TLMT600Wk6

Proceedingsof.pdf

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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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SCOR

SysML

ISO 15926 BPMN

STEP+

MIMOSA

SysM L

GEIA- 927

PLCS PLCS

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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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SIEMENS PLM

Modelica

SPMM/PSRL

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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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SIEMENS PLM

Modelica

SPMM

Closed Loop PLM

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

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for

public release; distribution is unlimited.

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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