The Development & Future Opportunities for Knowledge Representation of Product Design System

Abstract

Product design is a multidimensional process that requires accurate knowledge representation to enable informed decision-making. Given the large volumes of data involved, efficient knowledge management and transmission is crucial. This study investigates the nature as well as classification of knowledge in product design, looks at several computational methods, and anticipates future prospects in the subject. The study reveals that knowledge representation plays important roles such as simulating physical entities, presenting ontological commitments for developing shared understanding, making it possible intelligent reasoning, delivering computational environments over creative problem-solving, and serving as a medium during human expression and collaboration. Various types of knowledge representation, such as visual, symbolic, linguistic, virtual, and computational, perform diverse functions in the design process. Computational tools like as CAD systems, knowledge-based systems, production data management systems, along with ontology-based systems are critical for managing both process and product information. Future developments include the use overall artificial intelligence, augmented and virtual reality (AR), collaborative design platforms, as well as environmental concerns to improve the efficiency and

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Abstract:
Product design is a multidimensional process that
requires accurate knowledge representation to
enable informed decision-making. Given the large
volumes of data involved, efficient knowledge
management and transmission is crucial. This study
investigates the nature as well as classification of
knowledge in product design, looks at several
computational methods, and anticipates future
prospects in the subject. The study reveals that
knowledge representation plays important roles such
as simulating physical entities, presenting ontological
commitments for developing shared understanding,
making it possible intelligent reasoning, delivering
computational environments over creative problem-
solving, and serving as a medium during human
expression and collaboration. Various types of
knowledge representation, such as visual, symbolic,
linguistic, virtual, and computational, perform diverse
functions in the design process. Computational tools
like as CAD systems, knowledge-based systems,
production data management systems, along with
ontology-based systems are critical for managing
both process and product information. Future
developments include the use overall artificial
intelligence, augmented and virtual reality (AR),
collaborative design platforms, as well as
environmental concerns to improve the efficiency and
The Development & Future Opportunities for
Knowledge Representation of Product Design System
i) Rupesh M. Surwade ii) Abhishek Bangre
Associate Professor, Asstt. Professor,
Priyadarshini Institute of Architecture Priyadarshini Institute of Architecture
and Design Studies, Nagpur and Design Studies, Nagpur
Email: arupesh11@gmail.com
iii) Tushar Bokhad iv) Sharayu Manekar
Asstt. Professor, Priyadarshini Institute of Architecture
Priyadarshini Institute of Architecture and Design Studies, Nagpur.
and Design Studies, Nagpur
efficacy of knowledge representation in product
design.
Keyword s: Product design, Kno wledge
representation, Machine Learning and AI Integration
1. Introduction :
Design, whether simple or complex, is an
omnipresent activity across various disciplines. To
aid designers in making better-informed decisions,
effective computer support tools are essential,
necessitating robust knowledge representation
schemes. The increasing involvedness of design
problems emphasizes the importance of
understanding and implementing knowledge
representation in product design systems.
Product desig n syste ms have be come
inc rea singly sophist icat ed , driven by
advancements in technology and the growing
complexity of product requirements. At the heart
of these systems lies knowledge representation,
a crucial component that enables the encoding,
storage, retrieval, and utilization of design
knowledge [1]. Knowledge representation in
product design encompasses a wide range of
concepts, from simple data structures to complex
ontologies, and plays a pivotal role in facilitating
innovation, efficiency, and collaboration in the
design process [5]

