development of a rapid prototyping design advice system
TRANSCRIPT
Journal of Intelligent Manufacturing (1999) 10, 331±339
Development of a rapid prototyping design
advice system
R I C H A R D B I B B , * Z A H A R I TA H A , * RO B E R T B ROW N * and
D AV I D W R I G H T {
*Design Engineering Research Centre, University of Wales Institute, Cardiff, Western Avenue,Cardiff, Wales, CF5 2YB, UK{Department of Design, Brunel University, Engle®eld Green, Egham, Surrey, TW20 0JZ, UK
This paper describes the initial development of a computer based Rapid Prototyping Design Advice
System. The system is intended to assist the designer or project manager, particularly those in small
and medium sized companies, in planning the prototyping stage of product development. It provides
the user with an ef®cient and user friendly development aid which uses information obtained from
the user and Computer Aided Design data to recommend suitable rapid prototyping solutions.
Keywords: Rapid prototyping, expert system, knowledge based system, decision support, product
development aid
1. Background
Rapid prototyping (RP) refers to the technologies that
are capable of producing prototypes from Computer
aided design (CAD) data. It is increasingly being used
by industry as it offers signi®cant advantages in terms
of time and cost reduction and improved quality of the
®nal product. However the advent of a variety of rapid
prototyping technologies such as Stereolithography,
laminated object manufacture, fused deposition
modeling and selective laser sintering have put the
user in a dilemma as to which technology offers the
best solution to their needs. This may be further
complicated by the many secondary processes such as
vacuum casting which are often used in conjunction
with rapid prototyping. Current research in rapid
prototyping concentrates mostly on the improvement
of existing process and materials development
(Rahmati, 1995) and the development of new
processes and materials. This type of research is
mostly carried out by the manufacturers of rapid
prototyping machinery and their partners. Other
research is directed at ®nding ways of manufacturing
metal prototype parts and tooling rapidly (Hague,
1995; Iuliano, 1995; Norman, 1995; Spencer, 1995).
There is also research being carried out on the use of
RP technologies for use in medical applications (Bart
Swaelens, 1993; Duffy), stress analysis, ¯ow analysis
(Chris Driver, 1996; Calvert, 1995) and automated
post ®nishing of parts to remove the stair stepping
effect common to most rapid prototyping processes
(Reeves, 1995). It can be seen that due to the rapid
development of these RP technologies and the
potential bene®ts they provide there is a need for a
computer based system to recommend the best system
that suits a user's requirements.
There have been attempts at developing RP
selection system which are based on relational
databases (MuÈller, 1996; Campbell, 1996). The ®rst
approach uses a relational database-management
system. In this approach a database of machines and
materials as well as calculation of building time and
cost are constructed. A user has to specify details of
the machine and materials to be used. The user has
also to specify the way in which the prototype is to be
built. The method of Bene®t Value Analysis (MuÈller,
1996) is then used to evaluate various combinations of
machine and material against the user's requirements.
0956-5515 # 1999 Kluwer Academic Publishers
An example of the evaluation is ``®rst place, 83% of
requirement degree, for the machine ABC with
material XYZ'' (MuÈller, 1996). There are two major
drawbacks to this approach. Firstly the user has to
have a working knowledge of the machines and
materials that they intend to use. This precludes the
use of the system by contract out users who do not
have their own RP system but need to determine the
best system in order to commission a prototype.
Typically these users are in small companies (small to
medium sized enterprises or SMEs). Secondly the
reliability of the results are in question as they are not
veri®ed against the experience of expert users and
therefore have little or no practical value.
The second approach is also developed on a
relational database-management system. The struc-
ture of the database however differs from the ®rst
approach in that individual features are used as RP
benchmarks. These features could be dimensions,
holes, slots or even features which are speci®c to
particular products. This means that RP systems are
selected based on the speci®cations provided by the
user and re®ned through an iterative process. The
system is based on the hypothesis that ``once the
capability of an RP system has been determined for
individual features it's capability for any component
containing these features can be predicted''
(Campbell, 1996). The monumental task of building
a data base that contains the values for every feature
built using every RP system would make the system
unreachable in terms of cost to the casual user such as
small companies.
In this paper we describe the development of a
system that will recommend the most appropriate
route to creating a prototype. The system has the
distinct advantage of being easy to use by minimising
and simplifying input for non-expert users. In addition
the system covers a wider scope of prototyping
technology such as machining as well as pre and post
processing of rapid prototyping technologies such as
Stereolithography.
