Internet-based intensive product design platform for ... - GRACO

Internet-based intensive product design platform for product design
Shouqin Zhou* ,1 , Kwai-Sang Chin b , Prasad K.D.V. Yarlagadda a
a School of Mech., Mfg., and Medical Eng., Queensland University of Technology, Brisbane, QLD 4001, Australia
b Department of Manufacturing Engineering & Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, People’s Republic of China
Received 20 August 2001; accepted 4 February 2002
Abstract
For overcoming the limitations of traditional computer aided design platform, a framework of the Internet-based intensive product design
platform (IPDP) is proposed. The structure of IPDP and the key issues regarding its implementation are introduced. Further, the mean of
knowledge storage, acquisition, and representation are portrayed in detail. Based on the concept of function driven, connected artificial neural
networks and active server pages techniques, a novel method of knowledge search over the Internet is achieved. Finally, a prototype to
demonstrate the feasibility of the structure and the constructional method of IPDP is developed. q 2003 Elsevier Science B.V. All rights
reserved.
Keywords: Product design; Knowledge engineering; Internet-based; Artificial neural network; Standard for the exchange of product model data
1. Introduction
The quality of the product heavily lies in its design [1].
As increasing concerns over global environmental issues
and rapidly changing economic situations worldwide,
design must exhibit performance, not only in quality and
productivity, but also in life cycle issues including extended
producers’ liabilities [2,3]. This requires that designers and
engineers use various kinds of design knowledge intensively
during product design. As products become more complex
and competition intensifies, it is essential to make the
maximum use of the available knowledge and to deliver that
knowledge in an appropriate form at the right time during
the product development process.
In addition, currently, CAD technology plays a key role
in today’s advanced manufacturing environment [2]. The
designer increasingly depends on various commercial CAD
platforms, such as Pro/E, AutoCAD, and UGII. However,
the knowledge repositories of these commercial CAD
systems are self-contained and closed. When a designer
using a commercial CAD system designs a product, he must
* Corresponding author. Address: Faculty of Built Environment and
Engineering Research Concentration in Manufacturing Systems
Engineering, 2, George Street GPO Box 2434, 4001 Brisbane, Qld,
Australia. Tel.: þ61-7-38642423; fax: þ61-7-38641469.
E-mail addresses: zhoushouqin@263.net (S. Zhou),
s.zhou@qut.edu.au (S. Zhou).
1
Tel.: þ61-7-38642423; fax: þ61-7-38641469.
Knowledge-Based Systems 16 (2003) 7–15
0950-7051/03/$ - see front matter q 2003 Elsevier Science B.V. All rights reserved.
PII: S0950-7051(02)00035-7
www.elsevier.com/locate/knosys
base it on the inside repositories of the commercial CAD. If
a designer wants to add new knowledge, he must expand the
repositories of the commercial CAD system and based on
the special data structure of the CAD system. Therefore,
embodying a user’s knowledge into the repositories of a
commercial CAD system is very difficult. Besides, only
vendors themselves have the option of updating the
repositories of CAD. However, it is impossible for a vendor
of a CAD platform to involve all the various kinds of
knowledge in his CAD system repositories to bring them up
to date.
Since product designer need to utilize an amount of
knowledge coming from various locations, domains and
disciplines, and any single platform could not entirely
posses all kinds of knowledge. Therefore, to meet the need
of designer on knowledge resource during product development,
it is necessary to construct an intensive product
design platform through combining commercial CAD and
outside knowledge repositories. Form this standpoint, the
Internet-based intensive product design platform (IPEP) is
proposed. Based on Internet networks, this platform aims to
form a robust knowledge integrated platform to aid the
product design.
