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An Engineering-to-Biology Thesaurus for Engineering Design

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Engineering design is considered a creative field that involves many activities with the end goal of a new product that fulfills a purpose. Utilization of systematic methods or tools that aid in the design process is recognized as standard practice in industry and academia. The tools are used for a number of design activities (i.e., idea generation, concept generation, inspiration searches, functional modeling) and can span across engineering disciplines, the sciences (i.e., biology, chemistry) or a non-engineering domain (i.e., medicine), with an overall focus of encouraging creative engineering designs. Engineers, however, have struggled with utilizing the vast amount of biological information available from the natural world around them. Often it is because there is a knowledge gap or terminology is difficult, and the time needed to learn and understand the biology is not feasible. This paper presents an engineering-to-biology thesaurus, which we propose affords engineers, with limited biological background, a tool for leveraging nature’s ingenuity during many steps of the design process. Additionally, the tool could also increase the probability of designing biologically-inspired engineering solutions. Biological terms in the thesaurus are correlated to the engineering domain through pairing with a synonymous function or flow term of the Functional Basis lexicon, which supports functional modeling and abstract representation of any functioning system. The second version of the thesaurus presented in this paper represents an integration of three independent research efforts, which include research from Oregon State University, the University of Toronto, and the Indian Institute of Science, and their industrial partners. The overall approach for term integration and the final results are presented. Applications to the areas of design inspiration, comprehension of biological information, functional modeling, creative design and concept generation are discussed. An example of comprehension and functional modeling are presented.
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1 Copyright © 2010 by ASME
Proceedings of the ASME 2010 International Design Engineering Technical Conferences &
Computers and Information in Engineering Conference
IDETC/CIE 2010
August 15-18, 2010, Montreal, Quebec, Canada
DETC2010/DTM-28233
AN ENGINEERING-TO-BIOLOGY THESAURUS FOR ENGINEERING DESIGN
Jacquelyn K. S. Nagel, Robert B. Stone
Oregon State University
Corvallis, OR, USA
Daniel A. McAdams
Texas A&M University
College Station, TX, USA
ABSTRACT
Engineering design is considered a creative field that
involves many activities with the end goal of a new product that
fulfills a purpose. Utilization of systematic methods or tools
that aid in the design process is recognized as standard practice
in industry and academia. The tools are used for a number of
design activities (i.e., idea generation, concept generation,
inspiration searches, functional modeling) and can span across
engineering disciplines, the sciences (i.e., biology, chemistry)
or a non-engineering domain (i.e., medicine), with an overall
focus of encouraging creative engineering designs. Engineers,
however, have struggled with utilizing the vast amount of
biological information available from the natural world around
them. Often it is b ecause there is a knowledge gap or
terminology is difficult, and the time needed to learn and
understand the biology is not feasible. This paper presents an
engineering-to-biology thesaurus, which we propose affords
engineers, with limited biological background, a tool for
leveraging nature’s ingenuity during many steps of the design
process. Additionally, the tool could also increase the
probability of designing biologically-inspired engineering
solutions. Biological terms in the thesaurus are correlated to
the engineering domain through pairing with a synonymous
function or flow term of the Functional Basis lexicon, which
supports functional modeling and abstract representation of any
functioning system. The second version of the thesaurus
presented in this paper represents an integration of three
independent research efforts, which include research from
Oregon State University, the University of Toronto, and the
Indian Institute of Science, and their industrial partners. The
overall approach for term integration and the final results are
presented. Applications to the areas of design inspiration,
comprehension of biological information, functional modeling,
creative design and concept generation are discussed. An
example of comprehension and functional modeling are
presented.
INTRODUCTION
Utilizing biological information during the engineering
design process has taken many forms. Inspiration for solving
or finding direct solutions to engineering problems have been
obtained through chance observances [1-4], functional keyword
searches [5-7], systematic reverse engineering [8, 9], use of
function-structure-behavior terms to search a database [10, 11],
TRIZ [12], analogical reasoning [13-15], and functional
representation through functional models [16-19]. Although
each method has a different procedure, they all share one thing
in common; the promising biological system or phenomena
must be abstracted to capture the functional principle.
However, the functional principle is not the only biological
aspect that can be mimicked. Morphology (shape), behavior
(strategy), material, manufacturing process or any combination
of these can be imitated. For instance, principle and
morphology of a biological system can be imitated to improve
an existing product [20]. A typical strain gauge has
interdigitated electrodes, is rectangular and can only sense
strain in one direction. The campaniform sensillum or flexible
exocuticle that many insects possess inspired a novel redesign
of the traditional strain gauge, directly based on morphology,
that can sense strain in all directions (360°) [21]. Consider a
circular or elliptical hole in a rigid material; it acts as a stress
concentrator when pressure is applied. An elliptical opening in
the insect’s cu ticle, which is covered by a thin membrane layer,
senses deformation because of the stress concentration [22, 23].
The opening causes mechanical coupling and global
amplification to occur, and acts as a biological strain gauge.
The novel strain gauge is just one example of a successful
biologicallyinspired, engineering design [24-27], however,
choosing the biological system or phenomena to imitate is often
left up to the designer, of whom typically has limited biological
knowledge. Scenarios that involve the designer making
educated decisions about how to utilize biological knowledge
provided the impetus for developing an engineering design tool
that easies the burden on the designer. In 2007, Nagel et al.
