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Engineering Informatics: A Knowledge Based Engineering
System in Product Design
Mr.M.V.Sulakhe 3 , ProfS.G.Dhande b
Mechanical Engineering Department Indian Institute of Technology-Kanpur.
Ernail address:[email protected]
"Professor in Mechanical Engineering and Computer Science Engineering Indian Institute of TechnologyKanpur, Email address:[email protected]
"Research-scholar,
r-
Abstract :In today/s challenging global market
enterprises must innovate to survive and compete
with other organizations The application of product
lifecycle management (PLM) concept based on
knowledge based is a major strategic innovative
approach . This will improve the enterprises
competitiveness with shorter product development
time .concurrent engineering approach, supplychain -management etc:This paper will describe the
knowledge based system .Knowledge fusion is
modeled in this paper to demonstrate the profile of
modeling of knowledge based system. A case study
will illustrate this.
Key points: Knowledge, Knowledge based system.
Knowledge
Management,
Product
Lifecycle
Management, Knowledge Fusion, and Engineering
Informatics
I.
INTRODUCTION
Definition
of
Knowledge:
Definition
of
Knowledge range from the practical to the
conceptual to the philosophical, and from narrow
to broad in scope .The following definition are
relevant to the topic of Knowledge Management:
•
Knowledge is organized information
applicable to problem solving [17].
•
Knowledge is information that has been
organized and analyzed to make it
understandable
and
applicable
to
problem solving or decision making
[l5}.
•
•
•
Knowledge encompasses the implicit
and explicit restrictions placed upon
objects
(entities ),operations
and
relationships along with general and
specific
heuristics
and
inference
procedures involved in the situation
being modeled[13].
Knowledge
consists
of truths and
beliefs,perspectivea
and
conceptsjudgement
and expectations.
methodologies and know-howl 18].
Knowledge
is the whole
set of
insights,experiences,and procedures that
•
are considered correct and true and that
therefore guide the thoughts ,behaviors
and communications of people] 16].
Knowledge
is
reasoning
about
information and data to actively enable
performance, problem-solving, decision
making, learning and teaching [3].
Knowledge Management (KM)
Definition of knowledge management (KM):
•
KM is the systematic, explicit. and
deliberate
building,
renewal
and
application of knowledge to maximize
an enterprise's
knowledge
-related
effectiveness
and returns from its
knowledge assets [ 19].
•
KM is the process of capturing a
company's
collective
expertise
wherever it resides - in databases. on
paper.
or in people's
heads-and
distributing it to wherever it can help
produce the biggest payofl1.5].
•
KM is getting the right knowledge to
the right people at the right time so they
can make the best decision[ I I).
•
KM involves the identification and the
analysis of available
and required
knowledge,
and
the
subsequent
planning and control of actions to
develop knowledge assets so as to fulfill
organization objectives[7].
•
KM applies systematic approaches to
find, understand ,and use knowledge to
create value[ 10).
•
KHt is the explicit
control
and
management of knowledge within an
organization aimed at achieving the
company's objectives [16].
KM is the formalization
of and access to
experience, knowledge. and expertise the create
new capabilities. enable superior performance.
encourage innovation and enhance customer
value [3).
r:
Engineering System:
Any system can be defined as: An
organized or connected group of objectives; a set
or assemblage of things connected ,associated ,or
interdependent, so as to form complex unity; a
whole composed of parts in orderly arrangement
according to some scheme or plan; rarely applied
to a simple or small assemblage of things ;.... ;as
setoff principles ,ideas ,or statements belonging
to some department of knowledge or belief; a
department of knowledge or belief considered as
an organized whole; a comprehensive body of
doctrines,
conclusions
speculations
,or
theses;.[ J 4JHence the system
will have the
following characteristics
and play roles to
describe the system as a whole: [4]:
J. Assemblage: A system consists of a
plural number of distinguishable units.
II. Relationships: Several units assembled
together are a "Group" or a "Set"
Ill. Goal-seeking: An actual system as a
whole performs a certain function or
aims at single or multiple objectives.
IV. Adaptability to environment: A specific,
factual system behaves so as to adapt to
the change in its surrounding,
or
external environment
The engineering system will consist of the
following distinguishable factors.
a. Man: The man is seen as in the form of
two types of roles .ln first case man acts
as a controlling
part in the entire
system. In the second case man as
information
processing
and taking
appropriate decision making role.
b. Mechanism or Machine: There will be
some machine used for engineering
activity. In manufacturing activity there
will machine .which will carry out
actual
manufacturing
activity.