Recently, several studies of Internet-based collaborative
CAD are being conducted, because of the potential of
Internet technology [4], among them the Adaptive,
Collaborative, Open Research Network project (ACORN)
[5]. The aim of the project is to construct a design and

8
manufacturing architecture based on the Internet, with the
World Wide Web (WWW) as the user interface. A design
expert system controlled through the Internet and integrated
with a CAD system visualizes the product shape [6]; a
Concept-Modeler and an expert systems shell with CAD
ability, has also been used. The interface between the expert
system shell and the web was developed by Common Gateway
Interface (CGI) script using Pearl [7]. A distributed design
expert system should use an expert system shell and provide
information related to design and manufacturing through the
Internet and multi-agents. However, the current distributed
design expert system is not based on distributed design
knowledge, but is limited to an expert system shell remotely
operated through the Internet. Until now, there is still a little
research on how to achieve the integration of multidisciplines,
distributive knowledge resources, and product
design platform over the Internet to support product design.
From the author’s viewpoint, and for the sake of quickly
acquiring design knowledge and improving the speed of
product development, a kernel problem is the mechanism of
knowledge acquisition. Another problem is the form
of knowledge representation (i.e. how to achieve the
design knowledge interacting among various design
platforms and directly utilized by them). With the goal of
solving these problems, combining the Internet enabling
technologies with CAD platforms, a scheme, the IPDP, is
proposed. The problems of representation, storage, transference,
search and implementation of knowledge in IPDP are
discussed in detail later. The ultimate objective of this paper is
to present a rational scheme used to integrate the knowledge
resources and design platforms over Internet so as to support
the designer in developing a high quality product quickly and
efficiently.
2. The structure of IPDP
The framework of IPDP is shown as Fig. 1. It is
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15
Fig. 1. Framework of Internet-based intensive product design platform.
composed of three layers: traditional design platform,
middle-ware, and knowledge repositories. The IPDP apart
design platforms (e.g. SolidWorks) from knowledge
repositories, and links them via middle-ware. The design
platforms are independent of knowledge repositories. And
the outside knowledge repositories are distributed in the
servers of knowledge (product) vendors, it is also independent
of the design platform. The design platforms communicate
with knowledge repositories via Internet control, and
the knowledge is translated between them via pre/post
processor.
As Fig. 1 states, obviously, the IPDP is a system with a
Client/Server mechanism. Common repositories are useropened,
and can be expanded by any user according to the
specifications of the knowledge definition. The knowledge
in IPDP repositories is represented with certain standard
(e.g. the standard for the exchange of product model data,
STEP). All product designers may use these repositories.
Furthermore, the designers can also add their knowledge to
the repositories or construct their own repositories according
to the standard. Hence, the structure of repositories in
IPDP is distributed, flat and net-like. The knowledge store
style of the IPDP with the distributed, user-opened and userexpanded
feature breaks through the limitations of the
traditional style of commercial CAD (i.e. the single stiffness
closeness feature).
Considering the Internet networks can integrate distributive
repositories with an opened network style, in IPDP,
the knowledge (outside design platform) acquisition,
organization, construction and utilization all are
implemented through the Internet. All common repositories
of IPDP are constructed and integrated over the Internet. By
this mechanism, product designer can retrieve and download
the knowledge from common repositories of IPDP via
Internet control if the knowledge inside the repository of his
design platforms does not meet what he needs. In IPDP, the
product designer still depends on traditional design platform
(commercial CAD system) to develop product. Intensively,

Table 1
EXPRESS information model of rolling bearing
STEP information model
SCHEMA rolling_bearing_support_schema:
ENTITY bearing:
style: STRING;
static_load: REAL;
dynamic_load: REAL;
weight: REAL;
limited_speed_with_oil: REAL;
limited_speed_with_grease: REAL;
external_diameter: REAL;
inner_diameter: REAL;
width: REAL;
install_damin: REAL;
install_damax: REAL;
install_rasmax: REAL;
other_d2_inner: REAL;
other_d2_external: REAL;
other_rsmin: REAL;
END_ENTITY:
END_SCHEMA:
he can use not only the repositories inside the commercial
CAD system, but also the common outside repositories via
Internet control. Due to the common repositories are
maintained by knowledge (products) providers, they can
quickly be updated and kept up to date. Since knowledge
providers are distributed in all kinds of disciplines and to
various sites, the knowledge in the repositories of the IPDP
can abundantly contain distributive, various discipline
knowledge resources. Consequently, a robust intensive
product design platform is formed.
