INTEGRATION OF HUMAN COGNITION INTO PLASTIC
PRODUCTS’ DESIGN
A Decision Support System
Urška Sancin and Bojan Dolšak
Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, SI-2000 Maribor, Slovenia
Keywords: Product Development Process, Plastics, Design for Manufacturing, Knowledge-base Engineering, Decision
Support, Intelligent System.
Abstract: Product development process is knowledge intensive engineering task mainly supported by adequate
computer aids. Decision-making is a part of design process and almost never supported by computational
advice or recommendation to specific design aspects. This paper is orientated at deficiencies in material
selection process within a new product development process. General practice in major enterprises is
represented and the contribution of proposed intelligent decision support system is introduced. Its execution
and integration of human cognition in the field of Design of Manufacturing (DFM) and plastic products’
design are also explicated in this article.
1 INTRODUCTION
Engineering work is computer dependent as
computer aids are used from start to finish of design
process. Product development is also very intensive
decision-making process as designer has to provide
numerous decisions in order to achieve optimal
solutions and finally to present a trade product.
Achieving maximal quality at minimal cost is a goal
of every prosperous enterprise participating in the
market. Designers are often under pressure as they
have to justify management’s trust in new product
also supported with diverse tools for selecting and
evaluating the projects (Palcic and Lalic, 2009).
Today, the computer aids available on the market
does not support the designer in decision-making,
although, they are of great importance to
engineering design steps like drafting, modelling,
analyzing, and simulating. Designers face many
dilemmas linked with various aspects of the product.
Thus, compromises have to be considered at every
design step. In order to create as optimal
compromises as possible, designers have to possess
wide range of knowledge and be aware of all
influential parameters, or alternatively a team of
experts in various fields has to collaborate in
development process (Clarkson and Eckert, 2005).
This article presents an explanation of design
problems, appearing due to the difficulties at
decision-making process, all collected in Section 2.
Section 3 is orientated in plastic products’ design,
including current state analysis and presentation of
the expected behaviour for some similar type of
polymers by comparing one or two their technical
parameters. Section 4 represents the execution of
proposed intelligent decision support system for
plastic products’ design.
2 NEW PRODUCT
DEVELOPMENT PROCESS
A product development process is also a decision-
making process. The engineer has to choose the
proper tools when performing the design process,
such as selecting the adequate software for the initial
problem and, more importantly, he or she has to
make several decisions whilst working with these
tools, in order to achieve an optimal solution.
Human cognition plays the key role in product
development, as knowledge domain is crucial during
decision-making process.
Aiming to world-class product production, the
enterprises congregate the experts with expertise on
several different knowledge domains. The expert
123
Sancin U. and Dol
ˇ
sak B. (2009).
INTEGRATION OF HUMAN COGNITION INTO PLASTIC PRODUCTS’ DESIGN - A Decision Support System.
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 123-128
Copyright
c
SciTePress
team is often numerous due to the complexity of the
product as their knowledge is desirable in diverse
aspects of design:
at selection of material, tool, production
process, etc.,
at performance of analyses and simulations, at
their evaluation, and consequentially, design
modifications,
at Design for X, where X resembles the
appropriateness for manufacturing,
maintenance, service, etc. (Huang, 1996).
Experts’ cognition is a support to designer
during processing though the four phases of design:
task clarification, conceptual design, embodiment
design, and detail design. More developed and well-
provided enterprises are in better position in
correspondence to Small and Medium-sized
Enterprises (SME’s), whose economic status does
not enables hiring an expert to cover a specific
design aspect.
3 PLASTIC PRODUCTS’ DESIGN
PROCESS
3.1 Current State Analysis
Engineer’s contribution to a design results as a
functional and user friendly finished product,
meaning an ergonomic and aesthetic product,
supported by adequate technical background. In
enterprises, it is a common practice to divide a
product development process into two parts:
ergonomic and aesthetic design, where the
outer surface is designed by industrial
engineer (Gordon, 2003),
functional design, where all technical criteria,
along with requirements and conditions, are
considered, and the process is performed by
design engineer
This article is oriented into functional design,
where the material selection phase is of supreme
importance, therefore the ergonomic (Kaljun and
Dolšak, 2006) and aesthetic values, although their
contribution is not marginal, are not discussed here.
After research was made in several Slovenian
world-class enterprises involved in plastic products’
manufacturing, the conclusions were unexpected but
also reasonable. What is obvious, the material
selection within a new product development, can be
characterized as Selection by Synthesis (Ashby and
Johnson, 2005) where design requirements appear in
the form of intentions, features, and perceptions.
This method is used, when knowledge of the solved
cases can be exploited and transferred to other
products with some features in common. In other
words, the product with the same significant
attributes can be used as a template. Preconceptions
about the material used may be logical in some
cases, as also the shortcut to the solution, however,
the creativity and innovation are degraded here.
