Modern Mechanical Engineering, 2011, 1, 13-24
doi:10.4236/mme.2011.12003 Published Online November 2011 (
Copyright © 2011 SciRes. MME
The Part Count Tool (PaCT) for Design Concept Selection
Tarang Parashar1, Kerry Poppa2, Katie Grantham Lough3, Robert B. Stone2
1Department of Mechani cal a nd Aer os pace Engineering, Missouri University of Science and Technology, Rolla, USA
2School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, USA
3Departme n t of Engineer ing Management and Systems E ngineeri ng,
Missouri University of Scien ce and Technology, Rolla, USA
Received July 28, 2011; revised November 1, 2011; accepted November 11, 2011
This paper presents a part count tool that predicts the part count for a particular product concept during the
conceptual design phase. The part count tool will also aid in ranking the design concepts by the criterion of
number of components for a product. This tool utilizes existing automated concept generation algorithms to
generate the design concepts. It extracts the available data from the Design Engineering Lab Design Reposi-
tory to determine an average number of parts per component type in the repository and then calculates an
average part count for new concepts. The part count tool also uses an algorithm to determine how to connect
two non-compatible components through the addition of mutually compatible components. While emphasis
is placed on the average parts per product in evaluating designs, the overall functional requirement of the
product is also considered.
Keywords: Mechanical Design, Conceptual Design, Design for Manufacturing and Assembly
1. Introduction
Intense competition in the consumer market pushes de-
signers to consider manufacturing costs more thoroughly
and completely early in the design process. Typical cost
reduction is achieved either by reducing the number of
the components or by reducing the time required for ma-
nufacture or assembly or some combination of the two.
In order to minimize the cost associated with production,
time, and labor, Design for Manufactur e (DFM) and De-
sign for Assembly (DFA) techniques have been adopted.
DFM techniques fo cus on the manufacturing aspects of a
potential product during the design of the product whereas
DFA focuses on the assembly operations during design.
A major focus of both techniques is reducing the number
of required parts which in turn reduces cost associated
with inventory, material and overhead [1]. Approxi-
mately 70% of the final cost of a product is determined
during the design process [1]. Design for Manufacture
and Assembly (DFMA) combines DFM and DFA tech-
niques to systematically reduce final product cost by
guiding conceptual design decisions [1].
The Design Repository [2] is a heterogeneous collection
of design knowledg e about produ cts, which supp lies data
for a variety of concept generation tools such as the matrix
based Morphological Evaluation Machine and Interactive
Conceptualizer (MEMIC) [3], the grammar based Com-
ponent Flow Graph (CFG) [4], and Morphological Chart
Search [2]. Research is underway to develop and improve
concept generation techniques to provide designers with
a variety of tools for concept selection. The tool discussed
in this paper, the Part Count Tool (PaCT), is designed to
assist designers with concept selection based on the num-
ber of parts per product, usually referred to as part count
in DFMA. The PaCT uses available Design Repository
data to dynamically ca lculate the average number of parts
for a given component type, predict the final part count
and rank concepts accordingly. Given an estimate of part
count, designers can further evaluate concepts based on
predicted assembly and manufacturing cost of the final
2. Background
The PaCT algorithm builds upon sev eral concept genera-
tion technologies that generally follow from the system-
atic approaches of Pahl and Beitz [5] and Otto and Wood
[6]. The specific design approaches include functional mo-
deling, design repositories and automated concept gen-
eration algorithms and are reviewed next.
2.1. Functional Modeling and Functional Basis
Functional modeling clarifies the overall function (i.e.,
“what” the product must do to satisfy user requirements)
of a particular product [5], and a functional model diagram
is as a graphical representation of that model. Functional
modeling is an important tool in the conceptual design
phase since it places the emphasis on what the product
must accomplish versus how (or the physical solutions) it
is accomplished. Functional descriptions for a product des-
cribe the relationship between the input and the required
output and take the form of a verb-object pair [6] where
the function is the verb and the flow is the object. A black
box model illustrates the overall function of a system
based on the customer needs. Mor e detailed chains of func-
tions describe the changes undergone by different flows
as they pass through a system [6]. These chains are then
grouped together to form a functional model diagram for
the whole system. An example functional model diagram
is shown in Figures 1 and 2.