To achieve this IPDP framework successfully, there are
server all-important issues need to be solved. Firstly, the
common repositories of IPDP are distributed at various
locations, domains and disciplines. And the knowledge
stored in them should be utilized with various design
platforms. So the knowledge must be represented with a
neutral form independent of design platforms, meanwhile, it
should be understood by them via knowledge processor.
Secondly, because the IPDP is constructed over the Internet
and its knowledge repositories is a dynamic league of
knowledge sites, an efficient method of knowledge search
within Internet environment must be provided to assure the
running performance of IPDP. The following content then
will discuss these issues in detail.
3. Knowledge representation
As stated in Section 2, a key issue of the IPDP is that the
knowledge retrieved from the repositories of IPDP should
be directly utilized for various design platforms. So, if the
knowledge is still represented in a traditional handbook with
such things as data, tables and, diagrams, the user must
translate the knowledge into a format that his design
platform can understand. For example, a product designer
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 9
needs a rolling bearing when he designs the structure of a
product with a Solidworks platform. If the product designer
gains the detailed structure of the rolling bearing from a
traditional format such as a handbook, table or diagram, it
cannot be directly understood in a Solidworks platform. As
a result, the designer must draw a rolling bearing with
Solidworks using the rolling bearing information as
represented in traditional handbook form. This indicates
that the traditional handbook representation limits the level
of knowledge utilization and the speed of product
development.
Since the repositories of the IPDP are independent of the
application platform, and the knowledge acquired by the
designer requires that it directly utilize the application
platform, the knowledge representation must be standardized.
The standard for the exchange of product model data
(STEP) [8,9], released by ISO, can implement the
information expression depending on various application
platforms. It is a standard based on the EXPRESS language
[10], which can grow and can be extended to any industry
and will not be outdated as soon as it is published. The
EXPRESS language describes constraints as well as data
structure. The formal rules of the STEP standard will
prevent conflicting interpretations. The knowledge represented
in the STEP standard can felicitously meet the
requirements of the IPDP system on the knowledge
representation form. Since STEP provides a mechanism
for describing product data models and EXPRESS featureneutral
and independent of any application platform.
Besides the traditional form, in this paper, the knowledge
in common repositories of the IPDP is also represented with
EXPRESS language based on the STEP standard. Hence,
the designer can acquire not only the traditional handbook
form but also the STEP information model of knowledge
from repositories of the IPDP. The STEP contains all
information of this knowledge as expressed in traditional
handbook form in data, table and diagram style. When a
designer retrieves knowledge from repositories of the IPDP,
he first downloads the STEP information model of knowledge
and, then employs the STEP pre/post processor,
translating the STEP information into a form understood by
the application platform. By this mean, the knowledge can
be quickly and directly utilized in various product design
platforms.
