17
Knowledge-intensive collaborative design modeling and support Part I: Review, distributed models and framework Xuan F. Zha a, * , H. Du b a Nanyang Technological University and Institute of Manufacturing Technology, Singapore b Nanyang Technological University, Singapore Received 17 December 2002; accepted 27 April 2005 Abstract Contemporary design problems embody significant levels of complexity, which make it unlikely that a single designer can work alone on a complex design problem. Knowledge-intensive collaboration has emerged as a promising discipline for dealing with the modeling and decision- making processes in distributed design systems. In this paper, we first review previous work and current research status of knowledge-intensive collaborative design. Then, we develop knowledge-intensive distributed design models and a framework for collaborative design modeling and decision support. Based on the component-based modeling/design approach, a unified design-with-modules scheme is proposed to model the distributed network-centric design processes, and a web-based knowledge-intensive distributed module modeling and evaluation framework (KS- DMME) is developed. The KS-DMME framework intends to link distributed, heterogeneous knowledge-based design models and tools and assist designers in evaluating design alternatives, visualizing trade-offs, finding optimal solutions, and making decisions on the web. It also enables designers to build integrated design models using both the local and distributed resources and cooperate by exchanging services. Built on it, the client (browser)/knowledge server architecture allows product design models to be published and connected over the WWW to form an integrated intelligent models/modules network. Finally, as an illustration, a simple design problem model for modular micro-robotic systems design is built upon the KS-DMME framework. Published by Elsevier B.V. Keywords: World-wide web; Network-centric design; Design-with-modules; Knowledge servers; Distributed module modeling and evaluation (DMME); Integrated design and analysis; Collaborative design; Design support system 1. Introduction The need for an infrastructure based upon distributed computing, information and communication technology (i.e., a cyber-infrastructure) becomes increasing paramount as we progress closer to a knowledge economy. A recent report of US National Science Foundation (NSF) indicates that such a cyber- infrastructure (CI) will play a pivotal role in supporting and shaping future predicative product realization systems and processes. Design process is a knowledge-intensive and collaborative task. The knowledge-intensive support in a CI becomes critical in the design process and has been recognized as a key solution toward future competitive advantages in product development. Integrated design requires skills of many designers/users and experts; each participant creates models and tools to provide information or simulation services to other participants given appropriate input information. It is the goal that the network of participants exchanging services forms a concurrent model for integrated design. Contemporary design problems embody significant levels of complexity, which make it unlikely that a single designer can work alone on a design problem. The continuing growth of knowledge and supporting information and the ever-increasing complexity of design problems have led to the increasing specialization. Current computer tools for product design are generally stand-alone applications. How- ever, design activities may involve many participants from different disciplines and require a team of designers and engineers with different aspects of knowledge and experience to work together. Thus, there is a need to support and coordinate www.elsevier.com/locate/compind Computers in Industry 57 (2006) 39–55 DOI of original article: 10.1016/j.compind.2005.04.006. * Corresponding author. Present address: National Institute of Standards and Technology, Gaithersburg, MD 20899-8263, USA. E-mail address: [email protected] (X.F. Zha). 0166-3615/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.compind.2005.04.007

Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

Embed Size (px)

Citation preview

Page 1: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

Knowledge-intensive collaborative design modeling and support

Part I: Review, distributed models and framework

Xuan F. Zha a,*, H. Du b

aNanyang Technological University and Institute of Manufacturing Technology, SingaporebNanyang Technological University, Singapore

Received 17 December 2002; accepted 27 April 2005

Abstract

Contemporary design problems embody significant levels of complexity, which make it unlikely that a single designer can work alone on a

complex design problem. Knowledge-intensive collaboration has emerged as a promising discipline for dealing with the modeling and decision-

making processes in distributed design systems. In this paper, we first review previous work and current research status of knowledge-intensive

collaborative design. Then, we develop knowledge-intensive distributed design models and a framework for collaborative design modeling and

decision support. Based on the component-based modeling/design approach, a unified design-with-modules scheme is proposed to model the

distributed network-centric design processes, and a web-based knowledge-intensive distributed module modeling and evaluation framework (KS-

DMME) is developed. The KS-DMME framework intends to link distributed, heterogeneous knowledge-based design models and tools and assist

designers in evaluating design alternatives, visualizing trade-offs, finding optimal solutions, and making decisions on the web. It also enables

designers to build integrated design models using both the local and distributed resources and cooperate by exchanging services. Built on it, the

client (browser)/knowledge server architecture allows product design models to be published and connected over the WWW to form an integrated

intelligent models/modules network. Finally, as an illustration, a simple design problem model for modular micro-robotic systems design is built

upon the KS-DMME framework.

Published by Elsevier B.V.

Keywords: World-wide web; Network-centric design; Design-with-modules; Knowledge servers; Distributed module modeling and evaluation (DMME);

Integrated design and analysis; Collaborative design; Design support system

www.elsevier.com/locate/compind

Computers in Industry 57 (2006) 39–55

1. Introduction

The need for an infrastructure based upon distributed

computing, information and communication technology (i.e., a

cyber-infrastructure) becomes increasing paramount as we

progress closer to a knowledge economy. A recent report of US

National Science Foundation (NSF) indicates that such a cyber-

infrastructure (CI) will play a pivotal role in supporting and

shaping future predicative product realization systems and

processes.

Design process is a knowledge-intensive and collaborative

task. The knowledge-intensive support in a CI becomes critical

DOI of original article: 10.1016/j.compind.2005.04.006.

* Corresponding author. Present address: National Institute of Standards and

Technology, Gaithersburg, MD 20899-8263, USA.

E-mail address: [email protected] (X.F. Zha).

0166-3615/$ – see front matter. Published by Elsevier B.V.

doi:10.1016/j.compind.2005.04.007

in the design process and has been recognized as a key solution

toward future competitive advantages in product development.

Integrated design requires skills of many designers/users and

experts; each participant creates models and tools to provide

information or simulation services to other participants given

appropriate input information. It is the goal that the network of

participants exchanging services forms a concurrent model for

integrated design. Contemporary design problems embody

significant levels of complexity, which make it unlikely that a

single designer can work alone on a design problem. The

continuing growth of knowledge and supporting information

and the ever-increasing complexity of design problems have led

to the increasing specialization. Current computer tools for

product design are generally stand-alone applications. How-

ever, design activities may involve many participants from

different disciplines and require a team of designers and

engineers with different aspects of knowledge and experience

towork together. Thus, there is a need to support and coordinate

Page 2: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5540

highly distributed and decentralized modeling activities in

CAD systems. Wide-area networks and the internet-based

WWW allow developers to provide remote web-based design

servers, and CAD systems running on these servers can support

a large-scale group of users who communicate over the

network. As such, user interfaces based on the web protocols

provide access to the remote web-based design servers, and

users do not need special hardware or software to consult these

services with appropriate web browsers. The advantages of

using WWWas a cyber-infrastructure to build a design system

are three-folds. First, web browsers can provide a nice human-

interface for a design system, because they can display various

media including hypertext, moving images, sounds, and three-

dimensional (3D) graphics. They can also handle interactive

operations of the media, such as manipulation with a mouse of a

3D object described in VRML. Second, hypertext transfer

protocol (HTTP) can be a standard communication protocol for

design systems, since terminals connected to the Internet can be

accessed from any web site via the protocol. Third, it becomes

possible to use various hardware and software resources

distributed over the Internet together to accomplish a single

mission. Therefore, it is natural to consider developing design

support systems on the web.

This research aims to develop a methodology and a web-

based knowledge-intensive framework for collaborative design

modeling and support. The presentation of the proposed

methodology and framework is divided into two parts:

distributed modeling techniques and framework (Part I) and

system implementation and application (Part II). Part I is the

subject matter of this paper and Part II is covered in the

following paper of this issue. This paper focuses on the review

of previous work and current research status and the

development of distributed models and a web-based knowl-

edge-intensive framework for collaborative design modeling

and decision support. It intends to build up an information

cyber-infrastructure to facilitate the rapid construction of

integrated intelligent design models. A unified design-with-

module scheme is proposed for representing and modeling the

distributed network-centric design process.

The organization of this paper is as follows: Section 2

reviews network-based design tools and the distributed module

modeling and evaluation for the design process. Sections 3 and

4 give a brief of the proposed approach and discuss DMME for

the design process. Section 5 proposes an integrated knowledge

representation scheme. Section 6 provides a description of the

KS-DMME framework. The knowledge server architecture for

supporting different types of collaborative design activities in a

distributed design environment is described. As an illustration,

a simple design problem model is built upon the KS-DMME

framework. Section 7 summarizes the paper.