The
machine itself ,will be powered by some
prime mover.
c. information: The feed back from the
system will be in the form of some
display or some other form of output.
the man will be processing
the
information
based on the goal or
objectives of the system
Knowledge-Based System
Knowledge-based system as a computer
system that is programmed to imitate human
problem-solving
by
means
of
artificial
intelligence and reference to a database of
knowledge on a particular subject Knowledge-
based systems are systems based on the methods
and techniques of artificiaJ intelligence. Their
core components are the knowledge base and the
inference mechanisms. A knowledge base is a
special
kind of database
for knowledge
management
It provides the means for the
computerized
collection,
organization,
and
retrieval of knowledge. The most important
aspect of a knowledgebase is the quality of
information it contains. The best knowledge
bases have carefully written articles that are kept
up to date, an excellent information retrieval
system (search engine), and a carefully designed
content format and classification
structure.
Determining
what type of information
is
captured, and where that information resides in a
knowledge base is something that is determined
by the processes that support the system. A
robust process structure is the backbone of any
successful knowledge base.
Domain -
Ex~
Koo:i,oo..J€
Er,jneer
Fig.l Knowledge-Based
System Architecture
Software engineers: build the inference engine
and user interface.
Knowledge engineers: design, build, and debug
the knowledge base in consultation with domain
experts.
Domain experts: know about the domain, but
nothing about particular cases or how the system
works.
Users: have problems for the system, know
about particular cases, but not about bow the
system works or the domain
Product
Lifecycle Management:
PLM is a concept that aims at
integrating the various processes and phases
involved during a typical product lifecycle with
people participating
in product development
processes [I JConcept --+ Design --+ Prototype --+ Plan --+
Develop --+ manufacture -+ Market -+ Sell --+
Service --+ Re-cycle
PLM is a concept that based on
horizontal business processes as compared to
vertical business units in organization .These
business units are linked based on product life
stages and processes. As such implementing a
scalable and successful PLM strategy requires
re-alignment
or re-structuring
of internal or
external organizational
structures. Teams and
units formation needs to be studied with an aim
to provide an effective structure that can utilize
the benefits of PLM and technology .In order to
create
a scalable
PLM
or collaborative
stratergy,studies on organization structure and its
relevance with the new PLM strategy needs to be
studied.
11.ENGINEERING INFORMAT1CS
The explicit knowledge representation
formalisms and advanced reasoning techniques
are no longer the sole tenitory of artificial
intelligence (AI).New knowledge management
approaches have earned acceptance in many
research communities and have started to emerge
in commercial software. In general researchers
and commercial developers now employ a large
range
of advanced
computing
techniques
including AI research .Although some of these
computational techniques are still applied for
automating
existing mundane task, many are
capable of enhancing the working environment
and empowering engineers in ways that have not
previously been possible .ln all areas , which
involve knowledge -intensive
tasks ,a new
philosophy
that is specifically
tailored
to
computer
applications
in engineering
is
revolutionizing
the field
an "'Engineering
Informatics" is emerging] 12].
Engineering
is knowledge -intensive
activity guided by appropriately
organized
knowledge within computers,
engineers
can
perform a variety of engineering tasks more
efficiently and effectively then without computer
support this has enhanced the value associated
with the engineering tasks. Its uses knowledge intensive
representations
and
flexible
manipulation methods with the aid of computers
.Thus ,through integration and management of
various
kinds of computational
models
knowledge - intensive design aims to synthesize
more innovative artifacts
.to analyze
their
properties in a better way .to evaluate their
performance against requirements under certain
circumstances
.and to design flexible and
effective
manufacturing
process.
these
knowledge - intensive models are not simply
connected to each other but integrated , so that
mutual data exchange and model sharing is
possible.
Informatics is the study of functions,
designed structure ,behaviors and interactions of
natural and artificial computational
systems.
Informatics integrates the use of computational
systems. Informatics
integrates the use of
computational
methods
such
as
AI,database, visualization,and
robotics.
In
addition .it also includes new formalizations of
specialized engineering knowledge itself i:e .the
computational
model and analyses of the
functions , designed structure and behaviors of
engineering
systems.