2 Copyright © 2010 by ASME
developed a set of signal flow grammars to provide templates
that aid in the manual and automatic assembly of functio nal
models [28]. Nodes are utilized to clearly establish the location
of system boundaries and the required input and output flows in
a functional model. While a grammar is informative when
modeling an unfamiliar system or process, such as a biological
system, the very nature of a grammar prevents it from being all-
inclusive. A grammar of biological functions did not seem
feasible, thus, an engineering design tool that categorizes
biological information based on function, material, signal and
energy was created. The resultant tool is a thesaurus of
biological terms for use with the Functional Basis [29] as a set
of correspondent terms, which is named the engineering-to-
biology thesaurus. This work is not a biological ontology that
allows automated information processing or inference. Rather,
it is a means to map ter minology between two dissimilar
domains for the identification of synonyms. The thesaurus
serves as a versatile design tool that affords design engineers,
with a limited biological background, a means for developing
connections between nature and engineering.
Lindemann and Gramann acknowledge the difficulties of
utilizing biological principles in engineering design in the
following statement, “The first difficulty was to find some of
the huge number of possibilities within biology you might look
at. The main reason is the lack of the specific knowledge
especially concerning the terminology. This problem is time
consuming and in addition one has to understand the principle
of all the different phenomena [9]. The engineering-to-
biology thesaurus aims to circumvent these and other
difficulties by providing a list of synonymous biological terms
to the generalized engineering terms of the Functional Basis
modeling lexicon. The thesaurus has the potential to serve as a
key tool in future biomimetic design activities:
Promotion of knowledge transfer from the biological to
engineering domain;
mapping of biological terminology to engineering function
and flow terminology;
facilitation of biological information in engineering
designs without having an extensive background in
biological knowledge;
promotion of creativity in engineering design; and
assistance during an inspiration search.
In the following sections of this paper several points will
be discussed: (1) background research in engineering lexicons
and taxonomies related to biologically-inspired design; (2)
research efforts related to this work; (3) model for designing
the thesaurus structure; (4) approach taken to integrate
functional terms from related efforts; and (5) the applications
this thesaurus has in engineering design.
BACKGROUND
This research explores the structure and purpose of an
engineering design thesaurus and how it enhances an existing
design lexicon. Researchers at many universities are working
on the knowledge transfer problem between the engineering
and biological domains by developing function or function-
behavior-structure based design languages. The design
language research efforts of Oregon State University, the Indian
Institute of Science and the University of Toronto are the three
that comprise the second version of the engineering-to-biology
thesaurus. Their research efforts are explained in the following
paragraphs.
Oregon State University Research Effort
The formal idea of a standard set of engineering function
and flow terms for systematically creating function structures
was originally proposed by Pahl and Beitz [30]. A function
represents an operation performed on a flow of material, signal
or energy. Numerous researchers further evolved this set of
generally valid functions and flows. Hundal proposed a further
refined set of function and flow classes in [31]; however, flows
were excluded. Little et al. developed a set of function and
flow terms, which classified both functions and flows at class
and basic levels [32]. Szykman et al. created a standardized
taxonomy of function and flow terms, separated into classes
down to the fourth level, for the purpose of computer-based
design [33]. Separately, but at the same time, Stone and Wood
developed a well-defined standardized modeling lexicon
comprised of defined function and flow sets with definitions
and examples, entitled the Functional Basis [34]. Hirtz, et al.
later reconciled the efforts by Stone and Szykman to form the
current version of the Functional Basis [29]. Within the
Functional Basis there exist eight classes of functions and three
classes of flows, both having an increase in specification at the
secondary and tertiary levels. There are 21 secondary and 24
tertiary functions, accompanied by correspondent terms to aid
the designer in choosing the correct function . Similarly, there
are 20 secondary and 22 tertiary flows accompanied by
correspondent terms. In 2009, Stroble et al. [35] further
expanded the Functional Basis to include a set of biological
flow correspondent terms, which comprised the first version of
the engin eering-to-biology thesaurus. Adding biological
function correspondent terms was identified as the next step
and is achieved by integration of multiple research efforts.
Indian Institute of Science Research Effort
Chakrabarti et al. developed a software package entitled
Idea-Inspire that allows one to search a database with a
function-behavior-structure set, which is simply a verb-noun-
adjective set [10, 36]. Their database is comprised of natural
and artificial complex mechanical systems. Each entry’s
motion or process is described functionally by behavioral
language in the form of a function-behavior-structure model.
When using Idea-Inspire, the user abstracts a desired solution
action by choosing terms that describe the function, behavior
and structure from a pre-defined list of terms. The Idea-Inspire
software yields seven behavioral constructs following the
SAPPhIRE model state change, action, parts, phenomenon,
input, organ, and effect for each search result that adequately
fit the chosen function-behavior-structure set [37, 38].
SAPPhIRE explains the causality of natural and engineered
3 Copyright © 2010 by ASME
systems [37, 38]. The aim of the software is to inspire ideas
rather than solve the problem directly, as the name implies.