2. Literature review

There are many research efforts working on enabling

technologies or infrastructure to assist designers in the

computer-aided network-centric design environment [76–

79,81,83–87,90]. Some of them intend to help designers

collaborate or coordinate by sharing product information and

manufacturing services through formal or informal interactions

[82,88,4]. Others propose frameworks that manage conflicts

between design constraints and assist designers in making

decisions [80,89,51]. In this section, we will present a research

review in more detail on the work related to knowledge-

intensive collaborative design.

2.1. Design knowledge modeling and design support

There is a large body of knowledge that designers call upon

and use during the design process to match the ever-increasing

complexity of design problems. Generally, design knowledge

can be classified into two categories [1]: product knowledge

and design process knowledge. Product knowledge has been

fairly studied and a number of modeling techniques have been

developed. Most of them are tailored to specific products or

specific aspects of the design activities. For example, geometric

modeling is used mainly for supporting detailed design, while

knowledge modeling is working for supporting conceptual

designs. Based on these techniques, a design repository project

at NIST attempts to model three fundamental facets of an

artifact representation [2,3]: the physical layout of the artifact

(form), an indication of the overall effect that the artifact

creates (function), and a causal account of the operation of the

artifact (behavior). The recent NIST research effort towards the

development of the basic foundations of the next generation of

CAD systems suggested a core representation for design

information called the NIST core product model (CPM) [4] and

a set of derived models defined as extensions of the CPM (e.g.

[5,88]). The NIST core product model has been developed to

unify and integrate product or assembly information. The CPM

provides a base-level product model that is: not tied to any

vendor software; open; non-proprietary; expandable; indepen-

dent of any one product development process; capable of

capturing the engineering context that is most commonly

shared in product development activities. The core model

focuses on artifact representation including function, form,

behavior, material, physical and functional decompositions,

and relationships among these concepts. The entity-relationship

data model influences the model heavily; accordingly, it

consists of two sets of classes, called object and relationship,

equivalent to the UML class and association class, respectively.

Design process knowledge can be described in two levels:

design activities and design rationale. The importance of

representation for design rationale has been recognized but it is

a more complex issue that extends beyond artifact function. The

design structure matrix (DSM) has been used for modeling

design process (activities) and some related research efforts

have been conducted. For example, a web-based prototype

system for modeling the product development process using a

multi-tiered DSM is developed at MIT. However, few research

endeavors have been found on design rationale [6,7].

In terms of representation scenarios, design knowledge can

also be categorized into off-line and on-line knowledge [91].

The former refers to existing knowledge representation,

including design knowledge in handbook and design ‘‘know-

Page 3: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 41

how’’, etc.; the latter refers to the new design knowledge

created in the course of design activities by designers

themselves. For the off-line knowledge, there are two

representation approaches. One is to highly abstract and

categorize existing knowledge including experiences into a

series of design principles, rationales and constraints. TRIZ is a

good instance of this approach. The other is to represent a

collection of design knowledge into a certain case for

description. Case-based design is an example of this approach

[8]. The current research focus is on the computerization of the

design knowledge representation. For instance, researchers at

the Engineering Design Centre at Lancaster University

established a unique knowledge representation methodology

and knowledge base vocabulary based on the theory of

domains, design principles and computer modeling. They have

developed a radical software tool for engineering knowledge

management. The tool provides an engineering system designer

with the capability to search a knowledge base of past solutions,

and other known technologies to explored viable alternatives

for product design. The on-line knowledge representation is to

capture the dynamic design knowledge in a certain format for

design re-use and archive. A few research efforts have been

found in this area. Blessing [9] proposes the process-based

support system (PROSUS) based on a model of the design

process rather than the product. It uses design matrix to

represent the design process as a structured set of issues and

activities. Together with the common product data model

(CPDM), PROSUS supports the capture of all outputs of the

design activity. The results show that it seems to be a promising

approach. Another focal research area is using ontologies for

product representation (e.g. [10]).

Research suggests, therefore, that there is a need to provide

computer support that will supply clear and complete design

knowledge and also facilitate designer intervention and

customization during the decision-making activities in the

design process [11]. Rodgers et al. [12] describes a design

support system WebCADET using distributed Web-based AI

tools. The system can provide support for designers when

searching for design knowledge. WebCADET uses the ‘‘AI as

text’’ approach, where KBSs can be seen as a medium to

facilitate the communication of design knowledge between

designers.

2.2. Collaborative design frameworks

Computer-supported cooperative design (CSCD) is to use

computer technology to assist people working in the

engineering design field. King [13] considers that the

fundamental issue of CSCD focuses on the computerization

to establish a concept-sharing and seamless coordination

among engineering design participants for concept formation.

While many individuals and organizations may provide

services so that an integrated product model can be constructed,

it is not likely that each participant will disclose the full details

or structure of their proprietary models and data. Providing a

means for encapsulating expert knowledge or know-how is

essential. An object-oriented approach provides a framework

for such knowledge encapsulation. Furthermore, an object-

oriented architecture is also highly suited to a distributed

computing environment [14]. Distributed object technology,

such as Common Request Broker Architecture (CORBA) [15]

and DCOM/ActiveX [16] can be used to address the issue of

distributed computing environment. A computer platform and

language-independent interface definition allows software

applications to communicate with each other provided a

neutral interface has been agreed upon. For example, the

WWW has gained its popularity and momentum through a

platform-independent protocol (i.e., HTTP) and a language-

independent scheme (i.e, HTML) for presenting information.

The software component technology is adopted to build a

collaborative CAD system. Wang and co-workers [17–19]

proposed an approach to collaborative design based on the

software component mechanism. A component is a reusable

application whose data and methods are exposed, and can be

accessed and operated by other applications. The component

agent method is used as the basic system module. However,

their work is only focused on the data, not knowledge.

Distributed design systems might have two distinct forms:

distributed designers with access to centralized resources

(Fig. 1a) or distributed designers with distributed resources

(e.g., engineering models, databases, software applications,

etc.) (Fig. 1b and c). ‘‘Decentralized’’ means that coordinations

between design participants and models are not centrally

modeled or controlled (analogous to the WWW). This is

important because centrally controlling the interactions of all

distributed resources may restrict system growth and flexibility.

If there was a centralized control over theWWW for linking the

hyper documents, it could not evolve so rapidly. Pahng et al.

[20–22] developed a web-based framework for collaborative

design modeling and decision support, based on the distributed

object modeling and evaluation (DOME). The DOME frame-

work asserts that multi-disciplinary problems are decomposed

into modular sub-problems. Modularity divides the overall

complexity and distributes knowledge and responsibility

amongst designers. It also facilitates the reuse of modeling

elements. Thus, DOME allows designers to define mathema-

tical models or modules and integrate or interconnect them to

form large system models. In DOME, a multiple attribute

decision method is used to capture preferences and evaluate

design alternatives from different viewpoints. The resultant

service-exchange network forms an integrated concurrent

system model if module services are connected, as shown in

Fig. 1b. There are other programs (e.g. [23,24], architectures or

frameworks) for network-centric collaborative design. These

include the centralized multi-user system architecture (Fig. 1a),

e.g., the blackboard-based DICE [25,84], DIS [26], data and

model exchange system (Fig. 1c), e.g., SHARE [14], EDN [27],

NIIIP [28] and multi-agent-based distributed system architec-

tures (e.g. [29]). The comparisons between the DOME

architecture and these architectures are described as follows

[21]:

(1) F

or the DOME architecture, as shown in Fig. 1b, the distinct

characteristic is that when module services are connected,

Page 4: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5542

Fig. 1. Interactions between modules exchange services.

the resultant service exchange network forms an integrated

concurrent system model.

(2) F

or the centralized multi-user system, multiple users have

access to a centralized main system, which stores and

manages information such as product design models, design

information and design history. Although it powerful, a

central system is less suited for loose and flexible

collaborations as it is not an open environment and does

not allow for true knowledge encapsulation. However, such

architecture could be supported within a module of a larger

DOME network.

(3) T

he data and model exchange system architecture tends to

provide an ‘‘over-the-wall’’ sequential interaction between

designers and models. When a designer receives a model or

data from another designer, he/she works on the design and

sends the result of design modification to others. Therefore,

this architecture is not intended to provide concurrent

system modeling functionality.