Taking the rolling bearing as an example, we note in
Table 1 that the representation form of knowledge (rolling
bearing) in the repositories of IPDP is described with
EXPRESS language. This knowledge model then is
translated into a data dictionary model using the EXPRESS
compiler (e.g. ST-Deeloper8.0 [11]). The data dictionary is
a knowledge model that is not populated (e.g. a class of
rolling bearing), meaning that the model attributions are
not endued with exact values. The bearing’s code is in a
binary format, which can be directly read by machine
(for example, after being compiled, the data dictionary
of the information model rolling_bearing_support_schema

10
Table 2
Instanced information model of rolling bearing 6215
Instanced STEP information model
Format ¼ ‘rose_r3.0’
ROSE_OIDS (
(0 ¼ 0 £ 0000000000000000000000000000000000000000)
(1 ¼ 0 £ 01000004D20000387AD825000000B30000000000))
ROSE_DESIGN (RoseDesign
name: bearing6215
root: $
keyword_table: $
name_table: $
schemas: (,1-1 . ListOfRoseDesign
, “rolling_bearing_support_schema” . ))
STEP_OBJECTS (
(,1-0 . bearing6215
style: “general ball bearing with deep groove”
static_load: 66
dynamic_load: 49.5
weight: 1.171
limited_speed_with_oil: 5600
limited_speed_with_grease: 4500
external_diameter: 130
inner_diameter: 75
width: 25
install_damin: 0
install_damax: 0
install_rasmax: 0
other_d2_inner: 0
other_d2_external: 0
other_rsmin: 0))
is rolling_bearing_support_schema.rose). Populating the
data dictionary means to endue an exact value into every
parameter of the information model. Table 2 shows the
instanced information model of the rolling bearing that
populated the 6215 rolling bearing. Once the information
model is instanced, all parameters of the information model
are confirmed. The detailed structural data of the 6215
rolling bearing is shown in Fig. 2. The 6215 STEP
information model, containing all of the information stated
in Fig. 2, is shown in Table 2. It shows that, by contrast with
Fig. 2. Traditional handbook form of rolling bearing 6215.
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15
Fig. 3. Mechanism of C/S database.
the traditional representation form of knowledge, the STEP
information model is a digital form of knowledge
representation that can be understood with the product
design platform and directly imported into it. This form of
product design has the advantage of increasing the speed of
knowledge acquisition and utilization.
4. The knowledge storage and transference
Because the IDPP is built over the Internet, its
repositories should be constructed with a database that is
of the C/S mechanism so that it is easy to achieve the
connection with design platform within Internet environment.
In this paper, a prototype of knowledge repository is
built with an SQL server database (shown as Fig. 3). The
connection between the SQL database and the IPDP server
is achieved through the Open Database Connection
(ODBC). Once a user logs into the repositories of the
IPDP via Internet control, fills out the forms and sends in the
requests, then the IPDP server will start up the Active Server
Pages (ASP) scripts and the CGI program corresponding to
the request. ASP scripts can retrieve data from the SQL
database through the database control ActiveX Data Object
(ADO) inside the ASP, and return the results to the user. By
this means, once the product designer has a need for
knowledge that does not exist in his design platform, he can
log into the IPDP server, search and retrieve knowledge
according to his demands and the constraints that that
knowledge should meet.
5. The method of knowledge search
The common repositories of IPDP contain a great deal of
knowledge that belongs to various disciplines and is
distributed in various sites. When a designer needs to
retrieve the knowledge from repositories of the IPDP so as
to service for his product design, the efficiency of the
method of knowledge search greatly affects the running
performance of the IPDP. Therefore, the IPDP should
provide a high performance method for knowledge search
so as to retrieve knowledge quickly, exactly and efficiently

Fig. 4. The flowchart of searching support part on function driven.
from its repositories. To this purpose, a method of
knowledge search based on a function driven concept and
an Artificial Neural Networks (ANN) technique is proposed
in this paper.
5.1. Function driven
Designing and developing a product is system engineering;
it refers to multi-discipline knowledge. Even products
with a single function or certain phases within the
development of a product require the support of various
kinds of knowledge. A designer could not master all of the
necessary knowledge, or at least could not reach an expert’s
level in all those disciplines. Certainly, he could not employ
a knowledge that he does not completely know. This means
that much knowledge may be familiar to, but not mastered
by the designer though he may know the function and
constraints of this knowledge. Based on this viewpoint,
the IPDP proposes a method of knowledge search. That is,
the knowledge is searched according to the functions of the
knowledge, here called Function Driven Knowledge Search
(FDKS). With this method, when a designer needs a piece of
knowledge he determines the function of the knowledge and
the constraints that the knowledge requires. Then the IPDP
searches for the knowledge in the repositories and retrieves
it, matching the required functions and constraints. For
example, suppose that a designer needs a rotative support
part when he develops a product, and the part does not exist
inthedatabaseofhisdesignplatform.Hewantstogain
it from the outside repositories. Obviously, he does not
know the details of the part. But he knows the functions
of the part and the constraints that the part should meet.