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2. What is Knowledge in Product Design?
Definition and Nature of Knowledge
Product design acquaintance gets acquired by the
interpretation of information, which is then deduced
from data. Information includes observations,
computational outputs, and factual amounts.
Information is created by the understanding and
abstraction, or correlation of facts, and knowledge is
gained through experience and learning with this
information [2]. Knowledge may also be defined as
the expertise, concepts, attitudes, beliefs, and methods
of operation that are capable of being shared and
conveyed within a team or organisation [3].
2.1 Classification of Knowledge :
Classifying knowledge is vital for effective
representation and application. Knowledge for
design and engineering can get classified along a
variety of dimensions:
1. Formal vs. Tacit Knowledge:
o Formal Knowledge: Formal understanding is
found in product documentation, repositories,
descriptions of functions and structures,
problem-solving routines, organizational
structures, software algorithms, and specialist
expertise platforms.
o Tacit Knowledge: Tacit knowledge includes
firsthand knowledge, unspoken models, and
subconscious principles of thumb. The notion
is generally developed over time via
experience and education, making it difficult
to express.
2. Product vs. Process Knowledge:
oProduct Knowledge: Contains data and
understanding about how a product evolves
across its lifespan, such as requirements,
interactions between components and
assemblies, the st ud y of geometry,
functions, behavior, restrictions, and design
reasoning.
oProcess Knowledge: This includes design
process expertise, manufacturing procedure
knowledge, including business process
knowledge.
Figure 1. Knowledge and Product design Information

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3. Compiled vs. Dynamic Knowledge :
oCompiled Knowledge: Learnt by experience
and put into rules, objectives, scripts, and
examples of previously handled issues.
oDynamic Knowledge: Used to develop new
knowledge structures that are not covered by
collected knowledge. It might be qualitative
(sensible thinking reasoning, approximation
theories, explanations of processes) and
quantitative (constitutive, integration,
equilibrium equations, numerical methods).
o Figure 2 illustrates the categorisation of
knowledge in many dimensions within the
product design area.
Figure 2. Knowledge Classification with Product Design
3. Roles in Knowledge Representation :
Knowledge representation plays numerous
important functions in product design, all of which
improve the effective and efficient functioning for the
design process:
1. Substitute for an Entity:
oCo n ceptual Modeling: Kno wled ge
representation enables designers to construct
conceptual models of products that can be
analysed and tested virtually. This lowers the
need for actual prototypes throughout the first
phases of design, saving time and costs.
oSimulation and Analysis: Designers can
simulate numerous situations and forecast results
by digitally portraying a product’s attributes and
behaviour, rather than building and testing actual
models. This aids in spotting possible faults and
optimising designs prior to production.
2. Ontological Commitments :
oDefining Structure and Relationships:
Ontological commitments involve establishing
a shared understanding of the fundamental
concepts and relationships within a design
domain. This common framework helps
ensure consistency and clarity across the
design team.
oStandardization: Using standardized
ontologies for knowledge representation
encourages interoperability and cooperation
among various teams and systems. It permits
the integration of several tools and platforms,
allowing for smooth information interchange.
3. Part of Intelligent Reasoning :
oExpert Systems: information representation
is essential for creating expert systems that
may provide design suggestions based on a
repository of expert information. These
systems may infer design solutions by using
expert-derived rules and heuristics.
oDecision Support: Intelligent reasoning
facilitated by knowledge representation helps
with decision-making by analysing different

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design possibilities and forecasting their
ramifications. It allows designers to select the
best solution based on established criteria and
limits.
4. Computational Environment for Thinking :
oEnhanced Creativity: Providing a computer
platform for thinking enables designers to
experiment with novel ideas without the limits
of actual materials and prototypes. Virtual
environments provide quick iteration and
experimentation, which promotes creativity.
oProblem-Solving Tools: Computational
tools having knowledge representation skills
help to solve complicated design challenges
by breaking these down into feasible elements
and systematically examining potential
solutions.
5. Medium of Human Expression:
oCommunication and Collaboration:
Knowledge representation allows designers
to describe their ideas and thoughts in a way
that others can readily comprehend and
understand. It promotes cooperation among
team members, clients, and clients by offering
a clear and uniform method of conveying
design information.
oDocumentation and Knowledge Transfer:
Representing information in a structured
manner enables the effective documenting of
design decisions, methods, and justifications.
This documentation provides a significant
resource for future initiatives and helps to
transmit expertise across the organisation.
To summaries, knowledge representation in
product design is critical for designers’ ability to
conceptualize, analyses, and successfully convey their
ideas. It promotes logical reasoning, creativity, and
teamwork, resulting in more efficient and inventive
design processes. One of the primary functions of
knowledge representation is to move knowledge
forward and make it clear. Davis et al. [5] propose
knowledge representation in terms of five roles, as
seen in Figure 3.
1. A substitute for an entity whose repercussions
may be established by thought rather than
action.
2. The collection that ontological commitments
or viewpoints regarding a thing.
3. A subset of intelligent thought represented by
sanctioned and proposed conclusions.
4. A computational habitat for thought.
5. A platform for human expression.
Figure 3. Knowledge representation for product design is classified using Owen and Horváth’s [4] taxonomy.