(4) M

ulti-agent-based architectures are more appropriate

for loosely coupled environments where mutual interac-

tions between objects are not well defined. In the DOME

architecture, the interactions between sub-problems

are explicitly defined through design negotiation so

that a communicating object paradigm is appropriate.

However, within the DOME, agents are useful when

designers are not certain about what modules can provide

the service they require. Agents could locate appropriate

modules.

2.3. Collaborative design information and support systems

There are many information and distributed computing

systems that have been built to support the collaborative design

modeling and decision-making process. According to their

different purposes and/or focuses, these systems can be

generally grouped into the following categories [30]:

(1) C

ollaborative product data/information management sys-

tems for engineers to timely obtain the necessary product

data and knowledge [31–37].

(2) N

etwork-based collaborative design systems, which can be

further divided into web-independent [21,22,38–44], and

web-dependent [35,36,45–49] systems.

(3) P

rocess-centered collaborative design and workflow man-

agement systems [50–54].

(4) C

onflict detection, management and resolution systems for

collaborative design [53,55–57].

(5) F

lexibility and security focused collaborative design system

[58].

(6) I

nteroperability approaches in heterogeneous collaborative

design systems [38,59,60].

Since most of them are developed for the needs of

collaborative design, current systems can assist designers in

one way or another in collaboration. Below, we provide more

details for some typical or notable systems and comparisons

between them. Of those collaborative design information and

Page 5: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 43

support systems, notable ones include SHARE [14], NEXT-

LINK [61], DIS [26], DICE [25,84], EDN [26], MADEFast

[23], NIIIP [28], RaDEO [24], etc. Other typical proof-of-

concept systems include NetFeature [62], CooperativeAR-

CADE [63], WebSPIFF [64], CSCW-FeatureM [41], TOBACO

[65], CyberCAD [66], etc. There are also a number of

commercial software packages purporting to perform many of

the services that the system described supplies. Examples

include: Alibre (http://www.alibre.com), Co-Create (http://

www.cocreate.com), NexPrise (http://www.netprise.com),

CollabCAD (http://www.collabcad.com), etc. Modern product

data management (PDM) systems such as PTC Windchill and

UGS Teamcenter (iMAN) also provide some of the function-

ality described for collaborative design and development.

Table 1 gives an overview of some typical collaborative design

systems.

2.4. Summary of review and observations

From the above review, the requirements for network-based

collaborative design tools are highlighted as follows:

(1) A

Tabl

Ove

Syst

DIC

Alib

Co-C

(O

NetP

Win

Team

Coll

Cyb

NetF

Web

Coo

A

CSC

TOB

DOM

common framework enabling designers to capture, store

and retrieve knowledge efficiently and effectively through-

out the design process.

(2) C

omputer support that supplies clear and complete design

knowledge, and also facilitates designer intervention and

customization during the decision-making activities in the

design process.

(3) M

ediation of the information flow between participants and

support for a heterogeneous computing environment.

e 1

rview of some typical collaborative design systems

em Architecture Platform Communicatio

E Centralized Motif Compatible

Unix Workstation

Specialized M

Collaboration

re Centralized PC Windows TCP/IP, Direct

Cable Modem,

reate

neSpace.Net)

Centralized PC Windows Microsoft.NET

rise Centralized PC Windows Specialized M

Collaboration

dchill (PTC) Centralized PC Windows NT,

Unix Workstation

Java RMI, XM

center (UGS) Centralized PC Windows NT,

Unix Workstation

Microsoft.NET

SOAP, XML,

JSP Communi

aCAD Centralized PC Windows, Intel Linux Java RMI, Jav

erCAD Centralized Platform independent Point-Point Jav

eature Centralized PC Windows CORBA Comm

SPIFF Centralized PC Windows Java 3D Socke

perative

RCADE

Decentralized SGI, HP, SUN Workstation Specialized W

W-FeatureM Decentralized Motif Compatible

Unix Workstation

CORBA Comm

ACO Decentralized Solaris IRIX, Windows NT CORBA Comm

E Decentralized Platform independent CORBA Comm

(4) A

n

essag

(OO

X D

TI

essag

Hub

L

, J2E

catio

a3D

a R

uni

t

orkst

uni

uni

uni

n open environment so that models from new participants

can be added, allowing the design model and tools to evolve

with the design problem.

(5) A

means or mechanism for encapsulating expert knowledge

or know-how using an object-oriented approach, comparing

design solution alternatives and supporting the decision-

making process.

(6) E

mbodiment of a decentralized architecture that the

coordination between design participants and models is

not centrally modeled or controlled.

Further, the current status of research in knowledge-

intensive collaborative design modeling and support can be

summarized as follows. Although there have been efforts that

address design knowledge modeling and support, they all have

deficiencies in their representational capability. They only

focus on product knowledge modeling but none of them address

design process knowledge modeling as well as the relationship

between the product knowledge and the design process

knowledge. None yet provide explicit representation of the

design rationale as part of a comprehensive product knowledge

representation. It is also desirable to integrate a comprehensive

product model with a more formal representation of behavior.

Thus, they could not be directly used for design support. These

issues reinforce the need for consensus among product

development software developers and users regarding the

content and form for more comprehensive representation of

design knowledge.

The overview of the current collaborative design frame-

works and systems shows that they can assist designers in one

way or another in collaboration and their functionality can

Collaboration

granularity

Network

burden

Modeling technique

ing &

DMS)

Fine Heavy 2D/3D parametric

surface/solid modeling

SL,

or faster

Coarse Light 2D/3D, parametric

solid modeling

Fine Light 2D/3D Solid Modeling

ing/ Coarse Light Solid modeling

Coarse Light Feature-based modeling, etc.

E UDDI,

n Service

Coarse Light Parametric,

Feature-based modeling

Coarse Heavy Surface modeling,

solid modeling

MI Fine Heavy Solid modeling

cation Service Coarse Heavy Feature-based modeling

Fine Heavy Feature-based modeling

ation Network Coarse Light Solid modeling

cation Service Fine Light Feature-based modeling

cation Service Fine Light Solid/Feature modeling

cation Service Coarse Light Module modeling

Page 6: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5544

eliminate a large amount of work during the design process

such as data/information sharing and exchange. However, they

do not provide a structured and formalized framework for

modeling the characteristics of multidisciplinary and multi-

objective design problems. They suffer one or more of the

following drawbacks: a heavy dependence on the experience

and knowledge of designers/users; no built-in ways for

selection or decision-making knowledge representation; no

efficient mechanisms to share, exchange, and reuse utilize the

given knowledge and guide the designer/user; difficult to

modify and not easy to extend/update/maintain knowledge

base, and no efficient mechanism to provide advisory services

and explain the results and what-ifs. Thus, they are unable to

effectively and efficiently support and coordinate highly

distributed and decentralized collaborative design and model-

ing activities. As a result, an intelligent collaborative design

tool that can provide efficient coordination and intelligent

decision-making mechanisms for designers is still needed. To

overcome the above drawbacks suffered in current systems and

improve current methods for collaborative design, this research

proposes a knowledge-intensive design modeling and support

scheme and develops a web-based advisory system to help

designers/users collaborate in making rapid and more

intelligent decisions during the design process.

3. Knowledge-intensive collaborative design: an

overview of the proposed approach

As reviewed above, distributed design systems have two

distinct forms [20–22]: distributed designers with access to

centralized resources, or distributed designers with distributed

resources (e.g., engineering models, databases, software

applications, etc.). This work focuses on the latter architecture.

Thus, the coordination between design participants and models

is not centrally modeled or controlled (analogous to the

WWW). The motivation and vision presented in this research

share some similar themes with Pahng et al. [20–22] but

emphasize knowledge-level or knowledge-intensive design

modeling, decision-making support, and search/optimization

and navigation. Even more, the proposed approach decomposes

design tasks into several ‘‘design components’’ or ‘‘modules’’

that can be developed separately by collaborative product

developers. We investigate the application to modular product

development problems, particularly the design of electro-

mechanical artifacts. The proposed distributed module-based

approach follows the component technology and distributed

object technology, which is called distributed module modeling

and evaluation (DMME) framework. In the component

technology, a component is a reusable application whose data

and methods are exposed and can be accessed and operated by

other applications. Thus, the DMME framework asserts that

multidisciplinary problems are decomposed into modular sub-

problems. Modularity divides overall complexity and dis-

tributes knowledge and responsibility amongst designers,

which facilitates the reuse of modeling elements. It also

facilitates the reuse of modeling elements. Therefore,

distributed modules allow designers to define mathematical

models or modules and integrate or interconnect them to form

large system models. In DMME, a multiple attribute decision

method is used to capture preferences and evaluate design

alternatives from different viewpoints. The resultant service-

exchange network forms an integrated concurrent systemmodel.