When first developed, functional modeling described
only basic functions, and a common language did not exist
to describe the flows [5,7-9]. Progress was made when
the Functional Basis was introduced to provide a more
systematic and useful design vocabulary for the design-
ers and enable computing based on functionality [5,7-9].
The Functional Basis employs a set of definitions for the
Figure 1. Black box for iRobot Roomba.
Figure 2. Snippet of a functional model for iRobot Roomba.
f unction and flow terms. Terms like transfer, actuate, import,
etc. are examples of functions and terms like solid, liquid,
status signal, etc. are examples of flows [7]. Figure 1 is
an example of a black box model diagram for the iRobot
Roomba, a robotic floor vacuum. Figure 2 illustrates a
sn ippet of a functional model diagram of an iRobot Room-
ba representing one of its virtual walls, a device that cre-
ates a electromagnetic [EM] barrier to contain the robot
in a desired area.
2.2. Design Repositories
Over time product design and manufacture activities have
become more complex [7]. A wide variety of information
pertaining to various aspects of design is required by
designers to design or redesign a product so that it meets
the needs of the customers [10]. Design repositories have
been proposed as a hub for storing design information
that can be searched and reused. The National Institu te of
Standards and Technology (NIST) Design Repository
project [10] and the Design Engineering Lab Design
Repository [11] represent two efforts to create a design
database to aid designers in various aspects of design.
The Design Engineering Lab Design Repository (hereaf-
ter referred to more succinctly as the Design Repository)
hos ts data for approximately 5630 artifacts extracted from
129 consumer products representing the electromechani-
cal, hydromechanical and electronic domains. There are
multiple ways of searching for artifacts in the database.
For example, one may search for an artifact by its func-
tionality or attributes. Textual description, physical pa-
rameters, manufacturing process, failure information, part
number, and supporting functions are recorded as attrib-
utes. The product data in the Design Repository can be
extracted in a variety of forms geared to support concept
generation. Example formats include matrices such as
the function component matrix (FCM) [2] and the design
structure matrix (DSM) [2]. The FCM describes func-
tion-component interaction, and the DSM describes the
component-component interaction.
The Design Repositor y schema is built on a PostgreSQ L
database [11]. The schema is divided into sections rep-
resenting seven types of information: artifact-, function-,
failure-, physical-, performance-, sensory- and media-re-
lated information. Artifact hierarchy is developed by using
a parent-child relationship in the artifact table [11]. These
tables provides information regarding the artifacts in terms
of their names, type of assembly, quan tity, corresp ond ing
component basis system name, input flow, and output flow.
2.3. Automated Concept Generation
In the past few decades, much research has been devoted
Copyright © 2011 SciRes. MME
to automating the conceptual design process, reflecting a
more systematic and refined approach to conceptual de-
sign. Mature design methods are often the target of research
in the field as models to follow for automated concept
generation algorithms [12]. Various methodologies have
been developed to automate the design phase. These me-
thodologies primarily focus on assigning appropriate solu-
tions to the sub-functions of a functional model of a pro-
duct and then assembling the individual solutions together
to represent the final product [12].
Pahl and Beitz [5] and Hubka [13] have been pioneers
in formulating systematic approaches for the conceptual
design phase. Howe v er, few computatio na l t o ols exists to
assist the designers during the conceptual design phase.