University of Toronto Research Effort
Researchers at the University of Toronto have worked to
provide designers with biologically meaningful words that
correspond to engineering functions. Hacco and Shu developed
a method for biomimetic conceptual design [39], which was
later refined by Chiu and Shu for searching biological literature
using functional keywords for design inspiration [5, 6]. The
keywords used in the search strategy are cross-referenced with
Wordnet to define a set of natural-language keywords for
yielding better results during the search. Typically, searches
are based on multiple keywords. Later in 2008, Cheong et al.
used the search strategy in conjunction with the terms of the
Functional Basis to identify biologically meaningful words
[40]. The Functional Basis functions in the secondary, tertiary
and correspondent levels were analyzed to develop groups of
words that were similar according to WordNet. Four cases for
identification are discussed and examples presented:
synonymous pair, implicitly synonymous pair, biologically
specific form and mutually entailed pair [40]. Based on
semantic relationships, the engineering function terms of the
Functional Basis were used to systematically generate a list of
biologically significant and connotative function keywords.
ENGINEERING-TO-BIOLOGY THESAURUS
The engineering-to-biology thesaurus was developed to
encourage collaboration between biologists and engineers, and
discovery and creation of biologically-inspired engineering
solutions. The structure of the thesaurus was molded to fit the
knowledge and purpose of the authors; synonyms and related
concepts to the Functional Basis are grouped at class,
secondary and tertiary levels. In this section, the thesaurus
model, population methods of the biological flows and
functions, particular details about the thesaurus, and validation
are explained. The engineering-to-biology thesaurus provided
in Appendix A is the second version and is not a
comprehensive list of all biological terms. However, this work-
in-progress is slowly and steadily bridging the gap between the
two domains. Biological correspondent terms to the Functional
Basis functions and flows are shown in place of the original
engineering correspondent terms.
Thesaurus model
The purpose of a thesaurus is to represent information in a
classified form to group synonyms and related concepts. A
thesaurus of the English language has classes and categories
with an index of terms directing the user to the correct instance
(i.e., noun, verb, adjective) of the term under examination. The
engineering-to-biology thesaurus proposed here has a unique
structure and classification; it is merged with the reconciled
Functional Basis as a set of correspondent terms. It does not
include an ind ex nor does it include adjectives. Only verbs and
nouns that are synonymous to terms of the Functional Basis are
considered. The Functional Basis class level terms, however,
do emulate the classes of a traditional thesaurus. Furthermore,
the secondary and ter tiary level Functional Basis terms emulate
the categories of a traditional thesaurus. Biolog ical terms that
fit in the function and flow sets, and correspond to multiple
functions or flows, are repeated and italicized to designate the
special case. Thus, the classification is predetermined
according to that of the authors’ model; however, it remains the
intermediary between the biology and engineering domains. A
tool such as the engineering-to-biology thesaurus increases the
interaction between th e users and the knowledge resource [41]
by presenting the information as a look-up table. This simple
format fosters one to make associations between the
engineering and biological lexicons, thus, strengthening the
designer’s ability to utilize biological information.
Biological Functions
The majority of biological information is written in such a
way that correlating biological verbs to Functional Basis
functions is relatively straightforward. However, there are
always exceptions. Well-known functional terms that appear in
a biological text may not have the meaning an engineer would
typically know. For instance, the term bleaching outside of the
biological domain means to clean, sterilize or whiten, as most
know. Rather, the biological meaning refers to the process of
separation between the retina and opsin in vertebrate eyes and
causes the retinal molecule to lose its photosensitivity [42]. It
is these types of exceptions that OSU researchers were
cognizant of when compiling the set of biological
correspondent function terms for the engineering-to-biology
thesaurus. Keyword searches of a biological textbook using the
automated information retrieval tool [43] were performed to
gather a list of collocated verbs that occur within the same
sentence as the search word. To signify which function terms
are utilized in both domains, the Functional Basis term is
repeated in the biological correspondent list. It should be noted
that some of the biological function correspondent terms are
nouns that name a process corresponding to a Functional Basis
function. Identified biological functions were cross referenced
in the Oxford American dictionary [44], Henderso n’s
dictionary of biological terms [45] and the Oxford Dictionary
of Biology [46] before placement in the thesaurus, which was at
the discretion of the authors. All other function terms were
obtained from research performed at the Indian Institute of
Science and University of Toronto, which are made explicit in
the next section.
Functional terms from the Indian Institute of Science were
collected from the Idea-Inspire software. Every natural system
entered into the software’s database was indexed using the pre-
determined list of verbs, nouns and adjectives. Analyzing the
list of verbs by cluster [37] revealed scientific terms applicable
to biological systems grouped with engineering terms exactly
matching those of the Functional Basis. Utilizing multiple
dictionaries as in the OSU analysis, the verbs of Idea-Inspire
were paired with Functional Basis functions.
Functional terms from the University of Toronto were
collected from the work by Cheong et al. whom identified
4 Copyright © 2010 by ASME
biologically meaningful words to those of the Functional Basis
[40]. Becau se background work was already performed on the
semantic relationships of the biologically meaningful words,
further investigation was not performed. Rather, the terms
were directly added to the thesaurus.