More details about the proposed approach are discussed below.

4. Distributed module modeling and evaluation for

design process

In this section, based on the design with modules scheme, a

distributed module modeling and evaluation framework is

developed for modeling the network-centric design process.

4.1. Design with modules scheme

Over half of industrial products have sub-systems or

components or modules as a part of their basic designs. The

modular design concept has been widely used in product design

for flexibility, rapid responsiveness, ease of maintenance and

rapid deployment. As a matter of fact, during the design

process, information processing is inherently model-based

because design object is structural in type. Therefore, an object

orientation scheme is employed so that both calculating and

reasoning work in design can be carried out. The integrated

design object model is, in fact, an attempt to set up a

knowledge-intensive framework in such a way that it becomes

possible to process various types of knowledge in a top-down

design process [29,67–69]. Object-oriented programming

technique allows designers to look at a design problem as a

collection of objects/modules or sub-problems linked together

by rules. Thus, it provides designers with an expressive power

to represent complex problems or information in an effective

manner. If designers can break a design problem into the form

of well defined operable chunks with their own self-containing

information, which are interrelated through a series of rules and

constraints, then the problem is a well object-oriented

programming application and conveniently to be solved (Pahng

et al. [20–22]).

The central design process inherent in design-with-modules

scheme could be represented as the architecture as shown in

Fig. 2 with five main types of objects involved: namely, design

models (S), design objects (O), design algorithms (A), functions

(requirements and constraints) (FRC), and the evaluation

schema (E) [70]. Object operators can express the relationship

between these objects: inheritance, import and message

passing. The architecture in Fig. 2 shows how the particular

instance of a design model, Sk1, is obtained from the design

algorithm, evaluation schema, requirements, constraints and

the design model object. For the pure formulation design or

creative design, a new design model object, S, is defined that

describes the form of the model. A specific instance, Sk1, of thisdesign model can then be created. For pure parametric design

then the design model object S has already been defined and the

design process therefore only involves the determination of a

specific instance, Sk1, of the design model. Note that additional

objects can be defined within the overall architecture.

Page 7: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 45

Fig. 3. Module definition and embedded model.

Fig. 2. Overall architecture of design with modules (objects).

4.2. Module-based design and modularization process

A design model is created using object attribute variables

and relations between them. Modules are variable containers.

Variables can be grouped together into modules according to

logical, functional or physical component-based decomposi-

tions [71]. This grouping process is normally a bottom-up

decomposition as elementary entities are aggregated to build

more complex entities. A designer may also prefer to use a top-

down approach by defining high-level modules first and then

detailing the low-level variables and relations. Using both

bottom-up and top-down techniques, integrated design models

are built by interconnecting modules corresponding to different

sub-problems. Modules interact by service calls. A module

might request information from other modules to perform

internal computations and then provide the results as services to

other modules. A product module can be seen as a collection of

components that cover one or more sub-functions. It can be

defined by a combination of bill-of-materials (BOM) and

technical drawings. Thus, physically, a modular product system

is a collection of interchangeable modules (e.g., link and joint

units in modular robots) that can be assembled into many

different types and configurations of products. Unlike a

conventional industrial product that is designed for general

tasks or purposes, a modular product system has the advantage

of providing feasible product configurations for specific

customer or task requirements. As a result, the modular design

of products provides the ability to achieve product variety

through the combination and standardization of modules.

On the other hand, a partitioning or module is a building

block capable of performing calculations, reasoning and

exchanging information through service calls invoked by its

user. Defined as shown in Fig. 3, a module represents

knowledge related to different aspects of the design in the

form of variables and relations. The variables contained in the

module are represented as interconnected circles. The directed

arcs imply dependency. The outputs and inputs to the module

constitute the interface of the module. Modules can be

interconnected through interfaces. The calculations internal

to a module constitute its embedded model. Customer-created

computer programs and third-party applications, such as

domain specific analysis tools or CAD systems, can also be

embedded into a module. Modules interact with each other by

exchanging information and services, reacting to each other’s

changes for an integrated systemmodel. Modules can distribute

over the network and collectively form a distributed model for a

collaborative, multidisciplinary and concurrent design evalua-

tion. In this work, modules whose dependencies are to be

analyzed are tasks and parameters contained in design tasks,

and dependencies are constraints among parameters. Details

Page 8: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5546

Fig. 4. Motor modules.

about modules can be found in [21]. For illustration, an

example module of motor is shown in Fig. 4. The module motor

can be described with IDL (interface description language).

Decomposing a design problem into modules and defining

how modules are related to one another creates the model of the

design problem. This is actually a partitioning or modulariza-

tion process, as shown in Fig. 5. The modularization process is

fulfilled through the following steps [72]:

(1) R

equirement analysis and modeling is carried out both from

task or customer and designer viewpoints using design

Fig. 5. Modular design and mod

functions deployment technique and Hatley/Pirbhai tech-

nique [73,74]. A function–function interaction matrix is

generated.

(2) T

he combination of heuristic and quantitative clustering

algorithms is used to modularize the product architecture,

and a modularity matrix is constructed.

(3) A

ll modules in the product are identified through the

modularity matrix, and the types of all these modules can be

further identified according to the module classifications

(e.g., robot modules: link modules, actuator joint modules,

passive joint modules, etc.).

ularization process.

Page 9: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 47

(4) F

unctional modules are mapped to structural modules using

the function–structure interaction matrix. Module attribute

parameters or features can represent its structure.

(5) H

ierarchical building blocks (modules) are used to

represent the product architecture from both the functional

and the structural perspectives.

(6) O

ptimization algorithms (e.g., genetic algorithm, simulated

annealing algorithm) are used to optimize product

architecture to achieve one main objective. Other design

objectives are transformed into constraints for modules and

their attributes as well as their assemblies or configurations.

In addition, the cost or profit models can also be built as

system constraints.

Fig. 6. Distributed modules.

4.3. Distributed design modeling and evaluation

In a distributed design environment, each group of designers

can define their own modules, loading them into their local

work area and eventually connecting them to the other parts of

the design problem through appropriate networked interfaces

[20].

Fig. 6a shows a DMME model involving two designers and

three modules. Designer 1 defines modules A and B while

Designer 2 defines module C. Two domains communicate

through an Internet connection. Once the whole problem is

loaded and interconnected, each group of designers typically

has write access to local parts of the model, i.e., they can exert

decisions within their local range of influence, and read access

to relevant aspects of remote parts of the model. This allows

them to see the remote effects of their local decisions. Fig. 6b

Fig. 7. Module network modelin

shows that both Designers 1 and 2 might see the complete

problem, but with different access privileges. Designer 1 can

see modules A and B as local and module C as remote.

Conversely, module C is local to Designer 2. The remote part

seen by Designer 2 could showmodules A and B or just a single

distributed object (AB) if Designer 1 restricts their visibility/

access.

The modeling and implementation layers in DMME

[21] are illustrated in Fig. 7. At the modeling layer the

g and implementation [20].

Page 10: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5548

designer defines the problem in terms of modules and

interactions among modules (Fig. 7a). In this example, an

implementation is provided only for the local modules A and

B, while module C is remote. The designer provides location

constraints for compatible modules that may be used as C.

The unseen implementation layer (Fig. 7b) is created to

provide the functionalities described in the modeling layer.

This layer locates remote modules. The remote modules

must be distributed objects capable of communicating

via a standard communication protocol. A distributed

interface is wrapped around the group of standard modules

(A and B in Fig. 7) to allow the local and distributed

modules to communicate with each other. The external

interface of this distributed module now offers service

calls to and from the remote module. Thus, the distributed

module is considered as a separate application that is capable

of providing services upon request in a design problem

model.

The interactions between distributed modules can be

achieved by publishing and subscribing services. The term

publish refers to making the services of one’s local model

visible together designers. The term subscribe refers to

making use of published services. Such design problem

models are mixed variable, where independent parameters

within modules are set and catalog selections might be used to

substitute entire modules. Design solutions can be assessed

and compared with each other using the decision-making

module embedded in the DMME framework.