Figure 1 illustrates the product development
process resembling the situation in most enterprises
included in this study. Design engineers have expert
knowledge about some particular materials,
extensively applied in the industry, in which the
company participates. Thus, the preliminary material
selection is a decision without irresolution.
The design process starts with the outer design
introduced by various 3D computer models
performed by an industrial designer. Computer
models are supported by 3D prime models produced
using rapid prototyping, with the aim of evaluating
the ergonomic and aesthetic design of the product.
After adjustments, the most appropriate variant gets
the experts’ approval so the product progresses into
the most complex and time consuming phase,
functional design. The design engineer studies the
design requirements consisting of technical criteria,
safety requirements and the wishes of the
management. The completed product design can
again be represented by a rapid prototyping prime
model in the form of a completely functional
exemplar, often made of the material resembling the
preliminary selected material for the finished
product. Due to this fact, the testing and measuring
is possible and any necessary adjustment can be
performed accordingly. At this point of the design
process, the designer usually provides a modification
of the material based on past experiences in plastics’
design. The next step includes casting simulation
upgraded with appropriate production process
selection and tool design, where technical
parameters should be defined by considering the
selected material. Casting simulation is repeated
until the design engineer approves the production
appropriate solution.
From Figure 1 it can also be acknowledged that
without preconceptions about material selection, the
material could be modified more than once
throughout the product development process, as new
information about the product appears at every phase
of design.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
124
Figure 1: Potential material changes in plastics design process.
Furthermore, in every stage of design the
potential material choice could be designated as
optimal at that point of the design process.
In other words, if the technical and aesthetic
attributes of potential material are more appropriate,
the design is affected and, as a consequence, the
production price could be reduced.
3.2 Resembling Polymers – Technical
Parameters’ Comparison
In present time, more than 120.000 diverse polymers
have been discovered, some with specific
characteristics, offering the designer a wide range of
choices whilst a design process (Ashby and Johnson,
2005). Major difficulties appear at designing as
engineers cannot master such extensive amount of
polymers’ knowledge therefore, they rely upon
experiences and their expert knowledge of several
already known materials. All this can lead into a
questionable material selection decisions, regardless
the company’s designation as optimal.
There are diverse plastic materials on the market
with similar technical features. The scientific
research on thermoplastics’ technical parameters,
made in our laboratory, shows that Actrylometril-
butadiene-styrene, abbreviated to ABS, high density
Polyethylene (PE), Polypropylene, and Polyamide
(also called Nylon) have resembling overall
characteristics. However, the range is significantly
wider at certain parameters.
As the elastic modulus is a material parameter
with high significance to design, it was chosen to be
a basis of a comparison of four materials already
mentioned. We can observe from the chart at Figure
2 that Polyamide has the greatest extent of elastic
modulus values, while Polypropylene has the
smallest. Some versions of high density
Polyethylene are reaching the lowest values of
elastic modulus in contradistinction to the highest
Nylon variations. However, all studied materials are
well represented in the range between 1.10 and 1.55
GPa. Hence, any query about a material selection
based on the Selection by Synthesis is justifiable as
no evaluations were done for other competitive
materials, such as those included in this study.
Furthermore, all four thermoplastics evaluated
here, can be processed by injection moulding (Mok,
Chin and Hongbo, 2008, Wang and Zhou, 2000).
Due to the size and geometry of the product,
injection moulding is often the most appropriate
solution as it enables large series production.
Another advantage is the ability to produce
INTEGRATION OF HUMAN COGNITION INTO PLASTIC PRODUCTS' DESIGN - A Decision Support System
125
relatively small and precise plastic products, as low
tolerances and slight roughness can be achieved.
0 2 4 6
ABS
(High density) …
Polypropylene
Nylon (Polyamid)
Figure 2: Elastic modulus for thermoplastics [GPa].
In addition, the material price is an increasingly
important parameter of the design process,
especially in mass production where small savings
per part can become significant, when they are
multiplied the number of produced parts. The
diagram on Figure 3 shows that Polypropylene, high
density Polyethylene, and ABS can have a much
lower price than some versions of Polyamide.
0 5 10 15
(High density)
polyethylene
Polypropylene
Nylon (Polyamid)
Figure 3: Price for thermoplastics [$/kg].
It is necessary to explain that a higher price is
sometimes justified due to the acquisition of other
design components of the product, such as e.g. the
possibility of smaller wall thickness, which leads to
a reduction of the material needed for production of
the manufactured goods.
On the other hand, the designer has to evaluate
numerous, and for the final product design crucial
parameters within the development process by
considering all the materials available on the market,
with the aim of achieving optimal results, which can
sometimes be an almost mission impossible.