Thus
engineering
informatics provides fundamental theories, tool,
and methods to organize, mange, apply, share,
and reuse engineering knowledge effectively.
Thus, "Engineering
Informatics":
[6).
•
Explicit representations, describing
physical and abstract components, their
attributes and relationships in ways that
enable shared reasoning support and
incremental improvement.
•
Symbolic
and numerical process
models,
allowing
collaborative
reasoning about the conception, design,
construction, control and operational
behavior of engineering artifacts.
•
Graphical user interfaces, presenting
different visualization.
including 3D
views and animations
of physical
objects as well as other representations
such as special symbols for engineering
diagrams , formulas graphs, network
schemas and tables.
•
Large-scale
database,
including
perhaps millions of related shared,
reusable
and potentially
changing
elements.
•
interactive, enabling individual and
distributed users to carry out activities
such
as
interpreting
intermediate
solution stages; changing product and
process
model characteristics
while
examining
the
consequences;
introducing new contextual information
even at intermediate stages; modifying
solution strategies on the fly; taking
cues from active support and exploring
solution spaces in order to belp evaluate
criteria that are difficult to model
explicitly .
Engineering
Jnformatics
also includes
areas such as information modes / ontologies for
engineering
applications,
PDM
systems,
Enterprise Information Management, and data
exchange standards] 12].
III. KNOWLEDGE MODELING
Knowledge
modelling
is used
In
knowledge acquisition activities as a way of
structuring projects, acquiring and validating
knowledge and storing knowledge for future use.
Knowledge models are structured representations
of knowledge. They use symbols to represent
pieces of knowledge and their relationships.
Knowledge models are as follows: (I) symbolic
character-based
languages
logic;
(2)
diagrammatic representations
- networks and
ladders; (3) tabular representations - matrices
and frames and (4) structured text - hypertext.
Most models are constructed from knowledge
objects such as concepts, instances, processes
(tasks, activities), attributes and values, rules and
relations[9].
Knowledge
representation
IS
one of the
fundamental topics in the area of artificial
intelligence (which investigates representation
techniques, tools and languages). Knowledge
about the domain and the implementation
independent
reasoning-process
of the KBS
however is usually addressed through the use of
ontologies and problem-solving methods. There
are five prominent representation
techniques
widely used in developing KBSs; they are
attribute-value
pairs,
object-attribute-value
triplets, semantic networks, frames and logic.
Models are important for understanding
the working mechanisms within a KBS; such
mechanisms
are: the tasks,
methods, how
knowledge is inferred, the domain knowledge
and its schemas. Modelling contributes to the
understanding of the source of knowledge. the
inputs and outputs, the flow of knowledge and
the identification of other variables such as the
impact that management
action has on the
organizational
knowledge,
Using conceptual
modelling, systems development can be faster
and more efficient through the re-use of existing
models for different areas of the same domain.
Therefore.
understanding
and selecting the
modelling technique that is appropriate
for
different domains of knowledge will ensure the
success of the KBS being designed.
IV. KNOWLEDGE-BASED SYSTEM
ARCHITECTURE FOR KNOWLEDGE
FUSION
One of the preferred methods of
automating
geometry
generation
is to use
parametric template parts. . Using Open-NX
functions, a copy of template part can be brought
from the TDM Cream Data Manager) library
onto the workbench and values of different
parameters can be modified programmatically to
create a new part off the template part. This
approach works well as long as part topology is
fixed. If the topology varies among the different
parts, which need to be created off the template
part, it may require a unique template part for
each variation, particularly if the variation is at
the wire frame or sketch level. If the variation is
at the feature level, sometime one can get away
with using one or fewer template parts by
suppressing/un-suppressing
features
programmatically
to be able to create unique
parts. Regardless, whether the variation is at
wireframe level or at feature level, the solution is
very cumbersome and awkward. In the case of
wireframe variation, multiple template parts need
to be maintained,
which is a very time
consuming and tedious tasks. And in the case of
feature variation, the size of the history tree
becomes very large as it contains superset of all
possible features
Knowledge-Fusion
(KF)
is
a
knowledge-driven
programming
environment
within NX. KF has solution to both the problems
described above. A given part can be broken into
bunch of unique features. Udfs (user defined
features) can then be created for each unique
feature and stored in a library. Using KF, the
logic can be defined (in a .dfa file) to use only
required features (udfs) for a given situation, to
create a unique part. Hence the part history does
not contain extra features. Similarly, KF allows
one to program logic within a single .dfa file to
create unique wire frames (sketches).Knowledge
Fusion
permits the creation of powerful
applications that take advantage of engineering
knowledge. It supports the capture and re-use of
design intent and user intelligence to increase
design speed and productivity while intelligently
controlling change propagation. Designers and
application
developers
can
work
with
Knowledge Fusion directly within the NX user
environment to create rules that capture design
intent. These rules can be used to drive product
design, ensuring that engineering and design
requirements are fully understood and fully met.