Biological Flows
In the authors’ experience, understanding biological terms
that were considered flows (material, signal and energy) when
utilizing biological systems or phenomena for idea generation
or design inspiration posed the most difficulty. Determining if
a biological material is liquid, solid or a mixture by its name
typically requires domain knowledge that most engineers do
not have, which cause biological concepts to be perplexing.
Similarly, needing a reference to look up biological terms each
time a potential organism or phenomenon was found made the
research tedious, and disrupted thought patterns leading to
decreased efficiency.
Identification of engineering-to-biology thesaurus flow
terms, for the first thesaurus version, was achieved through
functional word searches of a biological textbook [35].
Functional Basis functions (verbs) were utilized for searching
the biological textbook to extract biological words (nouns) that
an engineering designer interested in function based design
might encounter. The nouns that were collocated, within the
sentence, to the search word were counted and sorted by
frequency and all nouns that appeared more than two times
were considered macrorelevent. Each macrorelevent term was
researched to determine if it was of signal, material or energy
type in the new Oxford American dictionary [44] and
Henderson’s dictionary of biological terms [45] before being
placed. Placement of terms in the engineering-to-biology
thesaurus was at th e discretion of the authors. The flow ter ms
of the first thesaurus version were carried over to the second
version.
Thesaurus Particulars
Key challenges to the approach for populating the
thesaurus described in this research were the time required to
search each term to generate a listing of collocated terms and
understand the definition provided in the dictionary of
biological terms. To determine the material, energy or signal
type of the flow term in question, generally multiple biological
dictionary entries were referenced. Considering biological
processes that perform a specific function within the system
revealed many macorelevant terms that would have been
overlooked if only verbs were analyzed.
The Functional Basis offers a definition and example for
each class, secondary and tertiary term. However, definitions
of the correspondent terms are not provided. Rather, the
correspondent terms are synonyms to the Functional Basis
terms to aid the designer when choosing the best-suited term.
This is also true for the biological correspondent terms.
Biological terms that fit in the function and flow sets, and
correspond to multiple functions or flows, are repeated in the
set of correspondent terms and are italicized to designate the
special case of those terms. This treatment is similar to the
repeated words of the engineering correspondent terms.
Validation
The addition of functional terms and nouns that name
biological processes similar to engineering functions to the
thesaurus requires a validity check of the current listing by a
biologist. Validation of the thesaurus terms was performed by
a professor of Zoology at Oregon State University. Term
placement analysis is the first step in the validation process.
Dr. Brownell reviewed both sets of biological corresponding
terms and offered his insight. Biological terms that were
incorrectly placed in the thesaurus were moved to better map
the terminology to the engineering domain or were removed
due to ambiguity per his suggestion. We believe term
placement analysis by Dr. Brownell is adequate validation to
facilitate all potential applications of the thesaurus, just as the
reconciled Functional Basis is adequate for use with a variety
of design activities. Application validation, the second step,
will occur through future design studies.
INTEGRATION OF DESIGN LANGUAGES
Compiling multiple research efforts focused on language
driven inspiration of innovative engineering designs
strengthens the advantages of each effort. The terms utilized
for Idea-Inspire must be broad enough to capture the principles
of both biological and engineered systems, whereas, the
carefully chosen terms of the Functional Basis were initially
meant for engineered systems only. The biologically
meaningful words discovered by semantic relationships utilized
for creative design exercises, demonstrate functional terms that
yield good results when searching a biological text for
inspiration. Integration of these two research efforts with the
OSU effort ensures the success of future design activities.
Previously tested and successful terms of Table 1 and the broad
scoping, yet easily overlooked, terms of Table 2 are included in
the engineering-to-biology thesaurus of Appendix A.
It is interesting to note that Table 2 does not include any
terms for the function of convert because transform (the
correspondent for convert) and change are considered as the
same cluster for the Idea-Inspire software. Additionally, some
of the biological correspondents in Table 2 are identical to the
original Functional Basis set of correspondent terms. These
terms were repeated to signify that the term is used in both
domains. Table 1 is shorter, but offers on average more
correspondent terms per Functional Basis term due to the
rigorous method of determining biologically meaningful terms.
Moreover, Table 1 offers a fascin ating observation about the
sustainability of natural systemsmultiple terms have multiple
functions. Consider connect, it could mean bringing two
objects together or it could refer to stabilizing support. Also
consider bind, this term could refer to stability, liking or
exporting. Both research efforts provide substantial
contributions to the engineering-to-biology thesaurus.
Table 3 lists the OSU contribution of biological function
correspondent terms for the second version of the engineering-
5 Copyright © 2010 by ASME
Table 1. University of Toronto Functional Terms [42]
Class
Secondary
Bio
Correspondents
Export
Bind, block,
breakdown, excrete,
inactivate
Circulate, conduct,
diffuse, pump
Transfer
Communicate,
transduce
Channel
Guide
Synthesize,
transcribe
Couple
Extend, link,
overlap, stretch
Activate, bind,
project
Connect
Mix
Contract, exchange,
fragment
Control
Magnitude
Stop
Cover, destroy,
inhibit, surround
Convert
Convert
Decompose,
degrade, develop,
grow, mutate,
photosynthesize
Store
Convert, deposit,
photosynthesize
Provision
Breakdown,
concentrate, digest,
reduce
Develop, wrap
Support
Stabilize
Bind, connect
to-biology thesaurus. All but four of the Functional Basis
function terms have identified biological correspondents.