Younger inexperienced design engineers have
major difficulties regarding material selection, thus,
in order to overcome this barrier, the intelligent
advisory system for plastic product design is
proposed. This computer aid will offer
recommendations and guidelines according to the
required parameters, shape or/and function of the
product and could also be helpful for experienced
designers using the system as a verification tool.
4 IMPLEMENTATION
OF DECISION SUPPORT
SYSTEM FOR DESIGNING
PLASTIC PRODUCTS
The decision-making process is a constant for every
designer aiming at a successful and efficient
performance. Alternatively to experts’ acquired
domain knowledge, we decided to develop a
decision support advisory system (Turban, Aronson
and Liang, 2004, Novak and Dolšak, 2008) in order
to overcome the bottle neck - plastics material
selection (Ullah and Harib, 2008). Figure 4 shows
the expected data flow, where input data are a
significant factor for intelligent module
performance, the results of which depend on
knowledge base content. The main objective of the
proposed system is a consultancy with the designer
in order to obtain the output, containing the most
appropriate material for the product application,
product design guidelines, etc.
The development methods included in this
research are a combination of human cognition in
the field of design knowledge (Chen, Sheu and Liu,
2007) and special domain knowledge expertise in
the field of plastics. The knowledge base will
contain human cognition useful for problem solving
in the form of relations considering modern plastic
materials’ selection and correlated manufacturing
processes, assisted by the Design for Manufacturing
(DFM) methodology (Molcho, Zipori, Schneor,
Rosen, Goldstein and Shpitalni, 2008). Different
approaches to knowledge acquisition and the
appropriate formalisms for the presentation of
acquired knowledge (Valls, Batet and Lopez, 2009)
within the computer program will be of special
importance
The potential for transparent and modular IF-
THEN rules, whose advantage is neutral knowledge
representation, uniform structure, separation of
knowledge from its processing and possibility of
dealing with incomplete and uncertain knowledge, is
planned to be compared with more flexible
knowledge presentation systems, such as fuzzy logic
(Zio, Baraldi, Librizzi, Podofillini and Dang, In
press), where fuzzy sets and fuzzy rules will be
defined as a part of an iterative process upgraded by
evaluating and tuning the system to meet specified
requirements.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
126
and recommendations acquired whilst plastics’
design are anticipated to be a major contribution to
engineer’s work, particularly to decision-making
process.
REFERENCES
Ashby M., Johnson K., 2005. Materials and Design,
Elsevier.
Chen R.Y., Sheu D.D., Liu C.M., 2007. Vague knowledge
search in the design for outsourcing using fuzzy
decision tree. In Computers & Operations Research.
34, 3628-3637.
Clarkson J., Eckert C. (eds.), 2005. Design Process
Improvement – a Review of Current Practice,
Springer.
Gordon M.J., 2003. Industrial Design of Plastics
Products, John Wiley & Sons.
Huang G.Q. (ed), 1996. Design for X - Concurrent
Engineering Imperatives, Chapman & Hall.
Kaljun J., Dolšak B., 2006. Knowledgebase for Ergonomic
Design Support. In WSEAS Transactions on
Information Science and Applications. 3, 1717-1724.
Mok C.K., Chin K.S., Hongbo L., 2008. An Internet-based
Intelligent Design System for Injection Moulds. In
Robotics and Computer-Integrated Manufacturing.
24, 1-15.
Molcho G., Zipori Y., Schneor R., Rosen O., Goldstein D.,
Shpitalni M., 2008. Computer aided manufacturability
analysis: Closing the knowledge gap between the
designer and the manufacturer. In CIRP Annals -
Manufacturing Technology. 57, 153-158.
Novak M., Dolšak B., 2008. Intelligent FEA-based Design
Improvement. In Engineering Applications of
Artificial Intelligence. 21, 1239-1254.
Palcic, I., Lalic, B., 2009. Analytical Hierarchy Process as
a Tool for Selecting and Evaluating Projects. In
International Journal of Simulation Modelling. 8,
16-26.
Turban E., Aronson J.E., Liang T.P., 2004. Decision
Support Systems and Intelligent Systems, Prentice
Hall.
Ullah S.A.M.M., Harib K.H., 2008. An Intelligent Method
for Selecting Optimal Materials and its Application. In
Advanced Engineering Informatics. 22, 473-483.
Valls A., Batet M., Lopez E.M., 2009. Using Expert’s
Rules as Background Knowledge in the ClusDM
Methodology. In European Journal of Operational
Research. 195, 864-875.
Zio E., Baraldi P., Librizzi M., Podofillini L., Dang V.N.,
in press. A fuzzy set-based approach for modeling
dependence among human errors. In Fuzzy sets and
Systems.
Wang K.K., Zhou J., 2000. A Concurrent-Engineering
Approach Toward the Online Adaptive Control of
Injection Moulding Process. In CIRP Annals -
Manufacturing Technology. 49, 379-382.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
128