2. System requirements
In general a system for giving advice must ful®l
several requirements. These are (Swift, 1987)
2.1. Capable of giving useful advice
The advice the system provides should be concise,
relevant and reliable. It should also be presented in a
convenient and familiar format so that it can be
readily assimilated into the existing tasks the user is
undertaking. The resulting advice should therefore be
presented using phrases, terms and units that the user
would already be using.
In reality the same prototyping methods will vary in
cost and lead time depending upon the circumstances
of the service provider and customer therefore
arriving at a precise numerical results may be
misleading. This design advice system should aim to
provide the user with enough information to select a
viable route and seek a detailed quote from a service
provider. The results of the system should be in the
form of generalized ``advice'' or ``guidance'' as
opposed to de®nitive numerical results. Calculations
that are essentially numerical will obviously produce
results as real numbers and in such cases these should
be rounded to comply with the overall aims of the
system.
2.2. Quick enough so that it does not signi®cantlyoccupy a designers time
The system should enable the required advice to be
obtained quicker than the existing methods of
seeking advice. In addition the system should not
be laborious to use. The user input procedure
especially should be kept to a minimum. The
system should not require input information that
the user could not be expected to have readily to
hand. The calculation time should also be a quick as
is reasonably possible as the user is effectively idle
whilst waiting for the system to produce the resulting
advice.
2.3 Easy to understand and use
The workings of the system should be as simple to
operate and understand as possible. This includes how
information is input in to the system, how the system
is controlled and how the resulting advice is
presented. The input information required by the
system should be in a form the user is familiar with.
Unlike the systems described previously, it would not
assume previous user knowledge of rapid prototyping.
The system though designed for a target user should
332 Bibb et al.
be clear enough to be used and understood by
anybody.
2.4. Able to explain the resulting advice
If the user is to have a high degree of con®dence in the
resulting advice the system should be able to indicate
to the user in simple, understandable terms how or
why a certain result was obtained. This should be in
part indicated by the presentation of the resulting
advice but more detailed information should be
available to the user if requested.
2.5. Easily updated as new knowledge becomesavailable
The system should be able to use the most up to date
information possible to provide the optimum advice.
The system should therefore have facilities built into
it that will allow the information stored within it to be
updated regularly and easily with the minimum
hindrance to the user. Some of this information may
be in a form that user can control but some may need
to be controlled by the system authors.
2.6. EducationalÐusage of the system reduces thenumber of crucial errors made by the designer
The system should aim to educate the user in both
the use of the system and the subject of the system.
The system should increase the users knowledge and
furnish them with enough information as to have
con®dence in the resulting advice. The system
should enable the user to be in a position to discuss
a proposed prototyping route with a service
provider.
2.7. Forgiving of non-crucial errors on the part ofthe designer
The system should be constructed in such a way as to
tolerate errors or omissions in non-crucial areas
without hindrance to the user. Where crucial errors
are made the system should indicate not only the error
but potential default values or limits which will enable
the user to continue using the system and help to
eliminate similar errors in the future.
2.8. Easily accessible
To be successful the system must be accessible to the
target user. In computing terms this will involve
considerations of computer type and operating
system. Cost will also be signi®cant. The cost of
implementing the system should be within the
economic reach of the target users.
3. The structure of the system
Traditionally the structure of an expert system uses a
knowledge base and an inference engine or inter-
preter. The rules are often conditional expressions
such as IF, THEN, ELSE routines. In rule based
applications these conditional statements are
expressed as a pattern rather than a simple Boolean
expression. Another characteristic is that the ¯ow
control is determined by the interpreter rather than
moving from one rule to the next in a lexical sequence
(Ringland, 1988)
In this case this traditional structure was not used
although in essence similar tasks are undertaken by
the software. As with other rule based systems the
tasks are broken down into subroutines. Some of these
subroutines make decisions based on the input data
and other subroutines contain rules for checking and
either implementing or rejecting the decision. The
method of working is similar to traditional rule based
systems although all the subroutines are held in the
same programming environment.
This was possible due to the nature of the subject.
The criteria upon which the decisions are made can be
prioritized and this can give an overall order to the
subroutines. For example the ®rst consideration would
be the ``Number of parts required'' as this has a
greater effect on the subsequent decisions than the
other inputs. Therefore it can be used as an initial
selection of a method, subsequently the other inputs
are checked against this rule according to their
priority.
