a product. We
discuss the development of an upper-tiered ontology to further
synthesize product information across the lifecycle. We
discussed the extent to which the quality of the information
should be preserved when translating information across the
lifecycle. Finally, we discuss how our proposed approach could
be leveraged in the development of a material meta-model to
support lifecycle thinking in terms of sustainable impact.
ACKNOWLEDGEMENTS
The authors would like to thank Marko Vujasinovic for his
technical contributions to this work. We would also like to
acknowledge help rendered by Kevin Lyons, Sharon
Kemmerer, Al Jones, and Mala Ramaiah.
DISCLAIMER
Certain commercial products may have been identified in
this paper. These products were used only for demonstration
purposes. This use does not imply approval or endorsement by
NIST, nor does it imply that these products are necessarily the
best for the purpose.
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