Since the IPDP provides the (FDKS) method, the
designer only needs to provide the function and
constraints that should be met. The IPDP then returns
to the designer the resulting part that coincides with the
user’s requirements. By this means, the designer can
gain the knowledge that meets his design requirements
even if he is not very familiar with it. The flowchart of
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 11
searching the support part based on function driven
knowledge is shown in Fig. 4.
5.2. Back propagation neural networks
Knowledge search driven by function requires the user to
determine the function and constraints that the knowledge
should meet. In many situations, a product (knowledge) has
several functions, and the same function can be achieved
with various products (knowledge). Moreover, the repositories
have a great deal of knowledge and the mapping
relationships between function and knowledge are implicit
and nonlinear. In these situations, it is very difficult to
construct all mapping relationships through general mapping
(e.g. rule reasoning). Then how does the IPDP
efficiently implement the mapping between knowledge
and the function of knowledge? To solve this problem, an
ANN method is applied to construct the mapping relationship
between functions and knowledge in the IPDP system.
In 1986, D.E. Rumelhart, J.L. Meclelland of MIT
University put forward a Back Propagation (BP) algorithm
of multi-layers feed forward ANN. The BP algorithm solved
the modeling problem for layer-networks, and realized the
ANN used in engineering applications. Until the present the
synapses of ANN have almost reached all fields of
engineering application, such as machine vision, class of
decision [12], intelligent control and fault diagnosis [13].
Since the feed forward neural networks with two layers
(containing a hidden layer) were applied in class and curve
approximate fitting, a two-layer BP neural network with a
momentum coefficient is adopted here to construct the
mapping relationships between functions of knowledge and
knowledge itself. The mathematical model of BP neural
networks is shown as follows.
Training samples are {ðXp; DpÞlp ¼ 1; 2; …; N}: Here, Xp
is the Pth input value of training samples. Xp ¼
ðx p p
1 ; x2 ; …x p a Þ: Dp is the Pth ideal output value of training
samples, Dp ¼ðd p p p
1 ; d2 ; …dL Þ: Yp is the Pth actual output
value, Yp ¼ðy p p p
1 ; y2 ; …yL Þ:
To the Pth group of training samples with N groups, the
input and output models of the hidden layer are stated as
Eq. (1). The input and output model of the output layer are
stated as Eq. (2).
net p
XM
j ¼ Wijx i¼0
p
i
o p
8
><
ð1Þ
>:
p
j ¼ f netj 8
><
>:
net p
k
y p
k
¼ XS
j¼0
¼ f net p
k
W jko p
j
Select Sigmoid function f ðuÞ ¼1=½1 þ expð2uÞŠ as the
active function of the BP neural networks. The valve values
ð2Þ

12
Table 3
Constraints of support-pairs with rotative movement
Constraints Values
are hidden in weights via Eqs. (3) and (4).
x p
0 ¼ 21:0; W0j ¼ u ð3Þ
o p
0 ¼ 21:0; W0k ¼ u ð4Þ
The global error function as:
E ¼ 1
2N
X N
X L
p¼1 k¼1
d p
k
2 y p
k
2
Weight-adjusted formula as: DW ¼ 2hð›E=›WÞ
Eq. (5) shows the calculating model of the adjusting
weight in the output layer (h represents the learning ratio
and a represents the momentum coefficient).
s p
k
DW p
jk
¼ y p
k
1 2 y p
k
p p
¼ hsk oj DW jk ¼ h XN
p¼1
s p p
k oj d p
k
2 y p
k
W jkðt þ 1Þ ¼W jkðtÞþDW jkðtÞþaDW jkðt 2 1Þ
The formula of adjusting weights of the hidden layer is
shown as Eq. (6)
s p
i
DW p
ij
¼ o p
j
1 2 o p
j
p
¼ hsj x;p i
DW ij ¼ h XN
p¼1
s p p
j xi X L
k¼1
1 0.5 0
Lubrication (LU) Need Any None
Speed (SP) Low Middle High
Load (LD) Low Middle High
Temperature (TP) Low General High
Precision (PR) Low General High
Structure (ST) None General Compact
s p
k W jk
Fig. 5. The BP networks for searching support-parts knowledge.