Fig. 8. Product, design pr

5. Design knowledge modeling and integrated

knowledge representation scheme

In this section, a design knowledge modeling approach is

proposed. As stated before, both on-line and off-line design

knowledge representations are dealt with in this research, but

more attention is paid to the design process knowledge, as

shown in Fig. 8. Since the design knowledge is very extensive,

the focus is only on product and some selected activities in the

design process. A systematic methodology and the relevant

technologies are developed for knowledge modeling, i.e.,

capturing, representing, organizing and managing knowledge

in the design process.

Design knowledge is very domain specific, which comes

from a variety of sources, such as corporate libraries, textbooks/

handbooks, scientific literature, on-line resource, and in

particular, the experience of individual designers. It is stored

in different forms including documents, drawings, electronic

means and memories of human beings. Therefore, design

knowledge must be classified into different categories and

represented in appropriate ways accordingly. Through an

analysis of the design process, design knowledge is abstracted

and classified into four categories, off-line and on-line, product

knowledge and process knowledge. Each category of knowl-

edge is represented in different ways from multiple views.

Product knowledge includes all information needed for

designing product throughout the whole design process such

as product specifications, concepts, structure and geometry. In

ocess and knowledge.

Page 11: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 49

view of this, a complete product information model is used,

which consists of customer requirements, design specifications,

modules (function—behavior, structure and geometry), module

interfaces, etc., as follows:

Product definition

requirements

specifications

artifacts

features

functions-behaviors-forms

performance objectives and constraints

relationships

design rationale

Product variety

assembly structure

module details

family parameters

product variants

In what follows, we concentrate on an integrated knowledge

representation scheme related to the design process. It deals

mainly with declarative representation, production rules and

object-oriented concepts. Procedural representation using

conventional languages such as C was not emphasized.

Integrating knowledge in its multiple forms, multiple levels

and multiple functions can fulfill design processes and

activities, especially for a more complex type. The integration

is very challenging, as the overall effect may be greater than the

sum of its parts. The integrated knowledge can solve problems,

which cannot be attained by the individual knowledge alone.

Based on a combination of elements of semantic relationships

with the object-oriented data model, a multi-level hierarchical

representation schema (meta-level, physical level, geometric

level) is adopted to represent the modular design process

knowledge in different design stages at different levels. To

effectively manage and utilize design knowledge, a generalized

design knowledge matrix is proposed for organizing design

knowledge. All tasks in the design process are listed in column

while all information and design knowledge is categorized in

rows. The contents of design knowledge for each task are

recorded in the corresponding cells of the design knowledge

matrix with appropriate representations.

More specifically, the object-oriented knowledge represen-

tation is based on a mixed representation method and object-

oriented programming (OOP) techniques, and allows designers

to look at the design problem as a collection of objects or sub-

problems linked together by rules. Thus, it provides the

designers with an expressive power to represent complex

problems or information in an effective manner. The basic

structure of this representation is described as a module. The

class of an object and its instances are described by the module

structure. An object-oriented module is composed of four types

of slots, which are the attribute slot, relation slot, method slot

and rule slot as follows:

(1) T

he attribute slots are used for describing the static

attributes (variables) of design object.

(2) T

he relation slot is used for describing the static relations

among objects. With the help of the relation slot and

according to the relation of classification, the design

object can be described as a hierarchical structure. Its

class and sub-classes can share the knowledge in super

class. The messages that control the design process can be

sent among all instances of objects. In addition, if needed,

other kind of relation slots can be defined, such as the

resolution, position and assembly, etc. These slots create

the foundation for describing a graph in design. The

hierarchical structure of object-oriented knowledge

representation is formed.

(3) T

he method slot is used for storing the methods of design,

sending messages and performing procedural control and

numerical calculation.

(4) T

he rule slot is used for storing sets of production rules. The

production rules can be classified according to the

constraints/differences among objects being treated

and stored respectively in rule slots in the form of slot

value.

Thus, the integrated knowledge representation scheme

realizes the advantages of both object-oriented representation

and rule-based representation.

6. Web-based knowledge-intensive collaborative design

framework

In this section, a web knowledge server-based distributed

module modeling and evaluation (KS-DMME) is proposed for

distributed design modeling.

6.1. Knowledge-based systems as knowledge servers

The widespread use of the Internet and WWW provides an

opportunity for making expert systems widely available. By

implementing knowledge-based systems (KBS) as knowledge

servers that perform their tasks remotely, developers can

publish expertise on the Web. Technologies and infrastruc-

tures that make this approach feasible are emerging.

Simultaneously, the interest in artificial intelligence support

for network navigation services is growing. Wide-area

networks and the internet-based WWW allow developers to

provide intelligent knowledge servers [75]. Knowledge-based

expert systems running on servers can support a large-scale

group of users who communicate with the system over the

network. In this approach, user interfaces based on web

protocols provide access to the knowledge servers, and

users do not need special hardware or software to consult

these services with appropriate web browsers. To make

knowledge servers available, developers must distribute the

software front ends that allow users to communicate with the

servers. The remaining parts are actually concerning how

KBS technology can be used to assist designer in navigating

design knowledge present on the WWW, drawing on an

alternative view of KBS, and making decisions in design

process.

Page 12: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5550

6.2. KS-DMME framework architecture

As discussed above, KS-DMME adopts the design with

modules, modules network and knowledge server paradigms,

which are techniques to utilize the connectivity of knowledge-

based systems provided by the internet to increase the size of

the user base whilst minimizing distribution and maintenance

overheads. Therefore, modules under the KS-DMME frame-

work are connected together so that they can exchange services

to form large integrated knowledge models. The module

structure of KS-DMME leads itself to be a client (browser)/

knowledge server oriented architecture using the distributed

object technology. Fig. 9 shows the main system components of

the proposed client (browser) knowledge server architecture.

Each of these components interacts with one another using a

communication protocol, CORBA, so that it is not required to

maintain the elements on a single machine. As a gateway for

providing services, the interface of a system component

invokes the necessary actions to provide requested services. To

request a service, a system component must have an interface

pointer to the desired interface [21,22]. The resultant service-

exchange network forms an integrated concurrent system

model when module services are connected. The distinct

characteristics of the KS-DMME architecture are described as

follows:

(1) K

S-DMME is well suited for loose and flexible collabora-

tions as it is an open environment and allows for true

knowledge encapsulation. The centralized multi-user

system architecture could be supported within a module

Fig. 9. (a) Client-knowledge server architecture and

so that multiple users have the ability to access to the

centralized module.

(2) K

S-DMME is intended to provide concurrent system

modeling functionality, i.e., concurrent interactions

between designers and models. When a designer receives

a model or data from another designer, he/she works on the

design and sends the result of design modification to others.

(3) I

n KS-DMME, the interactions between sub-problems are

explicitly defined through design negotiation so that a

communicating object paradigm is appropriate. Within the

KS-DMME, agents are useful when designers are not

certain about what modules can provide the service they

require. Agents could locate appropriate modules [30].

6.3. Module interactions for exchanging services

The KS-DMME architecture allows designers or experts to

publish and subscribe design modeling and decision support

services on the. These services operate when information is

received from other clients or knowledge servers. When

module services are connected, the resultant service exchange

network forms a concurrent integrated system model. Any

service requests in the module network can invoke a chain of

service requests if needed to provide correct information. When

a design alternative is evaluated, the local module asks for the

services of subscribed modules. If the subscribed modules

themselves need services from other modules in order to

provide the request services, they again request those services

from their own network to remote models. Thus, the service

requests are propagated through the connected modules.

(b) main components for KS-DMME.

Page 13: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 51

Fig. 10. Service exchanges between distributed modules.

However, the complete system may not be visible to any given

modules. Since modules can only interact through services, it is

possible for a module or local model to encapsulate its internal

modules and hide intellectual property (IP) if desired. Before a

designer publishes, they can assign access privileges for their

services. Three levels of modules access have been identified:

owner, builder and user. The owner is the original creator of the

module and has access to all the services defined in the model

and control over their publication. The builder can see the

internal details of a model the owner chooses to make public

and can add new modules. However, they cannot destroy

Fig. 11. Simple distributed design model with two modules and a remo

modules created by the owner or other builders. The users can

only subscribe the published services.