The tools in existence are typically limited to a p articular
application domain. For example, graph grammars [4,14]
and catalog design [15,16] tackle only specific compo-
nents on the basis of a required behavior and perform-
ance. This limited applicability led other researchers to
expand the capabilities of concept generation algorithms
to produce a broader area of possible solutions. Bryant et
al. [3] developed a means of automating the conceptual
design phase by introducing a concept generator for de-
signers in the early stages of product design. The con cept
generator takes a functional description of the desired pro-
duct as input and uses the Design Repository’s database
(in the form of the DSM and FCM) to generate concept
v ariants. The concep t generation proc ess filters co mponents
that solve the input functionality based on component-
component compatibility. The current concept generator
approach can be summarized as a five-step process:
1) Step 1. Creation of Functional Model
On the basis of customer needs, a functional model for
a product is created by identifying the flows and the
functions acting on the flows while ensuring that all cus-
tomer needs are met. The functional model is then con-
verted to an adjacency matrix identifying the relation-
ships among all of the sub-functions. Figure 3 illustrates
Figure 3. Example of functional model & its adjacency matrix.
a functional model and its associated adjacency matrix
that captures the functiona l topology.
2) Step 2. Generation of Concept Variants
Next, concept variants are produced by supplying the
fun ctional model generated in Step 1 to an automated con-
cept generator. These algorithms use matrix multiplication
or graph grammar rules to find sets of components that
form solutions for the specified functionality. The con-
cept variant supplied to PaCT include the components
need to produce the concept and their interconnections in
a component adjacency matrix roughly analogous to a
design structure matrix.
3) Step 3. Determination of Component Compatibility
At this point, component-component interaction data
is used to determine compatibility among all the compo-
nents in the chain. Two components are assumed compa-
tible if the design repository has a record of them being
connected in a previous product.
4) Step 4. Identification of Feasible Concept Variants
Finally, the process is concluded with the identifica-
tion of a set of concep t variants comprised of compatible
components that solve the desired functionality. Each con-
cept variant is computed by eliminating all the components
that do not solve the function and are not compatible.
3. Research Approach: The Part Count Tool
With the availability of automated concept generation
tools, a large number of concept variants may be produced
—typically too many for humans to process. Many methods
have been proposed to evaluate and filter the concept
variants such as customer satisfaction, risk [15] and per-
formance, and research continues in this area. Thus far,
researchers in the area have paid little attention to con-
cept filtering based on Design for Manufacturing and As-
sembly (DFMA) concerns. Therefore, this work focuses
on the part count for each concept variant as one crite-
rion for filtering the concepts. A part count estimate not
only helps to filter out the co ncept variants, but also pro-
vides needed information to evaluate the manufacturing
and assembling time—factors that largely determine the
final cost of the product. PaCT also addresses the case of
incomplete concept variants, i.e., those where a complete
set of compatible solutions does no t exist for an input func-
tional model. Assumptions about function sharing and
supporting components are made to determine the total
number of parts.
In this section, PaCT is first placed in context of the
typical workflow for a concept generation activity. Fol-
lowing that, the specific data required from a design
knowledge base (such as the Design Repository in this
work) is described along with a typical pseudo code
Copyright © 2011 SciRes. MME
query to retrieve the data. Finally, the PaCT algorithm is
presented along with an explanation of the assumptions
embedded within it.
3.1. General Approach
The general approach for the PaCT is to apply the algo-
rithm to a set of candidate solutions as an aid to concept
selection. As implemented in this work, the PaCT work-
flow consists of inputting a functional model of the de-
sired product to an automated concept generator to create
a set of concept variants, and importing the generated
concept variants into the PaCT application to compute
the part count estimate for each concept variant and to
display the results. All concept v ariants can be compared
in PaCT’s Bill of Materials (BOM) view. The user of PaCT
can select different options to account for needed fasten-
ers and to refine the part count estimates. The part count
es timate can then be used when developing con cept vari a n t
rankings to guide the selection of a concept for further
development. The general steps involved in generating
the concepts and executing the PaCT algorithm to com-
pute the total number of parts are outlined in Figure 4.