Knowledge Fusion delivers new cost and time
savings, and raises quality by standardizing
design processes, enforcing sourcing practices
and incorporating, up front, the manufacturing
and performance
constraints
into the design
environment.
I KM",* F.,..,. ,
!
constructing and documenting the artifacts of
knowledge-based
systems. It is a knowledge
modeling language that can be used with all
major
object technologies
and applied to
knowledge-based systems in various application
domains and task types. The UML profile is
based on the UML 2.0 specifications and is
defined by using the metamodelling extension
approach of UML. It is being designed with the
following
principles
in mind:
()
UML
integration: as a real UML based profile, the
knowledge modeling profiles defined based on
the
metamodel
provided
in the
UML
superstructure
and follows the principles of
UML profiles as defined in the UML 2.0 and (2)
Reuse and minimalist: wherever possible, the
knowledge modeling profile makes direct use of
the UM L concepts and extends them, adding
new concepts only where needed.
The initial knowledge modeling profile
is composed using four main packages based on
their role and relationship in modeling KBS. It
consists of the Knowledge Model package. Task
Knowledge
package,
Inference
Knowledge
package and Knowledge package. This package
forms the knowledge modeling language core
model and is shown in Figure 4 as the
knowledge modeling profile.
I
~MMt~PnIit
I
Kro~Mnl
Rcle rypt
~I
I
~!lS1
I
I
~
'-----,
KDl~B~
1\ I
IM~M~~
,
Fig.3 Knowledge Fusion Methodologies
T8SlKro~
v.
I
I
I~'
Com:epl$
I
Kro~
I
.s-> lIIfmm:e KmI>'Edte
UML KNOWLEDGE MODELING
PROFILE FOR KNOWLEDGE
FUSION
The aim of the UML Knowledge
Modeling Profile is to define a language for
designing,
visualizing, specifying, analyzing,
I
FigA Knowledge Modeling Profile Core package
19]
I
I
TlISkKro~
Fig.5 Domain Knowledge package
Fig.8 Task Knowledge Package
••••••
I
Typo
--~
I
I
I
1
--~
I~~~ I
I
C~.Rulo>
Typo
1
OsItT~.fuooe.d
Typo
I
I
I
I
I
1
1
u,;...~ Do.-(ano.d
Typo
I
I
r
I
Fig.9 Rule Type Package
Fig.6 Concept Package
Kncw'lfflglt
Ba..<-t'
~e<tclts.slt
Fig.7 Relation Package
Fig.l 0 Knowledge Base Package
1
c..... ..
I
qu_
1
Uoo,o.-..
Typo
I
Qank Web
1
Crankshaft
~
\
~
\
.
I',.
--I
rveJ· - -
,I
.
:0 p,.;A~ormtion
c, Crank
.
An
TIITe in hrs
II F\)st·Autormtion
Crank
Tlrre in hrs
Fig.12 Radar Chart representing Pre-automation
and Post-automation timings for Modeling
Crankshaft
Fig. I I Mathematical Model Package
VI. CASE STUDY: CRANFSHAFT
MODELING USING KF
To plot result it was decided to measure
the pre-automation time for each component of
the Crankshaft. while completing the 3D model
of the Crankshaft by different modelers having at
least one-year
experience
in NX-4.1t was
observed that there was difference in the timing
of each modeler to complete the 3D model of the
Crankshaft and Crankshaft component. As a
result average time for completing the 3D model
of Crankshaft and Crankshaft component was
considered.
Now
post-automation
nrmngs
for
completing
the Crankshaft
and Crankshaft
component 3D model were also measured for
validation of Automating process.