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15
ð5Þ
ð6Þ
Fig. 6. Input interface of constraints on Support-Parts with Rotative
Movement in IPDP.
W ijðt þ 1Þ ¼W ijðtÞþDW ijðtÞþaDW ijðt 2 1Þ
In BP neural networks, the coefficients h and a should be
greater than zero and less than one. The groups of training
samples of BP neural networks should be three to ten times
the number of weights [14].
5.3. An example of knowledge search
As seen earlier, the mathematical model of the ANN is
stated. Here, how to apply the ANN model for searching and
retrieving knowledge from the repositories of the IPDP is
discussed. The illustration is based on rotative support parts
stated in Section 5.1.
Corresponding to the knowledge function support-parts
with rotative movement shown in Fig. 4, the constraints are
shown in Table 3. The knowledge matched with the function
requirement of the rotative support is the rolling bearing, the
journal bearing and the magnetic bearing. Here, setting the
number of hidden nodes of BP networks at 4, the structure of
Fig. 7. Output interface of result on Support-Parts with Rotative Movement
in IPDP.

the BP network is shown as Fig. 5. The input parameters of
the BP networks are the constraints shown in Table 3: their
values are endued with 1, 0.5 or 0. If the output value of BP
networks is greater than 0.8, it should be set at 1; if it is less
than 0.2, it should be set at 0. Output values (1,0,0), (0,1,0)
and (0,0,1) represent the rolling bearing, journal bearing and
magnetic bearing, respectively.
Combining the ANN and ASP technique, and migrating
the trained ANN program to the Internet environment, the
IPDP achieves the knowledge search via Internet control
based on a function driven concept. Once a user wants to
retrieve the knowledge needed from the repositories of the
IPDP, he only needs to log into the servers of the IPDP and
confine the function of knowledge and the constraints that
the knowledge should meet (e.g. lubrication conditions
needs, any or none; working load heavy, general or slight;
working speed high, middle or low; etc.). Then the IPDP
system will return the results, which coincide with the
requirements to the user. The input interface of ‘support
parts with rotative movement’ shows as Fig. 6, the output
interface is shown as Fig. 7.
6. Prototype of the IPDP
The prototype of the IPDP is introduced in this section;
its structure is stated as Section 2. The client side of the
prototype system adopts Solidworks and VCþþ programs
(design platform) used for the structure design and
performance analysis of products. The browser for the
client side employs a general WWW browser (e.g. MS
Internet Explorer, Netscape, etc). The pre/post processor of
the prototype, which translates data between STEP
information models and design platforms, employs STEP
Developer 7.0, developed by STEP Tools Inc. The
repositories of the server side are built with the SQL server
database. The servers of the IPDP achieve communication
with the database through ADO and ODBC. The servers
were constructed upon a Peer Web Server in a Windows
NT4.0 workstation environment. The structure diagram of
the prototype system is shown as Fig. 8. Fig. 9 shows a piece
of knowledge searched from certain common repository of
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 13
Fig. 8. The prototype of Internet-based intensive product design platform.
Fig. 9. A piece of searched knowledge (rolling bearing) from repository of
IPDP.
IPDP. Fig. 10 indicates that importing the searched
knowledge into design platform (SolidWorks) to aid the
structure design of rotor-bearing system.
The knowledge is represented in standard and neutral
form, and its store and transfer are achieved successfully
over the Internet, but these are only the precondition to the
implementation of the IPDP servicing for product design.
Ultimately, the knowledge retrieved from the repositories of
Fig. 10. The structure design of rotor system using searched knowledge.