APPLICATIONS
The engineering-to-biology thesaurus was developed with
the intention of promoting collaboration between the biology
and engineering domains, resulting in discovery of creative,
novel ideas. The following subsections describe plausible
applications of the presented thesaurus, wh ich are summarized
in Figure 1. However, with few boundaries in th e field of
design, this thesaurus could be employed in ways the authors’
have not considered.
Searching for biological inspiration
Searching a natural-languag e corpus, such as a textbook,
for biological inspiration based on engineering functionality or
using engineering terms typically produces results that are
mixed. Results containing the search word often use the search
word out of context, not at all or in a different sense then the
designer intended. By utilizing the biological correspondent
terms of the thesaurus when searching for a specific function or
flow that solves the engineering problem, search results
improve [47] and become more focused on the desired
biological systems or phenomena.
Table 2. Indian Institute of Science Functional Terms
[38]
Class
Secondary
Tertiary
Bio
Correspondents
Separate
Free, detach, release
Remove
Evacuate
Branch
Distribute
Disperse, scatter,
spread, spray
Import
Consume, inhale, in
take, absorb, attract
Export
Repel
Transfer
Transport
Shift, displace, fly,
swim, jump, bounce
Translate
Slide
Channel
Guide
Rotate
Oscillate, spin, turn,
swivel, roll
Couple
Latch, lock
Join
Adhere, bond, fuse
Connect
Link
Clamp
Actuate
Activate, trigger
Regulate
Preserve, sustain,
remain, stabilize,
maintain
Increase
Grow, expand,
multiply
Decrease
Compress, coil,
divide, fold,
shorten, wrap
Change
Alternate, fluctuate
Stop
Halt, extinguish,
clog, seal, suspend
Control
Magnitude
Prevent
Constrain, obstruct
Store
Conserve, hold
Collect
Absorb, catch
Provision
Supply
Feed
Signal
Sense
Measure
Observe, monitor,
gauge, watch
Support
Cling, hold
Comprehension
Lopez-Huertas wrote that a thesaurus “…is thought of as a
way of easing communication between texts and users in order
to increase the interaction in information retrieval, and thus
facilitate information transfer” [43]. The engineering-to-
biology thesaurus has the potential to aid engineering designers
with the comprehension of biological contexts and facilitate
information transfer in two w ays; (1) direct translation of
biological text into engineering “speak” and (2) abstraction of a
biological system or phenomena in engineering terms.
Direct translation can be achieved by substituting
biological words that appear in the thesaurus with their
corresponding Functional Basis terms. Essentially, this will
rewrite the biological information in engineering “speak” and
increase the likelihood of a designer making connections
between the two sets of information and gaining inspiration as a
result. Many design methods rely on abstractions and
6 Copyright © 2010 by ASME
Table 3. Oregon State University Functional Terms
Class
Secondary
Bio Correspondents
Branch
Separate
Bleaching, meiosis,
replicate, mitosis,
segment, abscission,
electrophoresis, react,
dialysis, denature
Division, prophase,
metaphase, anaphase,
cleave, cytokinesis
Deoxygenated, filtrate,
deamination, liberate,
expulsion
Distribute
Exchange, circulate,
diffusion
Channel
Transfer
Migrate, transfer
Guide
Orient, position,
tunnel
Articulate
Connect
Couple
Recombination, mate,
build, phosphorylate,
bond, synthesis
Bind
Mix
Blend
Control
Actuate
Induce, trigger
Mag-
nitude
Regulate
Gate, electrophoresis,
respire, regulate,
organogenesis,
Hyperpolarize,
pinocytosis
Change
Pinocytosis, catalyze,
degrade, alter, bind,
contract, hydrolysis,
twist, slip, spread,
mutate, adiate,
charged, acclimatize
Attach
Decarboxylation,
constrict
Elongation, stretch,
attach, spread
Osmosis, constrict
Stop
Interphase
Repress
Convert
Convert
Polymerize, ionize,
synthesize, hydrolysis,
gluconeogenesis,
metabolize, glycolysis,
translation,
respiration,
photosynthesis,
fermentation, burn
Provision
Store
Absorb
Supply
Lactate1
Signal
Sense
Detect, locate, see,
smell
Indicate
Fluoresce, mark,
communicate, react
Process
Learn
Support
Stabilize
Homeostasis
Secure
Surround, envelope
1 www.designengineeringlab.org
describing an abstracted biological principle in engineering
terms is advantageous. Not only does it incr ease the likelihood
of a designer understanding the biological principle, but also it
lends itself to formulating connections between the biological
and engineering domains and easy comparison to other
abstractions. Efficient information retrieval through the
engineering-to-biology thesaurus allows an engineering
designer to cross into the biological domain and gain functional
knowledge without becoming overwhelmed by unfamiliar
biological systems and phenomena.
