INTELLIGENT SUPPORT
TO ANALYSIS-BASED DESIGN IMPROVEMENT PROCESS
PROPOSE: An Intelligent Consultative Advisory System
Marina Novak and Bojan Dolšak
Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, SI-2000 Maribor, Slovenia
Keywords: Computer-aided Design, Structural Analysis, Design Optimization, Intelligent Decision Support.
Abstract: The paper presents the use of intelligent consultative decision support computer system in engineering
design. The system presented is able to provide an expert advice to designer engineers how to improve a
certain design solution considering the results of the preceding engineering structural analysis. The system
guides design engineers through the post-processing phase of the structural analysis and suggests them the
appropriate redesign actions in case the structure is under- or over-dimensioned. The application of the
system in practice is presented by two examples.
1 INTRODUCTION
Engineering design is a set of decision-making
processes and activities used to determine the form
of an object. Considering the technical, economic,
safety, social and certain other constraints, the
designers use their creative abilities to synthesize
alternative design solutions.
Engineering analysis can prove or reject a design
candidate by predicting and simulating its
performance or behaviour. Structural analysis is thus
an integrate part of the design process for many
components. Finite Element Analysis (FEA) is the
most extensively used numerical analysis in
mechanical engineering practice and is incorporated
into many computer aided design systems
(Zienkiewicz, Taylor and Shu, 2005). A candidate
design that fails to satisfy the constraints should be
modified, new values regarding form should be
chosen, and the changed/redesigned candidate
reanalyzed. Engineering analyses play a very
important role in the design improvement process.
The skilled usage of Computer Aided Design
(CAD) tools increases the designers’ effectiveness
and their capabilities when solving complex design
problems (McMahon and Browne, 1999). CAD
systems cover different design activities, such as
modelling, kinematics, simulations, structural
analysis or just drawing technical documentation. In
spite of all this, these kinds of systems do not offer
sufficient support to the designer during the more
creative parts of the design process involving
complex reasoning as, for example, when a possible
candidate design needs to be evaluated and
modified.
In analysis-based design improvement process
the results of engineering analysis need to be studied
and decisions made regarding the design’s suitability
with respect to its engineering specifications. In
general, design changes are indispensable and
designers need help to deal with this problem
properly.
The prototype of an intelligent rule-based
consultative system is being developed by the
authors to provide such advice when considering a
description of the design structure’s critical area.
The system can deal with the results of prior strain-
stress or thermal analysis. It presents a short list of
proposed design changes that should be taken into
account when improving the design.
According to the experience gained so far by
applying the system in engineering practice as well
as in design education, redesign recommendations
presented as a list of the proposed design changes
can support decision making process significantly.
135
Novak M. and Dol
ˇ
sak B. (2009).
INTELLIGENT SUPPORT TO ANALYSIS-BASED DESIGN IMPROVEMENT PROCESS - PROPOSE: An Intelligent Consultative Advisory System.
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 135-140
Copyright
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 SciTePress

Figure 1: Some crucial decisions in FEA-based design improvement process.
Figure 2: Basic architecture of the PROPOSE system.
2 ANALYSIS-BASED DESIGN
IMPROVEMENT
Design is iterative process. How many iteration
steps are needed directly depends on the quality of
the initial design and later design changes. Basic
parameters for design improvement process are
often the results of some engineering analyses. Post-
processing phase of the engineering analysis
represents a synthesis of the whole analysis and is
therefore of special importance. It concludes with
the final report of the analysis, where the results are
quantified and evaluated with respect to the next
design steps that have to follow the analysis in order
to find approved design solution. At this point, FEA
software offers an adequate computer graphics
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support in terms of reasonably clear pictures
showing a distribution of unknown parameters
inside the body of the structure. However, the user
still has to answer many questions and solve many
dilemmas in order to conclude the analysis and to
choose the appropriate redesign steps. Figure 1
shows basic algorithm that the design engineer have
to perform in analysis-based design improvement
process.
It is obvious the algorithm presented requires a
lot of knowledge and experience, not only in the
area of analysis itself, but also in design, giving the
word design its broadest meaning. Designer has to
be able to judge, whether the results of the analysis
are correct and reliable, and also to decide what kind
of design changes are needed, if any.
Many young inexperienced engineers need
intelligent advice to perform the analysis’ results
interpretation and consequent design improvement
process adequately. Unfortunately, this kind of help
still cannot be expected from the present FEA
software, as the traditional systems are rather
concentrated on numerical aspects of the analysis
and are not successful in integrating the numerical
parts with human expertise. Intelligent decision
support is required (Turban, Aronson and Liang,
2004).