Pre·Automation TIlDe in
POlI·Automation TIlDe in
~s
~s
ern Web
0.1
0.1
Crcd:Pin
01)
0.1
ern
03
0.1
MBJ
03
0.1
1
01
I
Crroliaft
Tables I: Timings for completing Crankshaft and
Crankshaft component
Radar chart shows that modeling time of
Crankshaft has reduced to 90 percent by using
Knowledge Fusion for automating the modeling
procedure
VII CONCLUSION:
Engineering Informatics is k.nowledge
based system and can be used in successful
implementation
of
Knowledge
Based
Engineering
application
Knowledge
Fusion.
Knowledge Fusion delivers new cost and time
savings, and raises quality by standardizing
design processes. enforcing sourcing practices
and -incorporating, up front, the manufacturing
and performance constraints into the design
environment.
Managing
knowledge
through
knowledge-based systems is an important part of
an
enterprise's
knowledge
management
initiatives. The process of constructing KBSs is
simiJar
to other
software
systems
with
conceptual modelling playing an important role
in
the
development
process.
Software
engineering has adopted UML as a standard for
modelling,
but the
field
of knowledge
engineering
is still searching for the right
technique. UML could be adopted for knowledge
modelling but UML in its current state has its
limitations, it is an extensible language and thus
can be used to support the k.nowledge modelling
activity through the profiles mechanism.
VIII
REFERENCE:
[I)
A.
Sharma.
"Collaborative
product
innovation: integrating elements of CPI via PLM
framework" Computer-Aided
Design, Science
direct, Elsevier, 2 February 2005
[2] Broking, A. "Introduction to intellectual
Capital." The Knowledge Broker Ltd.
Cambridge, England.I 996
[3] Beckman,
T.
"A
Methodology
for
Knowledge
management."
International
Association of Science and Technology
for
Development (lASTED) Al and Soft Computing
Conference. Banff, Canada. 1997.
[4] Hotomi,K.,"The Concept of System and its
Design(inJapanese),in
Wakuta,H(ed)-Design
of
Accounting information Systems" (Tokyo:JapanManagement association,Chapter-3.
[5] Hibbard.J. "Knowing What We Know."
Information Week. October 20, 1997.
[6] Kunz, J.C, Smith, lTC, and Tomiyama, T.
"Advanced Engineering Informatics."
Journal
Vol. 16, Issue I. pp 1-2.
[7] Macintosh.A. "Position Paper on Knowledge
Assets for Management ." Artificial Intelligence
Applications institute, University of Edinburgh,
Scotland.May 1996.
[8) Myers, P. ed. Knowledge Management and
Organizational
Design.
ButterworthHeinemann. I 996.
[9) Mohd Syazwan Abdullah, Andy Evans, Ian
Benest, Chris Kimble. "Developing
a UML
Profile
for
Modelling
Knowledge-Based
Systems"
Department of Computer Science.
University of York Heslington. YOlO 500. United
Kingdom.
[10) O'DelL "A Current Review of Knowledge
Management
Best Practice." Conference
on
Knowledge Management and the transfer of Best
Practices.
Business
Intelligence.London.December
1996
[II) Petrash, G."Managing Knowledge Assets
for value."
Knowledge
Based
Leadership
Conference.Linkage, lnc.Boston.october
1996.
[12] Shah, Jami J., "Journal of Computing and
Information Science in Engineering."
ASME
journal VoLllssue I. ppl-2.
[J 3] Sowa.J. Conceptual Structures. Addision Wesley. 1984.
[14] The Shorter Oxford English Dictionary of
Historical Principles (1973). (Oxford: Clarendon
Press)
[15] Turban, E. Expert System and Applied
Artificiallnteligence.
Macmillan. 1992.
[16] van der Spek, R. and Spijkervet,
a.
"Knowledge Management: Dealing Intelligently
with Knowledge." Knowledge Management and
its Integrative Elements. Liebowitz & Wilcox.
eds. CRC Press. 1997
[17]
Woolf.Hced.Websters
New
Word
Dictionary of the American language.G. and
CMerriam.] 990.
[18]
Wiig,
k.
Knowledge
Management
Foundation. Schema Press. 1993.
[19)Wiig, k. Knowledge Management: Where
Did it Come From and Where Will IT Go".'
Expert System with Applications
,Pergamon
Press / Elsevier, Vol.l 4 , Fall 1997.