The use of software authoring tools enabled
graphical user interface design and program structure
to be devised relatively quickly and simply. When
tasks were required that the authoring package could
not handle other applications were automatically
called from the main program to accomplish them
and return the results. This eliminated a great deal of
Rapid prototyping design advice system 333
programming and allowed more time to be spent on
the content of the system rather than coding.
A major aim of the system was a simple user
interface. To this end the number of user inputs was
minimized. This involved the interpretation of three
dimensional Computer Aided Design data (CAD).
This was accomplished by having another program
called from the main program to read the CAD data
and calculate properties and ``infer'' some character-
istics from it. These properties are then passed to the
main program as numerical input data.
4. Linking to CAD data
The main problem with using CAD data is obtaining
the speci®c characteristics of an object from the CAD
description. Simple properties such as overall size and
volume can be obtained quite easily but the
recognition of actual features is problematic at best.
(Swift, 1987) This can be seen by the fact that early
CAD integrated expert systems used relatively simple
two dimensional or two and a half dimensional
objects. For example plates with holes drilled through
them.
Due to these problems the generic STL ®le was
used. The STL ®le format is a triangular faceted
model of the object and is available as an output from
almost all three dimensional CAD systems (STL ®le
format). Originally developed by 3D System Inc. for
use with stereolithography it has subsequently
become a standard input format for all rapid
prototyping systems. This would mean some of the
characteristics would be absolute (such as volume)
but others would be approximations or inferred
properties. These properties although invisible and
meaningless to the user enable specially written rules
to make decisions based upon assumptions made from
these properties. These may not be as accurate as
absolute data but allowed enough information for the
operation of the system within the limits de®ned.
This proved much more satisfactory than the
alternative method of manually de®ning the object
as an assembly of primitive shapes, as has been done
with other software solutions, as this would take an
unreasonably long time for the user to manually
interpret and input the object description whilst still
remaining more approximate than the STL ®le route.
5. Basic rule structure
In this system there are basically two sorts of rules:
(a) Decision rules.
(b) Calculation rules.
The decision rules use the input data to select
possible methods for a solution. These will often be in
the form of either IF, THEN, ELSE statements or Case
Statements. The calculation rules will perform
calculations to create the results of the system.
These will often be mathematical in nature.
The input data consists of manually input data and
inferred data. The manually input data is in the form
of real numbers, integers and selections from pick
lists. These will de®ne the users requirements for the
prototype part. The other data consists of an object
description. This data is inferred or calculated
automatically from the STL ®le of the object.
The result of the system is an Rapid Prototyping &
Tooling (RP&T) ``method''. This method will be a
collection of activities that will enable the production
of the required number of prototypes with the required
physical properties. Each RP&T method will have a
limit on the number of parts it can reliably produce. If
each method has a rule or subroutine written for it
they can be assembled in order of increasing
production limit. This limit can be directly compared
to the user input ``Number of parts required''.
Therefore the ®rst rule encountered in the system
will move down the list of subroutines until it reaches
the ®rst subroutine that satis®es the number required.
Another important requisite of the method will be
whether the physical properties of the parts produced
by this method will satisfy the users speci®cation. To
simplify matters this is de®ned by categories and the
user picks a category. There is a relationship between
the number of parts a particular tooling process is
capable of producing and the physical properties of
the parts they produce. As the process approaches
volume production methods and capabilities the
number of parts possible increases. Therefore the
subroutines for the methods can be grouped into the
categories whilst still being listed overall in order of
increasing number possible. Thus a second sorting
rule can use this selected category to ``jump'' down
the list until it meets a method capable of producing
parts with the required physical properties.
If no physical properties are speci®ed by the user
the initial selection will be governed by the ``Number
334 Bibb et al.
of parts required''. If a physical property is de®ned by
picking the appropriate category the selection will be
performed ®rstly on the ``Number of parts required''.
This initial selection will then be checked against
which category of physical properties it can produce.
If this is correct the selection takes place, if not the
system will jump down the list until the condition is
satis®ed. This is the most basic rule in the system and
can be seen in schematic form in Fig. 1. The basic
structure of the code is indicated in Fig. 2.
Once a method (subroutine) has been initially
selected other rules within that subroutine will then
check whether the other aspects of the input data can
be satis®ed by the selected method. These rules will
decide whether the method will be:
(a) Capable. If not the system will jump to the
next subroutine.
(b) Suitable. If it is capable what additional
processes (if necessary) will be
required to use the method success-
fully. Are there more suitable
alternatives?