The KS-DMME framework provides the methods and

interfaces needed for the interaction with other modules in a

networked environment. These interactions are graphically

depicted in Fig. 10. When Designer B makes a change, the

service corresponding to the request from Designer A will

reflect this change. The enumerated request shows the sequence

for obtaining the service needed by Designer A. The light gray

module seen by Designer A is the remote module published by

Designer B. For more details, please refer to [20–22].

te module: (a) modules A and B and (b and c) remote module AB.

Page 14: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5552

Table 2

The description of the distributed model

Local modules Remote module

module A ( module Remote_AB (

Variable A1( ) URL: 159.69.1.19//IP address

Variable A2( ) Receive a2

Constraint a1 RuleSet AB (Rule AB1:

Constraint b2 IF (. . .)

EmbeddedModel calculateA2

(A2 = f1(a1,b2))

THEN (. . .)

RuleSet A (Rule A1: )

IF (a1=) )

THEN (. . .) module C (

) Variable C1( )

) Variable C2( )

module B ( Constraint c1

Variable B1( ) Constraint a2

Variable B2( ) EmbeddedModel calculateC2

(C2 = f3(a2,c1))

Constraint b1 RuleSet C (Rule C1:

EmbeddedModel

calculateB2 (B2 = f2(b1))

IF (. . .)

RuleSet B (Rule B1: THEN (. . .)

IF (. . .) )

THEN (. . .) )

)

)

Design: Two-module design ( Design: design with a

remote module (

Module A Module AB

Module B Module C

) )

6.4. Design modules network formulation

As discussed above, the modularization process decomposes

a design problem into modules and defines how modules are

related to one another. The relationships among modules

specify how outputs of a module are connected to the inputs of

other modules. The embedded model of a module produces

outputs using its internal design resources as well as inputs

from other modules. Fig. 11 illustrates a simple distributed

module network model used for a design process. The variables

of the model are governed by a set of equations and/or rules. As

depicted in Fig. 11, the interface connections between variables

in different modules (e.g., modules A and B) can be established

interactively or defined explicitly using a modified model

definition language (MMDL) (Phang et al. [20–22,15]). The

embedded models defined with the variable declaration can also

be created separately and linked to the model definition using

keywords. Modules A and B are local to the problem. Using the

remote module AB, a new design model (ABC) can be created.

As such, the problem model is available for use as a distributed

module with the outward appearance in Fig. 11a. These

distributed modules allow users to utilize variables and their

dependencies such as module A (A1, A2, a1, !b2), module B

(B1, B2, b1, b2 !), module C (C1, C2, c1,!a2) and module AB

(A1, A2, B1, B2, a1, a2 !, b1, b2). Fig. 11b illustrates the model

from the viewpoint of the ABC designer. Module C is local to

the designer. Fig. 11c illustrates the true integrated model

created when the remote module AB and the local module C are

connected. The problem model ABC is thus created, which

Fig. 12. Module network configuration under KS-DMME framework.

Page 15: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 53

requires additional information such as the distributed module’s

name and IP address. The description of the distributed model

can be illustrated in Table 2. It is shown that the relations

between modules do not need to be changed even if the

embedded model of a remote module (i.e., module AB) is

changed. This flexibility enables a designer to define a model

independently from the actual location (i.e., local or remote) of

embedded models. When the designer utilizes the remote

module AB in conjunction with the local module C, the

resulting integrated model forms a distributed computing

system comprised of two autonomous computing elements.

Fig. 12 illustrates the configuration process for distributed

modules using the system components, including the Internet

and web resources. The embedded model of the module AB in

design problem model ABC contains module connector that

manages the design information exchange with the distributed

design module AB.

7. Summary and conclusions

In this paper, we presented a design-with-modules scheme,

distributed modeling and a web-based knowledge-intensive

framework (KS-DMME) for collaborative design modeling and

support. Because of the heterogeneous structure, product design

and simulation requires different grades of abstraction and needs

the cooperation and collaboration of different disciplines and

resources. The developed framework can provide distributed

designers with an intelligent tool to collaboratively build

integrated system models. The advantage of the demonstrated

module concept consists in the flexibility of the structure and the

reduction of costly software support by integrating design

tools and simulators. Large problems are decomposed into

sub-problems with modules; models or other software applica-

tions are encapsulated in modules. A module can provide

information services through its interface, and the network of

modules exchanging services form a concurrent intelligent

design model. Therefore, the behavior of complex systems and

the interactions of components/modules can be analyzed and

optimized during the design process, resulting in shorter design

cycles.

As the knowledge server-based framework was built to

provide the module network architecture for integrating

intelligent modeling services available on the network, it can

accommodate top-down and bottom-up approaches in the

context of both the traditional sequential design processes and

the concurrent intelligent design. In the module network,

design resources, models, data and activities are not

centralized nor concentrated in one location, but distributed

among many companies, designers, or design participants

working together over the web. The module network

architecture is extended to a network-centric environment,

focusing on the knowledge-intensive and collaborative design

modeling and support. Design modules and modules network

are created by fully implementing the locally defined modules

and subscribing to the services of the remote modules. Design

modules and modules network are created. In the module

network architecture, when modules services are connected,

the resultant service exchange network creates an integrated

concurrent intelligent system model or module network

that invoke a chain of service requests if needed to

provide correct information. The following paper (Part II)

will present in detail the implementation and application of

the web-based knowledge-intensive collaborative design

support system.

Compared to existing or ongoing research efforts, the

framework presented in this paper differs in its focus to create a

concurrent intelligent design modeling scheme that handles the

different variable types and knowledge needed in engineering

design, integrate multiple objective evaluation and optimiza-

tion with design models, and provide an object-oriented design

methodology to facilitate the intelligent integration of design

models and their utilization in an open and distributed

intelligent design environment.

Acknowledgements and disclaimer

The bulk of the work was done while the first author was

with Nanyang Technological University and Singapore

Institute of Manufacturing Technology (SIMTech), Singapore.

The authors would like to express their gratitude to the

anonymous reviewers of this paper for their valuable comments

and suggestions.

References

[1] M. Stokes, Managing Engineering Knowledge: MOKA Methodology for

Knowledge Based Engineering Applications, MOKA Consortium, Lon-

don, 2001.

[2] S. Szykman, R.D. Sriram, W. Regli, The role of knowledge in next-

generation product development systems, ASME Journal of Computing

and Information Science in Engineering 1 (1) (2001) 3–11.

[3] S. Szykman, Architecture and implementation of a design repository

system, in: Proceedings of ASME DETC2002, 2002, Paper No.

DETC2002/CIE-34463.

[4] S.J. Fenves, A core product model for representing design information,

NISTIR 6736, NIST, Gaithersburg, MD, 2001.

[5] X.F. Zha, R.D. Sriram, Feature-based component model for design of

embedded system, in: B. Gopalakrishnan (Ed.), Intelligent Systems in

Design and Manufacturing, Proceedings of SPIE, vol. 5605, SPIE, Bel-

lingham, WA, vol. V, 2004, pp. 226–237.

[6] F. Pena-Mora, R.D. Sriram, R. Logcher, SHARED DRIMS: SHARED

design recommendation and intent management system, in: Enabling

Technologies: Infrastructure for Collaborative Enterprises, IEEE Press,

1993, pp. 213–221.

[7] F. Pena-Mora, R.D. Sriram, R. Logcher, Conflict mitigation system for

collaborative engineering, AI EDAM—Special Issue of Concurrent Engi-

neering 9 (2) (1995) 101–123.

[8] W.H.Wood III, A.M. Agogino, Case based conceptual design information

server for concurrent engineering, Computer-Aided Design 8 (5) (1996)

361–369.

[9] L.T.M. Blessing, A process-based approach to computer supported engi-

neering design, Ph.D. Thesis, University of Twente, 1993.

[10] L. Patil, D. Dutta, R.D. Sriram, Ontology-based exchange of product data

semantics, IEEE Transactions on Automation Science and Engineering 2

(3) (2005) 213–225.

[11] A.M. Madni, The role of human factors in expert systems design and

acceptance, Human Factors 30 (4) (1988) 395–414.

Page 16: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du /Computers in Industry 57 (2006) 39–5554

[12] P.A. Rodgers, A.P. Huxor, N.H.M. Caldwell, Design support using dis-

tributed web-based AI tools, Research in Engineering Design 11 (1999)

31–44.