3.2. Data Collection
In order to compute the part count for a given concept,
two intermediate calculations are needed for each com-
ponent that composes a concept variant. The first is the
average number of parts in the general component. The
second is a table of possible connections between the
general component and all other potential components,
whether they are included in the current concept variant
or not. Both of th ese calculations rely on observations fro m
previous products and utilize data in the Design Reposi-
tory database. For each calculation, the logic used to gather
the data is presented as well as an example of the col-
lected data.
3.2.1. Average Number of Parts Per Component
This value represents the average number of parts that
make up a compon ent’s o ccurrence in a product. For ex am-
ple, the component term “housing” typically occurs as
two or more parts in a product, e.g., left and right housing.
This value is needed in cases where the returned concept
variant may suggest the same component to solve two or
more successive functions. In order to accommodate this
opportunity for function sharing, the string of connected
components can be replaced with the average value. The
following pseudo code queries were used to extract the
data from the database:
1) Logic:
(a) COUNT all Artifacts of the Component Type
Figure 4. Steps involved in generating concepts for the part
count tool.
(b) COUNT the number of Systems with Artifacts of
the Component Type
2) Example: Tube
Average values for artifacts classified as a “tube” are
calculated by dividing the value obtained from Step (a)
by the value obtained from Step (b). A value of 2.7931
was assigned to the tub e component, meaning on average
when a tube appears in a product, it is composed of more
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than two parts.
3.2.2. Compo nent Interaction Table
This table defines how components interact with other
co mponents in the Desig n Reposito ry. The foll owing pseu -
do code queries are used to used to determine component
1) Logic:
a) SELECT the Input Artifact of every Artifact of the
specified Component Type
b) SELECT the DISTINCT Component Types of the
Input Artifacts
c) SELECT the Output Artifact of every Artifact of the
specified Component Type
d) SELECT the DISTINCT Component Types of the
Output Arti facts
2) Example:
Following results were generated for tube component.
Table 1 exhibits the result of the component interaction
generated from the data base.
Table 1. Result of Component-Component Interaction
for Tube
3.3. Part count Tool Algorithm
After generating the concept variants (a typical view is
shown in Figure 5) the PaCT algorithm shown in the
Figure 6 is used for pr edicting the number of parts fo r a
The concept variants are first checked for the compo-
nent compatibility. If there is no interaction between the
first two components the algorithm identifies the next
component in the chain and checks for compatibility with
Table 1. Result of component-component interaction for
Compatible Input
Components Compatible Output
guiders screw
nozzle distributor
housing mixer
tube material filter
cover housing
friction enhancer tube
ic engine clamp
electric motor cap
heating element reservoir
container container
Figure 5. Concept variant shown in the concept generator interface.
Figure 6. Algorithm for part count tool.
the initial component. If an interaction is discovered, the
positions of the components are switched and the com-
patibility continues with the next component in the chain
(Figure 7). If no interaction exists with any components,
a linking component is selected that interacts with each
of two components (Figure 8). Once all components are
checked for interaction, the average value is assigned to
each component and the sum of the values are calculated
to determine the average number of parts required for the
3.3.1. Part Count T ool Assump tions:
Se veral assumptions are required to arriv e at the total part
count for a given concept variant. They are described next.
1) If one component immediately follows an identical
component in sequence, only one component is selected.
However, if an identical component is found at a differ-
ent place, it is considered twice. See Figure 9 for illus-
2 ) When checking inter actions, only the nex t component
complexity at this stage in design tool development. See
Figure 7. Example of a component switching.
Figure 8. Example of addition of linking component.
Copyright © 2011 SciRes. MME
Figure 9. Illustration of assumption 1.
in a series is checked for compatibility in order to avoid
Figure 10 for illustration.
3) If there are more than two components capable of
providing a link between two incompatible components,
then preference is g iven to the component with the lowest
average value in order to consider the fewest components
for a particular product. See Figure 11 for illustration.