3 PROPOSE – INTELLIGENT
DECISION SUPPORT SYSTEM
A prototype of the intelligent system named
PROPOSE, is being developed to support analysis-
based design improvement process (Novak and
Dolšak, 2008). PROPOSE provides a list of redesign
recommendations that should be considered to
improve the design candidate considering the results
of a prior analysis. As a rule, there are several
redesign steps possible for design improvement. The
selection of one or more redesign steps that should
be performed in a certain case depends on the
requirements, possibilities and also on wishes.
The most important part of the system is the
knowledge base. The theoretical and practical
knowledge about design and redesign actions are
presented within the system in form of production
rules.
As it can be seen in Figure 2, the knowledge
base of the system is consisted of many different
types of rules and facts that are necessary for the
system to be functional. For example, several rules
are needed just to define the status of the structure
(not stiff enough, under-dimensioned, over-
dimensioned or satisfactory). However, from the
technical point of view, the most important rules in
the knowledge base are those defining redesign
recommendations.
The system is encoded in Prolog that was chosen
because of its built-in features such as rule-based
programming, pattern matching and backtracking
(Bratko, 2000). Our work was concentrated on
declarative presentation of the knowledge, using
data–driven reasoning, which is built in Prolog.
However, some control procedures were also added
to the inference engine of the system to adjust the
performance to the real-life design process.
For the user interface, our goal was to simulate
the communication between the student and design
expert. As presented in Figure 2, the user interface
has many features including help, which enables the
efficient and user-friendly communication. It is
however evident that PROPOSE is a prototype,
which is still the subject of research and, as such,
cannot be compared with commercial software.
A detailed description of the system architecture
including all development phases can be found in
(Novak and Dolšak, 2008). Here we will concentrate
on some application characteristics of the system.
4 APPLICATION
OF THE PROPOSE SYSTEM
In order to use the system, the user simply needs to
run the executable file "PROPOSE.exe". The
execution starts with the system introduction
presented on the screen including some basic
information how to use the system. From that point,
the system leads the user from the specification of
the problem to the final conclusions and
recommendations for design improvement. The
actual data flow that is followed in the application
process is presented in Figure 3.
First, the user needs to present the qualitative
manner of the information about the results of the
engineering analysis: the results reliability, the type
of the engineering analysis (strain-stress or thermal
analysis), the results deviations from allowable
limits, the type of the structure and the abstract
description of the problem area. In case the problem
area can be described in different ways, it is
advisable to do so, as the system will be able to
propose more improvements that are possible.
Help is available through the whole data input
process. For every problem area, the system searches
INTELLIGENT SUPPORT TO ANALYSIS-BASED DESIGN IMPROVEMENT PROCESS - PROPOSE: An Intelligent
Consultative Advisory System
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for the redesign recommendations in the knowledge
base. The results in form of the redesign
recommendations are written on the screen. The user
can also get insight into the inference process.
If required by the user, the system presents all
the steps that led to the final conclusion together
with the list of recommended design changes.
In addition to the explanation of the inference
process, the user can also get more information
about certain redesign proposals. This kind of
information is provided not only for the geometry
changes, but also to support the selection of more
relevant material. Redesign proposals are explained
with text or with pictorial examples. Some proposals
are explained in either ways.
Figure 3: Data flow of the PROPOSE system.
5 PRACTICAL EXAMPLES
In continuation, two design studies are briefly
presented to demonstrate the use of PROPOSE
system in practice. Complex, expensive and time
consuming analysis-based design improvement is
justified and makes sense when the product needs to
satisfy certain structural and other specific criteria.
Mass production where even small savings per
single product can lead to significant savings for the
whole production quantity is the other important
optimization criteria. Our first example is an ice axe
that has to fulfil very strict structural criteria, as the
life of the user depends on its strength. At the same
time the ice axe also needs to be as light as possible.
Our second example is an open-end spanner. It
belongs to the group of products that are produced in
big series, while structural criteria are also
prescribed in detail.
5.1 Ice Axe Design Optimization
Ice axe is special mountaineering equipment.
Considering the strength, two types of ice tools
exist. In the project presented here, the basic type
with lower strength for use in general circumstances
as on glacier for snow hiking, for ski mountaineering
etc., was a subject of consideration. The material of
the ice axe should be as light as possible, while at
the same time it has to ensure the strength and
toughness at low temperatures. There are several
static, dynamic and fatigue test methods and
requirements prescribed for the ice axe in special
standards (EN-13089 and UIAA-152).