[13] S. King, Co-Design: a Process of Design Participation, Van Nostrand

Reinhold, New York, 1989.

[14] G. Toye, M.R. Cutkosky, J.M. Tenenbaum, J. Glicksman, SHARE: a

methodology and environment for collaborative product development, in:

Proceedings of Second Workshop on Enabling Technologies: Infrastruc-

ture for Collaborative Enterprises, Morgantown, West Virginia, 1993), pp.

33–47.

[15] J. Siegel, CORBA: Fundamentals and Programming, OMG, 1996.

[16] D. Chappel, Understanding ActiveX and OLE, Microsoft Press, Red-

mond, WA, 1996.

[17] F.J. Wang, J.J. Mills, V. Devarajan, A conceptual approach managing

design resource, Computers in Industry 47 (2002) 169–183.

[18] M. Rosenman, F.J. Wang, CADOM: a component agent-based design-

oriented model for collaborative design, Research in Engineering Design

11 (1999) 193–205.

[19] M. Rosenman, F.J. Wang, A component agent-based open CAD system for

collaborative design, Automation in Construction 10 (2001) 383–397.

[20] F. Pahng, N. Senin, D.R. Wallace, Modeling an evaluation of product

design problems in a distributed design environment, in: Proceedings of

ASME DETC, Sacramento, CA, 1997 (CD ROM).

[21] F. Pahng, N. Senin, D.R.Wallace, Distribution modeling and evaluation of

product design problems, Computer-Aided Design 30 (6) (1998) 411–423.

[22] F. Pahng, S.H. Bae, D.R. Wallace, Web-based collaborative design

modeling and decision support, in: Proceedings of DETC’98, Atlanta,

Georgia, USA, 1998.

[23] MADEFast, http://madefast.stanford.edu/, 1999.

[24] RaDEO, http://elib.cme.nist.gov/radeo/, 1998.

[25] R.D. Sriram, R. Logcher, The MITDICE project, IEEE Computer (1993)

64–65.

[26] P.I. Bliznakov, J.J. Shah, D.K. Jeon, S.D. Urban, Design information

system infrastructure to collaborative design in a large organization, in:

Proceedings of ASME DETC’95, vol. 1, Boston, MA, 1995), pp. 1–8.

[27] J.W. Lewis, K.J. Singh, Electronic design notebooks (EDN): technical

issues, in: Proceedings of Concurrent Engineering: a Global Perspective,

McLean, VA, 1995), pp. 431–436.

[28] NIIIP, http://www.niiip.org/, 1999.

[29] X.F. Zha, Knowledge intensive methodology for intelligent design and

planning of assemblies, Ph.D. Thesis, Nanyang Technological University,

Republic of Singapore, 1999.

[30] Y.L. Li, X.Y. Shao, P.G. Li, Q. Liu, Design and implementation of a

process-oriented intelligent collaborative product design system, Com-

puter in Industry 53 (2004) 205–229.

[31] Y.M. Chen, M.W. Liang, Design and implementation of a collaborative

engineering information system for allied concurrent engineering,

International Journal of Computer Integrated Manufacturing 13 (1)

(2000) 11–30.

[32] M. Hardwick, D.L. Spooner, T. Rando, K.C. Morris, Sharing manufactur-

ing information in virtual enterprise, Communications of the ACM 39 (2)

(1996) 46–54.

[33] Y.P. Zhang, C. Zhang, H.P. Wang, An Internet based STEP data exchange

framework for virtual enterprises, Computers in Industry 41 (1) (2000)

51–63.

[34] M. Rezayat, The enterprise-web portal for life-cycle support, Computer-

Aided Design 32 (2) (2000) 85–96.

[35] Y. Kim, S.-H. Kang, S.-H. Lee, S.B. Yoo, A distributed, open intelligent

product data management system, International Journal of Computer

Integrated manufacturing 14 (2) (2001) 224–235.

[36] Y. Kim, Y. Choi, S.B. Yoo, Brokering and 3D collaborative viewing of

mechanical part models on the web, International Journal of Computer

Integrated Manufacturing 14 (1) (2001) 28–40.

[37] P.-Y. Chao, Y.-C. Wang, A data exchange framework for networked CAD/

CAM, Computers in Industry 44 (2) (2001) 131–140.

[38] S. Abrahamson, D. Wallace, N. Senin, P. Sfereo, Integrated design

in a service marketplace, Computer-Aided Design 32 (2) (2000)

97–107.

[39] Y.-C. Kao, G.C.I. Lin, Development of a collaborative CAD/CAM system,

Robotics and Computer-Integrated Manufacturing 14 (1) (1998) 55–68.

[40] N. Shyamsundar, R. Gadh, Internet-based collaborative product design

with assembly features and virtual design spaces, Computer-Aided Design

33 (9) (2001) 637–651.

[41] A. Stork, U.V. Lukas, R. Schults, Enhancing a commercial 3D CAD

system by CSCW functionality for enabling cooperative modelling via

WAN, in: Proceedings of 1998 ASME Design Engineering Technical

Conference, Atlanta, GA, 13–16 September, 1998 (CD).

[42] G.W. Tan, C.C. Hayes, M. Shaw, An intelligent-agent framework for

concurrent product design and planning, IEEE Transactions on Engineer-

ing Management 43 (3) (1996) 297–306.

[43] J. Sun, Y.F. Zhang, A.Y.C. Nee, A distributed multi-agent environment for

product design and manufacturing planning, International Journal of

Production Research 39 (4) (2001) 625–645.

[44] R.I. Whitfield, A.H.B. Duffy, G. Coates, W. Hills, Distributed design

coordination, Research in Engineering Design 13 (2002) 243–252.

[45] F.-C.F. Wang, P.K. Wright, Web-based CAD tools for a networked

manufacturing service, in: Proceedings of 1998 ASME Design Engineer-

ing Technical Conferences, Atlanta, GA, 13–16 September, 1998 (CD).

[46] C.S. Smith, P.K. Wright, CyberCut: a World Wide Web based design-to-

fabrication tool, Journal of Manufacturing Systems 15 (6) (1996) 432–

442.

[47] C.-Y. Kim, S.-H. Kang, N. Kim, P. O’Grady, Internet-based concurrent

engineering: an interactive 3D system with markup, in: Proceedings of

ASME 18th Computers in Engineering Conference, Atlanta, GA, 13–16

September, 1998 (CD).

[48] K. Cheng, P.Y. Pan, D.K. Harrison, Web-based design and manufacturing

support systems: implementation perspectives, International Journal of

Computer Integrated Manufacturing 14 (1) (2001) 14–27.

[49] U. Roy, S.S. Kodkani, Collaborative product conceptualization tool using

web technology, Computers in Industry 41 (2) (2000) 195–209.

[50] G.Q. Huang, K.L. Mak, Web-integrated manufacturing: recent develop-

ments and emerging issues, International Journal of Computer Integrated

Manufacturing 14 (1) (2001) 3–13.

[51] S.C.Y. Lu, J. Cai, W. Burkett, F. Udwadia, A methodology for collabora-

tive design process and conflict analysis, Annals of CIRP 49 (1) (2000)

69–73.

[52] G.Q. Huang, J. Huang, K.L. Mak, Agent-based workflow management in

collaborative product development on the Internet, Computer-Aided

Design 32 (29) (2000) 133–144.

[53] Y. Jin, L. Zhao, A. Raghunath, ActiveProcess: a process-driven and agent-

based approach to supporting collaborative engineering, in: Proceedings

1999 ASME Design Engineering Technical Conferences, Las Vegas, NV,

12–16 September, 1999 (CD).

[54] H. Kim, J.Y. Lee, S.-B. Han, Process centric distributed collaborative

design based on the web, in: Proceedings of ASME 19th Computers in

Engineering Conference, Las Vegas, NV, 12–15 September, 1999 (CD).

[55] S.T.C. Wong, Coping with conflict in cooperative knowledge based

systems, IEEE Transactions on Systems, Man and Cybernetics—Part

A. Systems and Humans 27 (1) (1997) 57–72.

[56] M. Klein, Supporting conflict resolution in cooperative design systems,

IEEE Transactions on Systems, Man and Cybernetics 21 (6) (1991) 1379–

1390.

[57] M.P. Case, S.C.-Y. Lu, Discourse model for collaborative design, Com-

puter-Aided Design 28 (5) (1996) 333–345.