4) The final concepts are ranked according to number
of parts, and not functional accuracy. The ranking assumes
that the concepts with the least number of parts is most
3.3.2. As se mbly Functions
Components that perform assembly functions (i.e., parts
needed to secure, guide, position or couple other com-
ponents together) are added to the part count tool based
on user input in order to generate a more accurate picture
of the total number of parts. These components incl ude t he
screw, n ut-bolt, solder ,fastener, and rivet. T o validate the
choices of these co mponents, a check was made to ensure
that all assembly functions were covered including gu ide,
position, secure, and couple. For example, the screw, nut-
bolt and fastener all solve a coupling connection, based
on the definition of coupling [17] which states that two
components exhibit a coupling connection if an interme-
diate artifact aids in the assembly of those components.
No external components are required to perform the se-
cure function [17]. The guiding connection requires no
intermediate component because by definition mating
surfaces must have a moving interface nor does the last
assembly function [17]. Position does not require any ex-
tra components since this function requires a connection
that restr ic ts th e movement of the two components in mul-
tiple directions and allows the artifact to come loose with
the application of additional extra force [17]. Concepts are
then ranked based on the total number of parts where the
fewest parts implies the highest ranking.
4. Case Studies and Discussion
To illustrate the initial validation of t he tool, two cases are
Figure 10. Illustration of assumption 2.
Figure 11. Illustration of assumption 3, where x < y.
presented. First, a product that already exists in the De-
sign Repository is checked to make sure the algorithm
can accurately recall part count. Second, a functional model
for an existing product not currently included in the De-
sign Repository is predicted. Both products’ functional
models are processed through an automated concept gene-
rator algorithm and the output that matches the actual
product configuration is submitted to PaCT and the esti-
mated part count is compared to the actual part count of
the product.
4.1. Case Study I
The first product to be tested was a Fisher Price racing
dog toy for children. Initially, a functional model (Figure
12) was created and the interrelationships among the sub
functions wer e expressed in an adjacenc y matrix. An appli-
cation to draw functional models, known as FunctionCAD
and available at,
was used to draw the model and automatically export an
adjacency matrix of the functions to the concept genera-
tor application. The concept generator provided many
design concepts that solved the required functionality.
The generated concept that most closely matched the
racing dog toy was saved in a component adjacency ma-
trix format that was directly input to the part count tool.
Then the part count tool estimated the number of parts
required to build the concept.
Table 2 shows that a total of 30 parts were used to
manufacture the actual Fisher Price racing dog toy from
Design Repository database. The PaCT (Figure 13) pre-
dicted 43.4 parts to manufacture the concept that most
closely resembled the actual product. These 43.4 parts in-
clude parts that perform assembly functions. Comparing
the parts listed in the part count tool with the actual data
re veals that while basic component types are the same, the
estimated of total number of parts is higher than th e c oun t
observed in the real product. The PaCT algorithm appears
to provide a conservative estimate of parts in this case.
Copyright © 2011 SciRes. MME
Copyright © 2011 SciRes. MME
Figure 12. Functional model for the fisher price racing dog toy.
Table 2. Actual number of parts for the fisher price racing
dog toy.
Component Name Number of Distinct Parts
Switch 2
Wire 1
Back Wheel 2
Speaker 1
Motor 1
Screws 9
Shaft 1
Housing 3
Circuit Board 1
Gear 2
Battery Contacts 3
Spring 2
Wheel Assembly 2
Total Part Count 30
Figure 13. Concept with support ing c ompone nts.