The optimization of the ice axe design was
performed in step-by-step manner. First of all, a
simple initial design was made in geometric
modeller. This model and each consecutive design
candidate was then analyzed according to the tests
and requirements prescribed by the standard. After
every analysis, the PROPOSE system was applied to
get some recommendations for further design
improvements (Kurnik and Zerdin, 2007). Figure 4
presents an example of the recommendations
provided by the PROPOSE system.
In process of improving the ice axe design all
three possible types of design changes (A, B and C)
were made. The first FEA results for the axe “made”
entirely from aluminium alloy clearly shown, that
the pick of the axe is not strong enough. As a
consequence, it was decided to design the axe as a
combination of the steel pick and aluminium shaft
(change type A). The position of the juncture
between both parts of the axe was chosen carefully
to move the force from the critical cross-section area
(change type C). However, most of the changes
addressed the geometric appearance of the axe
(changes type B). In earlier design optimization
phases, geometry was changed in order to improve
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Figure 7: The execution of the proposed design change.
The functionality of the spanner remains
unchanged, while material use and consequently the
weight are reduced. Thus, analysis-based design
optimization supported by intelligent advisory tool
has proved to be successful again.
6 CONCLUSIONS
Structural analysis-based design optimization is a
part of development process for many products.
When numerical part of the engineering analysis is
finished, designer has to be able to judge, whether
the results of the analysis are correct and reliable,
and decide what kind of design changes are needed,
if any. Most of design engineers need “intelligent”
advice to perform results interpretation adequately
(Pinfold and Chapman, 2004). Unfortunately, this
kind of help cannot be expected from the present
software. For this reason, many research activities
are oriented in making analysis-based design
optimization process more intelligent and less
experience-dependent (Chapman and Pinfold, 2001).
In this paper an intelligent aid for analysis
results’ interpretation is presented in form of the
intelligent consultative advisory system, which
provides a list of redesign recommendations that
should be considered to optimize a certain critical
area within the structure, considering the results of a
prior stress/strain or thermal analysis.
The user has to define design problem and
present the results of the engineering analysis. In
addition, critical areas within the structure need to be
qualitatively described to the system. These input
data are then compared with the rules in the
knowledge base and the most appropriate redesign
changes are determined and recommended to the
user. The abstract description of the problem area
should be as common as possible to cover the
majority of the problem areas, instead of addressing
only very specific products.
In cases when the problem area can be described
to the system in different ways, it is advisable to run
the system several times, every time with different
description. Thus, the system will be able to propose
more design actions, at the expense of only a few
more minutes at the console.
Some experts individually evaluated the system
from two points of view. Firstly, they tested and
evaluated the user interface of the system by
inspecting how well the system helps and guides the
user, or even enables him or her to acquire some
new knowledge. Secondly, they analysed the
performance of the system on some real-life
examples. They evaluated the suitability, clearness
and sufficiency of the recommended design changes.
They all shared general opinion that the PROPOSE
system is an effective tool, which provides useful
guidance for further design steps. All comments,
critiques and suggestions presented by the experts
were taken into consideration and resulted into
numerous corrections and adjustments of the system.
REFERENCES
Bratko I., 2000. Prolog: Programming for Artificial
Intelligence, Addison-Wesley. 3rd edition.
Chapman C., Pinfold M., 2001. The application of a
knowledge based engineering approach to the rapid
design and analysis of an automotive structure. In
Advances in Engineering Software, 32, 903-912.
EN-13089 and UIAA-152. Ice Tools (Axes and Hammers).
Kurnik R., Zerdin D., 2007. Analysis-based design
optimization using intelligent advisory system
PROPOSE. Seminar work for the subject: Intelligent
CAD systems, Faculty of Mechanical Engineering,
University of Maribor.
McMahon C., Browne J., 1999. CADCAM: Principles,
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Hall. 2nd edition.
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Improvement. In Engineering Applications of
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results. In International Journal Computer
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Podpecan A., 2009. Applying PROPOSE system for open-
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subject: Intelligent CAD systems, Faculty of
Mechanical Engineering, University of Maribor.
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Support Systems and Intelligent Systems, Prentice
Hall. 7th edition.
Zienkiewicz O.C., Taylor R.L., Shu J.Z., 2005. The Finite
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