Functional modeling of biological systems
The engineering-to-biology thesaurus provides direction
when choosing the best-suited function or flow term to
objectively model a biological system. A wide range of
biological terms have been collected and placed into the
thesaurus, which can accommodate a designer when developing
functional models of well known to just introduced biological
systems. Functional modeling of biological systems allows
representation of solutions to specific engineering functions and
direct knowledge discovery of the similarities and differences
between biological and engineered systems, as viewed from a
functional perspective. The creation of engineered systems that
implement strategies or principles of their biological
counterparts without reproducing physical biological entities is
an additional benefit to biological functional models.
Concept Generation
Concept generation, manual or computational, aims to
generate several conceptual design variants. During this
process engineers draw on their prior knowledge, search design
catalogs, use a knowledge basis and in some cases search
patents [48-51]. Biology is another resource available to
engineers for design inspiration. Designers can use the terms of
the thesaurus to understand how nature removes for example.
From the biological correspondent terms one could relate the
terminology to prior knowledge or develop an analogy that
leads to design inspiration. Considering biological systems and
phenomena through generalized engineering terms allow
connections to be made b etween the domains, which facilitates
knowledge transfer. Therefore, biological information can be
used in function-based engineering design methods.
A computational method that ahs been pursued is the
population of a biomimetic design repository, which enables
the storage of biological knowledge indexed by engineering
function. Storing the biological information based on the
function the biological system or phenomena solves allows
quick access to principle solutions. There are a total of 30
biological entries in the OSU Design Repository1, 13 are
phenomena and 17 are systems (organisms) for this purpose.
The OSU Design Repository facilitates computational concept
generation and comparison of biological and engineered
components. The designer chooses from resulting
computational concept generator suggestions, engineered and
biological, to develop a complete conceptual design.
7 Copyright © 2010 by ASME
Collaboration, creation, discovery
Terms contained within the engineering-to-biology
thesaurus can be utilized for increasing creativity in
engineering designs and to discover connections between
biological systems and existing engineered systems and visa
versa. Formulating connections often requires an
interdisciplinary team to ensure the connection is properly
represented, whatever the mix of domains. Exploration of
biomimetic designs prompts collaboration between biology and
engineering researchers.
Figure 1. Engineering-to-Biology Thesaurus
Applications
APPLICATION EXAMPLE
In effort to demonstrate the versatility of the engineering-
to-biology thesaurus a comprehensive example exploring
sensing, or signal transduction, in bacteria is considered. This
example demonstrates the engineering-to-biology thesaurus
applications of comprehension and functional modeling. The
majority of biomimetic designs have been modeled after
physical biological phenomena that can be observed, or
experienced first hand as mimicking unseen phenomena, such
as activity at the cellular level, is more difficult. Biological
terminology often becomes narrow and requires more
knowledge of the subject. The following example serves as a
qualitative measure of the engineering-to-biology thesaurus to
show that this tool can assist with translating narrow biological
terminology into generalized engineering terms without
requiring the designer to learn deep biological knowledge. A
simple translation of what is the two component regulatory
system, the mechanism of sensing within b acter ia, is presented
to demonstrate comprehension. From the translated biological
information a functional model is derived.
The topic of signal transduction in prokaryotes explains
how bacteria sense their environment for survival. Signal
transduction occurs to alert the b acteria of stimuli via a two-
component regulatory system (TCRS) [52, 53]. Bacteria
respond to nutrients, synthesizing proteins involved in uptake
and metabolism, and non-nutrient signals both physical and
chemical [52, 53]. Signaling pathways in bacteria consist of
modular units called transmitters (sensor proteins) and receivers
(response regulator proteins), which comprise the TCRS.
Example bacterial processes that are controlled by TCRS are
chemotaxis, sporulation and osomoregu lation [52].
Tiaz and Zeiger explain bacteria employ TCRS to sense
extracellular signals as the following. Bacteria sense
chemicals in the environment by means of a small family of
cell surface receptors, each involved in the response to a
defined group of chemicals (hereafter referred to as ligands). A
protein in the plasma membrane of bacteria binds directly to a
ligand, or binds to a soluble protein that has already attached to
the ligand, in the perip lasmic space between the plasma
membrane and the cell wall. Upon binding, the membrane
protein undergoes a conformational change that is propagated
across the membrane to the cytosolic domain of the receptor
protein. This conformational change initiates the signaling
pathway that leads to the response.” - [52]
By manually identifying unclear biological terms and
substituting Functional Basis terms, the text exerpt above is
translated to: “Bacteria sense chemical energy in the
environment by means of a small family of cell surface
receptors, each involved in the response to a defined group of
chemicals (hereafter referred to as chemical energy). A protein
in the plasma membrane of bacteria joins directly to chemical
energy, or joins to a soluble protein that has already attached to
the chemical energy, in the periplasmic space between the
plasma membrane and the cell wall. Upon joining, the
membrane protein undergoes a conformational change that is
propagated across the membrane to the cytosolic domain of the
receptor protein. This conformational change initiates the
detection that leads to the response.”
Figure 2 provides a visual representation of the TCRS
sensing process; (A) Defining cellular boundaries and
substances present in bacteria; (B) Conformational change
sends a signal to cytosolic domain triggering the transmitter to
release protein phosphate; (C) phosphate binds to the receiver
initiating the output response. Abbreviations: T-Transmitter,
R-Receiver, ATP-Adenosine triphosphate, ADP-Adenosine
diphospahte, P-Phosphate. ATP and ADP are required to
initiate communications between the transmitter and receiver
proteins and phosphate is required to activate the receiver to
produce a response [52, 53].