4.2. Case Study II
The PaCT was next tested on a SKIL PowerTwist Screw-
driver shown in Figure 14, a product that has not been
Figure 14. SKIL power screwdriver in assembled form.
incorporated in the Design Repository. The power screw
driver was dissected (Figure 15) to determine that it was
comprised of 24 distinct parts representing 9 general
component types. Table 3 lists the components and their
respective part counts. A functional model for the power
sc rewdriver was constructed, shown in Figure 16, and sub-
mitted as input to the automated concept generator. The
tool generated concepts based on the functional model
diagram and the concept shown in Figure 17 was deter-
mined to be the closest match to the actual components
in the screwdriver
The number of parts in a screwdriver predicted by the
PaCT compares favorably to the number of parts found
when the tool was dissected, as shown in Figure 18. In
general, however, the part count tool added an extra housing
for the power drill. The average part count for the com-
ponent type housing is 3.61 based data in the repository.
In this case the average is an overestimate because the
real product is comprised of a two piece housing. The
concept variant in Figure 18 uses only ten distinct com-
ponent types to solve the functionality of the power screw-
driver. The reduction in component types from the raw
concept generator output is due to the assumptions of
algorithm, the effects of which are visible in the use of a
single housing for three consecutive functions rather than
a separate housing for each (see Figure 17). Without this
assumption, PaCT would significantly overestimate the
permits the storage, supply, and transfer of electrical en-
ergy (see Figure 17) and makes the concept more modu-
lar than one that relied on an intermediate electric wire to
tra nsfer the electrical energy to the swit ch. By default, the
part count tool does not consider the supporting compo-
nents, but in this case screws were used in addition to
snap fits to connect the two halves of the housing. The
option of modifying the results with assembly compo-
nents was chosen to mimic the actual SKIL power screw-
d r ive r. Data in th e Des ign Repo sito ry ind ica tes tha t a screw
is a likely choice to join two housings. An average of
5.24 parts is found for products in the database that con-
tain the general component type of screw. Adding these
additional parts brings the total part count to 29.13. Fig-
ure 19 shows the concept with supporting components.
Again, PaCT is found to give a conservative estimate for
the products actual p art count.
Figure 15. SKIL power screwdriver in disassembled form
able 3. Total number of parts of SKIL power screwdriver.
Component Name Number of Distinct Parts
Housing 3
Screw 4
Planetary Gear Set
Spring 1
Switch 1
Battery 1
Motor 1
Chuck A4
Bit Holder 1
Total Part C24
total number of parts. In a similar case a single battery
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Copyright © 2011 SciRes. MME
Figure 16. Functional model diagram for the SKIL power screwdriver.
Figure 18. Concept without assembly components.
Figure 17. Concept selected from the concept generator.
Figure 19. Concept with assembly components.
5. Conclusions and Future Work
The PaCT tool presented in this paper allows design for
manufacture and assembly (DFMA) considerations to be
evaluated at the concept selection stage by linking com-
ponent part counts from existing products to product
functionality. This finding provides designers with a more
realistic prediction for the number of parts in a new de-
sign that lends itself to manufacture and assembly cost
forecasting. This tool allows for the rapid comparison of
the large set of results returned by automated concept
Analysis of both the case studies reveals that the part
co unt too l conservatively predicts a greater numbe r of parts
than exist in either product. For the Fisher Price racing
dog toy and the Skil screwdriver the predicted number of
parts was 20% - 30% higher than the actual number. The
conservative estimates of the part count provided by
PaCT are directly linked to the assumptions described in
Sectl in
engineering, increased accuracy can likely be found by
improving the assumptions for handling repeated com-
po nents within a concept. The addition of supporting com-
ponents to the tool, however, does offer a more accurate
picture of the components needed to assemble the main
parts together and, thus, the total number of parts.
In addition to part count, PaCT also is able to find con-
nections between two inco mpatible components b y either
rearranging the existing concept’s components or adding
a new compo nent as an inter mediar y. This alg orith m close s
a gap in the current automated concept generation rou-
tines. However, the addition of a component that might
introduce unintended functionality present issues that
must be considered by the designer prior to acceptance or
level one heading s in you r article.
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