14
Fig. 11. The flowchart of organization, transfer, and implementation of information models in the IPDP prototype.
the IPDP must be imported into a design platform to support
the product design, so it should be utilized and understood in
an exact design platform. Therefore, one must consider how
the design platform is to understand knowledge with neutral
forms (i.e. how to achieve the knowledge downloaded from
the IPDP but utilized in a user application platform). Here,
an implementation flowchart on how to utilize the knowledge
downloaded from the IPDP into a VCþþ 6.0 program
(design platform) is stated. Fig. 11 shows the diagram of the
organization, transference and implementation of knowledge.
The implementation manner of information models
(knowledge) is concretely expounded as follows.
The information models (knowledge) as expressed based
on STEP, were compiled into a data dictionary and Cþþ
classes using the STEP Developer 8.0. The data dictionary is
the compiled binary format of information models. Under a
VCþþ situation, the information models are instanced; and
the instanced information model and data dictionary is stored
in the SQL server database. The instanced information models
contain all information of the traditional form of knowledge
such as structure size, materials and characteristic parameters.
The servers are connected with the SQL database via the ASP
and ODBC. Users can log into the server of the IPDP, search
the knowledge and download the STEP information model of
the knowledge. Once the downloaded STEP information
models are processed with STEP Developer 8.0, they can be
directly used in the VCþþ programs (a design platform).
7. Conclusions and future work
Upon analyzing the limitations of the commercial
S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15
CAD and requirements of product design on knowledge
resource, a framework of the IPEP with client/server
structure, which used to integrate traditional product
design platform and distributive knowledge repositories,
is developed. The knowledge acquisition and transference
is achieved over the Internet. The knowledge is
represented based on the STEP standard. And the
prototype of IPDP is developed successfully. The
research provides a scheme to intensify the design
ability of traditional product design platform, increase
the speed of knowledge acquisition, and shorten the
cycle of product development.
Based on the ANN and the function driven concept,
a new method (FDKS) for knowledge search is put
forward. Superior to rule reasoning, the ANN can solve
complex mapping between input and output and the
FDKS method excellently implements the non-linear
mapping between function of knowledge and knowledge
itself.
The success of the developed prototype illustrates
that the mechanism of the IPDP is right and feasible.
Certainly, there are many works that need further
refinement before the engineering design system runs
perfectly. For instance, the knowledge in repositories of
the IPDP must be further enriched. In addition, the
prototype stated in this paper is only used for acquiring
and utilizing knowledge remotely. The methods of
representation, storage and transference of knowledge
have a commonality. But the application of knowledge
depends on various design platforms. Therefore, the
STEP pre/post processor of the commercial CAD
platform needs to be considered in next-step works.

Acknowledgments
This research was partially supported by an RGC project
of Hong Kong under grant No. 9350007 and National
Natural Science Foundation of China under the grant No.
59990472.
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Shouqin Zhou was born in 1973. Currently, he works in the
Department of Manufacturing Engineering and Engineering Management,
City University of Hong Kong. He obtained the PhD degree in
April 2001, and received M.Sc. in 1997, B.A. in 1994. His current
research interests include distributed knowledge information system for
product design; the standard for the exchange of product model data;
Internet networks techniques, supply chain management, expert
system, etc.
Kwai-Sang Chin is an Associate Professor in the Department of
Manufacturing Engineering and Engineering Management, City
University of Hong Kong. Before joining the university, Dr Chin has
more than ten years of experience in the manufacturing industry. He is a
Chartered Engineer in the UK and Registered Professional Engineer in
Hong Kong. Dr Chin is a fellow of Hong Kong Society for Quality,
senior members of American Society for Quality (ASQ), Institute of
Industrial Engineers (IIE) and Society of Manufacturing Engineers
(SME), USA. His current research interests are new product
development strategies in the Internet Age, quality management
strategies beyond ISO 9000 for Hong Kong and China, and the use
of Artificial Intelligence technologies in product design.
Prasad K.D.V. Yarlagadda is the Director of the Mfg. Systems Engg.
Research Conc., School of Mech., Mfg., & Medical Eng., Queensland
University of Technology, Australia.