Ligans are found in the thesaurus under material-solid-
object and chemical energy. In the case of TCRS, ligands are
utilized as chemical signals, thus chemical energy was the
chosen flow. Protein, an organic compound made of amino
acids arranged in a linear chain and folded into a globular form
[46], is synonymous with material-solid-liquid-mix, as is cell.
Bind was found under multiple classifications. Join was
8 Copyright © 2010 by ASME
chosen to represent binging of chemical energy and a solid-
liquid material. Binding causes detection of the stimulus
signal. Detection causes a status signal to be transferred to the
cytosolic domain, which causes the release of protein
phosphate. Communication is now initiated. Phosphate, which
under goes phosphorylation, acts as a control signal that is
transferred to the receiver protein to regulate and condition the
chemical energy within the bacterium to produce a response.
The two components of TCRS are transmitter and receiver
proteins, however, from a functional standpoint chemical
energy is needed to join with and change the bacterium material
to elicit a response. The textual and diagrammatic abstractions
of TCRS can now be utilized for developing connections
between biology and engineering. A functional model of
TCRS in bacteria is shown in Figure 3.
Figure 2: Method of sensing extracellular signals with
TCRS in bacteria
CONCLUSIONS
The natural world provides numerous cases for inspir ation
in engineering design. From simple cases such as hook and
latch attachments to articulated-wing aircrafts, nature provides
many sources for ideas. Though biological systems provide a
wealth of elegant and ingenious approaches to problem solving,
there are challenges that prevent designers from leveraging the
full insight of the biological domain. Biologically-inspired
designs require that designers have knowledge of previous
design solutions during engineering design activities. The
learned representations from the decomposition of design
solutions, engineered and biological, organized at different
levels of abstraction allow connections to be discovered with
cues taken from each level. This paper presented an
engineering-to-biology thesaurus that (1) lessens the burden
when working with knowledge from the biological domain by
providing a link between engineering and biological
terminology; (2) assists designers with establishing connections
between the two domains to facilitate biologically-inspired
designs; and (3) lists biological correspondent terms that an
engineering designer interested in function-based design might
encounter.
The version of the thesaurus presented in this paper
represents an integration of three independent research efforts,
which include research from Oregon State University, the
University of Toronto, and the Indian Institute of Science, and
their industrial partners. This research is a work-in-progress
and is not a comprehensive list of all biological terms; however,
it is among the first steps to bridging the gap between the
biology and engineering domains. The overall approach for
term integration and the final results are presented. Through
this research, biological function and flow correspondent terms
were mapped to engineering terms and placed into pre-
determined classifications set by the Functional Basis structure.
It was observed that the majority of biological flow
correspondent terms are grouped at the tertiary level, whereas
biological function terms are primarily grouped at the
secondary level.
Implications of the proposed thesaurus on the engineering
and biology communities were explored. Signal transduction in
bacteria was analyzed as a comprehensive example that
demonstrates the engineering-to-biology thesaurus applications
of comprehension and functional modeling. Breaking down a
biological solution into smaller parts, based on functionality,
allows one to liken a biological system or phenomenon to an
engineered system for ease of understanding and transfer of
design knowledge. We believe the thesaurus will enable the
engineering and biology communities to better collaborate,
create and discover in the future. Furthermore, the engineering-
to-biology thesaurus is a subject domain oriented, intermediary
structure, which can be updated as needs are identified.
FUTURE WORK
Future work for improving the engineering-to-biology
thesaurus includes examining potential terms through clustering
and analyzing terms contained within the glossary of a
collegiate entry-level biological textbook. While collocated
terms provide an indication for macrorelevant terms, clustering
analysis could be utilized to find less obvious, but equally
important, biological terms for thesaurus population.
Additionally, biological texts that focus on a topic of interest
(i.e., insects, fungi) should be analyzed for relevant biological
terms that an introductory text may not in clude.
9 Copyright © 2010 by ASME
Figure 3: Functional Model of TCRS in Bacteria
Future work for the adoption of biologically-inspired
engineering design involves integration of the thesaurus terms
into computational concept generation software. This will
enable a greater number of biological organisms, strategies and
phenomena that achieve desired functionality to be found
during concept generation. Thereby increasing the likelihood
of biomimetic engineering solutions.
ACKNOWLEDGMENTS
The authors would like to thank Dr. Brownell for his time
and effort in the validation of the thesaurus terms. This
material is based in part upon work supported by the National
Science Foundation under Grant CMMI-0800596. Any
opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not
necessarily reflect the views of the NSF.