If all of the input data is satis®ed the subroutine will
follow another set of rules which will calculate and
display the resulting advice, the costs and lead times
and prepare the explanation material.
The methods for producing signi®cant numbers of
prototype parts are most often secondary tooling
processes which use rapid prototyping (RP) to provide
the necessary master pattern. While the secondary
tooling method is being selected other rules will be
running to select which RP method would be best
suited to the production of the original master pattern.
This is often done independently of the secondary
tooling decisions although there may be some cases
where these two sets of rules affect each other.
For example, the selection of Selective Laser
Sintered tools. These are built directly on RP
machines and therefore no master pattern is required.
The rules may still select the best method for
producing a master pattern but the subsequent tooling
method could either eliminate it or adjust the results to
indicate that although possible it is not necessary to
produce a master pattern.
Selection of the most suitable RP method will
identify characteristics of the object and use them
initially to eliminate the methods which are not
capable. Of the remaining methods some may be
unsuitable and these too will be selected out. Rather
than iterating until only one method remains if a
number of methods are all suitable they will all be
displayed and their relative merits described in the
explanation material. The user can then make an
informed decision of which method to choose based
on that explanation.
As before, some of these rules will be more
important than others. For example ``producible wall
thickness'' will be crucial in selecting an appropriate
method. Equally important may be accuracy.
However, build size constraints are less important. If
a part is too big for the RP machine it can simply be
built in parts and assembled. The rules in that situation
would just adjust the method to account for the
separate builds and the assembly. As before once the
method is established other rules calculate the results,
costs, lead times and prepare the explanation material.
6. Updating the system
The subroutines for the secondary tooling methods are
self contained. In the event of a change either the
subroutine can be replaced or a new one can be
inserted in a suitable place in the order according to
tool life.
For the rules which select rapid prototypingFig. 1. Basic rule structure.
Rapid prototyping design advice system 335
methods for producing small numbers of parts or
master patterns the processes have similar properties
because they essentially operate the in the same
manner (layer manufacturing). The properties such as
accuracy are all de®ned as limits on variables, and
these could be modi®ed by a simple ®nd and replace
routine. If an entirely new method became available
it's properties would need to be inserted and
compared to the existing methods to ®nd where it
should be placed with respect to the initial selection
rule.
7. Worked example
Figure 3 shows the input screen after the STL ®le has
been read in. The object is a part of a ratchet spooling
device for headphones. Some of the properties of the
STL ®le are shown to the user in this screen when the
results are read in. A three dimensional view of the ®le
is shown in another window to the right of the input
window. The user then speci®es the number of
prototypes required, the minimum wall thickness,
maximum aspect ratio and accuracy. The user then
proceeds to the next screen, shown in Fig. 4 to specify
the use required for the prototype and the intended
material.
Once the user has selected a category for the
prototype use and material and any special features
that may be required they proceed to the results
window shown in Fig. 5.
The results are shown broken down into the tasks
required for rapid prototyping of the master pattern,
®nishing the master pattern, and the secondary
tooling process. An estimate for cost and lead time
is indicated for each stage. Clicking the ``More
Info'' buttons opens a windows containing a
description of the processes including diagrams
and photos where necessary. These can be seen in
Fig. 6.
8. Conclusion
The use of this rule structure has enabled a
working system with an excellent user interface
Fig. 2. Basic code structure.
336 Bibb et al.
to be constructed very quickly and simply without
resorting to high level programming environments
or powerful computer hardware. The system will,
within the de®ned limits, recommend a viable rapid
prototyping and tooling method given user inputs
and an object description in the form of an STL
®le. Now a working system can be used in trials
there is scope to incrementally improve the system
according to the results of the trials.
9. Further work
Once the basic structure and interfaces are established
the system can be incrementally improved. This will
be necessary from the point of view of incorporating
developing RP&T technology but also in improving
the level of interpretation of the object to be produced
for example identifying draught angles could be an
important factor. Some of this may be quite
Fig. 3. The ®rst user input window.
Fig. 4. The second user input window.
Rapid prototyping design advice system 337
Fig. 5. The results window.
Fig. 6. The ``more info'' windows.
338 Bibb et al.
demanding in terms of coding and computing
resources and these should always be balanced against
the requirements of the system and the target users to
ensure the system remains fast, ef®cient and simple to
use.
Acknowledgments
The authors would like to thank Jon Ward and Paul
Freeman for their invaluable help in creating this
system.
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Rapid prototyping design advice system 339