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4. Different types of knowledge representations
Knowledge representation is classified into five
types: visual, symbolic, linguistics, virtual, as well as
algorithmic. Each area contains a range of
representation formats related to product design.
4.1 Computing Tools towards Knowledge
Representation
Several computational techniques help in
knowledge representation throughout product
design, with each addressing a distinct facet of
knowledge management. These instruments aid in
all stages of the designing process, from initial
co nception to more complete design and
manufacturing. Key tools include:
1. CAD systems help create, modify, analyse,
and optimise designs.
2. Knowledge-Based Systems (KBS): Capture
and apply expert knowledge to solve design
problems.
3. Product Database Management (PDM)
Systems: Track product knowledge and data
throughout its lifespan.
4. Ontology-Based Systems: Facilitate the
sharing and reuse of knowledge by providing
a common vocabulary and structure for
representing knowledge.
5. Fu t u re Trend s fo r Knowledge
Representation Research.
The future for knowledge representation upon
product design lies on overcoming the hurdles of
integrating large volumes of data to facilitate
collaborative design processes and enable real-time
knowledge transmission. Emerging trends include:
1. Artificial Intelligence and Machine Learning:
oEnhanced Decision-Making: Artificial
intelligence and machine learning are projected
to play an important role in improving
information representation as well as decision-
making processes. These will allow for more
accurate forecasts, automatic design
optimization, and intelligent data analysis.
oAdaptive Systems: Future AI systems are
going to be more adaptable, learning from
previous design experiences and refining
suggestions over time. They will help
designers explore new alternatives and make
educated conclusions.
2.Virtual and Augmented Reality:
oImmersive Design Environments: The
usage of VR and AR into product design will
continue to expand, resulting in deeper and
engaging design environments. These
technologies will enable designers to
investigate and evaluate their designs in a virtual
setting, thereby reducing the need for physical
prototypes.
3. Remote Collaboration: VR and AR will
make distant cooperation between design
teams possible, allowing for immediate
interaction with digital models as well as
seamless communication. This increases the
efficiency and efficacy of dispersed design
processes.
4.Collaborative Design Platforms:
oIntegrated Knowledge Repositories:
Future designing together platforms will have
complete knowledge libraries, allowing for
quick access to important material, best
practices, and previous project experiences.
This improves information exchange and
collaboration between team members.
oReal-Time Knowledge Transfer: These
systems will facilitate real-time knowledge
transfer, allowing designers to obtain and utilise
essential information throughout the design
process. This improves the speed along with
precision of design decisions.
5.Sustainability and Lifecycle Considerations:
oSustainable Design Practices: Future
representations of knowledge systems will
consider sustainability and lifespan effects.
They will give tools and methods for evaluating

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both the social and environmental impacts of
design decisions.
oLife cycle Management: Knowledge
representation systems can assist in whole
lifecycle management, through initial concept
towards end-of-life disposal. They will allow
designers to think about the complete lifespan
of a product and make educated decisions
that minimize unwanted repercussions.
6. Conclusion:
Effective information representation is critical to
the achievement for product design systems.
Designers may make educated judgements and build
cre ative pro ducts by understanding t he
characteristics and categories of knowledge, as well
as using computational tools. Future study will look
at new approaches and technology to improve
knowledge representation and meet the changing
demands about the design industry.
Knowledge representation within product design
systems is critical for increasing the efficiency,
precision, and originality during the design process.
Designers may make more educated choices and
develop more effective and sustainable products
through comprehension of the nature and
categorization of information, using various types of
knowledge representation, and using computational
tools. Future advancements in AI, VR/AR, design
collaboration platforms, and sustainability concerns
will improve the capabilities for knowledge
representation systems, helping the design industry
meet its changing demands.
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