[58] L.M. Camarinha-Matos, H. Afsarmanesh, A.L. Osorio, Flexibility and

safety in a web-based infrastructure for virtual enterprises, International

Journal of Computer Integrated Manufacturing 14 (1) (2001) 66–82.

[59] G. Zhao, J. Deng,W. Shen, CLOVER: an agent-based approach to systems

interoperability in cooperative design systems, Computers in Industry 45

(3) (2001) 261–276.

[60] C.S. Han, J.C. Kunz, K.H. Law, An internet-based distributed service

architecture, in: Proceedings of the 1999 ASME Design Engineering

Technical Conferences, Las Vegas, NV, 12–15 September, 1999 (CD).

[61] C. Petrie, M. Cutkosky, H. Park, Design space navigation as a collabora-

tive aid, in: Proceedings of Third International Conference on Artificial

Intelligence in Design, Lausanne, Switzerland, 1994.

Page 17: Knowledge-intensive collaborative design modeling and support: Part I: Review, distributed models and framework

X.F. Zha, H. Du / Computers in Industry 57 (2006) 39–55 55

[62] J.Y. Lee, H. Kim, S.B. Han, Network-centric feature-based modeling, in:

Proceedings of Pacific Graphics’99, Seoul, Korea, 1999), pp. 280–289.

[63] A. Stork, U. Jansnoch, A collaborative engineering environment, in:

Proceedings of the TeamCAD’97 Workshop on Collaborative Design,

Atlanta, 1997), pp. 25–33.

[64] R. Bidarra, E. van den Berg, W.F. Bronsvoort, Collaborative modeling

with features, in: Proceedings of 2001 ASME Design Engineering Tech-

nical Conferences, Pittsburgh, 2001 (Paper No. DETC2001/CIE-21286).

[65] U.V. Lukas, Collaborative geometric modeling using CORBA services, in:

Advance Proceedings of the ECSCW’97 Workshop on Object-Oriented

GroupWare Platform (OOGP’97), Lancaster, UK, 1997), pp. 91–92.

[66] F.E.H. Tay, A. Roy, CyberCAD: a collaborative approach in 3D-CAD

technology in a multimedia-supported environment, Computer in Industry

52 (2003) 127–145.

[67] J.S. Gero, Design prototypes: a knowledge representation schema for

design, AI Magazine 11 (4) (1990) 6–36.

[68] J.S. Gero, Knowledge-Based Design Systems, Addison-Wesley, Reading,

MA, 1990.

[69] S.R. Gorti, A. Gupta, G.J. Kim, R.D. Sriram, A. Wong, An object-oriented

representation for product and design process, Computer-Aided Design 30

(7) (1998) 489–501.

[70] P. O’Grady, W.Y. Liang, An object-oriented approach to design with

modules, Iowa Internet Laboratory Technical Report TR98-04, 1998.

[71] N. Senin, D.R. Wallace, N. Borland, M.J. Jakiela, A framework for mixed

parametric and catalog-based design problem modeling and optimization,

MIT CAD Lab-Technical Report 97.02, 1997a.

[72] X.F. Zha, W.F. Lu, Knowledge intensive support for product family

design, Proceedings of ASME, DETC 02/DAC-34098, 2002a.

[73] S. Sivaloganathan, P.T.J. Andrews, T.M.M. Shahin, Design function

deployment: a tutorial introduction, Journal of Engineering Design 12

(1) (2001) 59–74.

[74] G.J. Rushton, A. Zakarian, Development of Modular Vehicle Systems,

Department of Industrial and Manufacturing Systems Engineering, Uni-

versity of Michigan, Dearborn, 2000.

[75] H. Eriksson, Expert systems as knowledge servers, IEEE Expert 14 (3)

(1996) 14–19.

[76] P.I. Bliznakov, Design information framework to support engineering

design process, Ph.D. Dissertation, Arizona State University, 1996.

[77] C.A. Ellis, S.J. Gibbs, G. Rein, Groupware: some issues and experience,

Communication of ACM 34 (1) (1991) 38–58.

[78] M. Klein, Capturing geometry rationale for collaborative design, in:

Proceedings of the 1997 6th IEEE Workshops on Enabling Technologies:

Infrastructure for Collaborative Enterprises, WET-ICE, Cambridge, MA,

USA, 18–20 June, Journal of Engineering and Applied Science 1997) 24–

28.

[79] G. Konduri, A. Chandrakasan, Framework for collaborative and distrib-

uted web-based design, in: Proceedings of Design Automation Confer-

ence, Proceedings of the 1999 36th Annual Design Automation

Conference (DAC), New Orleans, LA, USA, 21–25 June, 1999), pp.

898–903.

[80] A. Kusiak, J. Wang, Dependency analysis in constraint negotiation, IEEE

transactions on systems, Man and Cybernetics 25 (9) (1995) 1301–1313.

[81] S.E. Lander, Issues in multi-agent design systems, IEEE Expert: Intelli-

gent System and Their Application 12 (2) (1997).

[82] J. Owen, STEP—an Introduction, Winchester, 1993.

[83] U. Roy, B. Bharadwaj, S.S. Kodkani, M. Cargian, Product development in

a collaborative design environment, Concurrent Engineering Research and

Applications 5 (4) (1997) 347–365.

[84] R.D. Sriram, Distributed and Integrated Collaborative Engineering

Design, Sarven Publishers, Glenwood, MD 21738, December, 2002.

[85] A.K. Singh, CONSENS—an IT solution for concurrent engineering, in:

Proceedings of Concurrent Engineering: a Global Perspective, McLean,

VA, 1995), pp. 635–644.

[86] N. Senin, N. Borland, D.R. Wallace, Distributed modeling of product

design problems in a collaborative design environment, in: CIRP Inter-

national Design Seminar Proceedings: Multimedia Technologies for

Collaborative Design and Manufacturing, Los Angeles, CA, 8–10 Octo-

ber, 1997.

[87] M.W. Sobolewski,W. Erkes J., CAMnet: architecture and applications, in:

Proceedings of Concurrent Engineering: a Global Perspective, McLean,

VA, 1995), pp. 627–634.

[88] R. Sudarsan, Y.H. Han, S.C. Feng, U. Roy, F. Wang, R.D. Sriram, K.

Lyons, Object-oriented representation of electro-mechanical assemblies

using UML, NISTIR 7057, NIST, Gaithersburg, MD, 2003.

[89] K.-L. Wang, Y. Jin, Managing dependencies for collaborative design, in:

Proceedings of 2000 ASME Design Engineering Technical Conferences

and Computers and Information in Engineering Conference, Baltimore,

MA, 10–13 September, 2000 (CD).

[90] A.W. Westerberg, R. Coyne, D. Cuningham, A. Dutoit, E. Gardner, S.

Konda, S. Levy, I. Monarch, R. Patrick, Y. Reich, E. Subrahmanian, M.

Terk, M. Thomas, Distributed and collaborative computer-aided environ-

ment in process engineering design, in: Proceedings of ISPE, 1995.

[91] X.F. Zha, M.F. Fu, W.F. Lu, S. Ma, C.F. Zhu, Knowledge modeling in

design process-knowledge modeling in product family design for mass

customization, SIMTech Technical Report, MIT/02/031/PDD, Singapore,

2002.

Xuan F. Zha is currently with the Design and

Process Group, Manufacturing System Integration

Division, National Institute of Standards and Tech-

nology (NIST), USA. He is a guest professor of

Shanghai JiaoTong University (China). He received

his PhD from Nanyang Technological University,

Singapore. He is a senior member of IEEE and

SME. His current research interests are in virtual

and collaborative product design and development,

product lifecycle management, knowledge manage-

ment in design, artificial intelligence and soft computing in design and

manufacturing, supply chain management, micro electro mechanical systems

design and simulation, embedded systems, robotics and so forth. Dr. Zha has

authored or coauthored over 100 papers, book chapters and reports in these

areas.

H. Du is currently at School of Mechanical and

Production Engineering, Nanyang Technological

University, Singapore. He obtained his BEng and

MEng from Nanjing University of Aeronautics and

Astronautics, Nanjing, China, in 1983 and 1986,

respectively. He obtained his PhD from Imperial

College of Science, Technology and Medicine, Lon-

don, UK, in 1991. Since then, he has been working at

School of Mechanical and Production Engineering,

Nanyang Technological University, Singapore. His

research interests include micro-electro-mechanical system (MEMS), topology

optimization design, concurrent design as well as smart materials and struc-

tures.