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11 Copyright © 2010 by ASME
ANNEX A
ENGINEERING-TO-BIOLOGY THESAURUS
Tertiary
Biological Function Correspondents
Bleaching, meiosis, abscission, mitosis, segment, electrophoresis, dialysis,
denature, free, detach, release
Divide
Division, prophase, metaphase, anaphase, cleave, cytokinesis
Remove
Deoxygenate, filtrate, liberate, expulsion, evacuate
Circulate, diffusion, exchange, disperse, scatter, spread, spray
Absorb, attract, consume, inhale, intake
Bind, block, breakdown, excrete, inactivate, repel
Migrate, transfer
Transport
Circulate, conduct, diffuse, pump, shift, displace, fly, swim, jump, bounce
Transmit
Communicate, transduce
Orient, position, slide, tunnel
Translate
Synthesize, transcribe
Rotate
Oscillate, spin, turn, swivel, roll
Allow DOF
Articulate
Recombination, mate, build, phosphorylate, bond, synthesis, latch, lock, extend,
link, overlap
Join
Bind, adhere, bond, fuse
Link
Clamp, activate, bind, project
Blend, contract, exchange, fragment
Activate, induce, trigger
Electrophoresis, gate, o rganogenesis, respire, sustain, preserve, rem ain,
stabilize, maintain, regulate
Increase
Hyperpolarize, pinocytosis, grow, expand, multiply, replicate
Decrease
Compress, coil, divide, fold, shorten, wrap
Pinocytosis, degrade, alter, bind, catalyze, contract, hydrolysis, twist, mutate,
radiate, charged, slip, acclimatize, alternate, fluctuate
Decrement
Decarboxylation, constrict
Shape
Elongate, stretch, attach, spread
Condition
Osmosis, constrict
Clog, extinguish, halt, interphase, seal, suspend
Prevent
Constrain, obstruct
Inhibit
Cover, destroy, inhibit, repress, surround
Polymerize, synthesize, burn, gluconeog enesis, metabolize, grow, transduction,
fermentation, glycolysis, hydrolyze, hydrolysis, respiration, ionize, decompose,
degrade, develop, mutate, photosynthesize
Conserve, hold, convert, deposit, photosynthesize
Contain
Absorb
Collect
Absorb, catch, breakdown, concentrate, digest, reduce
Feed, lactate
Detect
Detect, locate, see, smell
Measure
Observe, monitor, gauge, watch
Fluoresce, communicate, react, mark
Learn
Develop, wrap
Homeostasis, cling, hold, bind, connect
Surround, envelope
Overall increasing degree of specification
12 Copyright © 2010 by ASME
Class
Secondary
Tertiary
Biological Flow Correspondents
Material
Human
Being, body
Gas
Oxygen, nitrogen, chlorine
Liquid
Acid, chemical, water, blood, solution, b ase, buffer, fluid, plasma
Solid
Object
Fiber, body, substrate, microfilament, microtubules, structure, chain, organ,
nucleus, tissue, muscle, cilia, flagella, tube, v ein, heart, plant, ribosome, somite,
apoplast, stem, kidney, egg, ovary, leaf, embryo, b acteria, chlo roplast, carbon,
sperm, g lucagons, adipose, angiosperm, meristems, m ineral, stoma, shoot, seed,
capillary, recepto rs, hair, bone, tendon, neuron, sporangium, photoreceptors,
mechanoreceptors, chromosome, petiole, lysosome, archaea, cone, strand,
centrio le, spore, zygote, sulfur, lipoprotein, nephron, hyphae, plasmodesma,
conifer , plasmid, plastid, xylem, pigment, sperm, hippocampus, phloem
Particulate
Cytokinin, pyruvate, nicotine, opium, glycerol, carotenoid, , GTP, ATP, urea,
RNA, tRNA, mRNA, DNA, glucagon, parathormone, cryptochromes, ligand,
promoter, gene, exon, intron, molecule, enzyme, lipid, hormone
Composite
Enzyme, virus, ribosome, p rokaryote, macromolecule, polymerase, nucleotide,
polypeptide, organelle, symplast, mesophyll, brood, codon, messenger, DNA,
RNA, cytoplasm, organ, tissue
Mixture
Gas-gas
Air
Liquid-liquid
Hormone, melatonin, thyroxine, calcitonin, thyrotropin, estrogen, somatostatin,
cortisol, glu cagon, adrenocrticotropin, testosterone, auxin, insulin, intracellular
fluid, extracellular fluid, spinal fluid, poison, urine, peptide, solutio n, steroid
Solid-solid
Adenosine, glomerulus, blastula, monosaccharide, membrane, phosphate,
ribosome, centrosomes
Solid-Liquid
Algae, synapse, peptidoglycan, cell, glia, phytochrome, retina, protein,
repressor, hemoglobin, blood, membrane, bacterium
Signal
Status
Change, variation, lateral, swelling, catalyzed, translation, exposed, active,
separated, cycle, formation, reaction, redox, d eficient, saturated, diffusion,
broken, hybridization, orientation, resting, cue, magnetic, volume, under,
organized, fruiting, fatty, anaphase, metaphase, prophase, conjugation,
osmolarity, senescence, signal, allele
Auditory
Sound
Olfactory
Smell
Tactile
Pain
Taste
Gustation
Visual
Length, shortened, long, dark, full, double
Control
Place, inhibit, release, excrete, development, match, induce, digest, integrate,
translation, transduction, equilibrium, grown, splice, capture, distribute,
phosphorylation
Analog
Binding, center, synthesis, photosynthesis
Discrete
Flower, translocation
Energy
Human
Acoustic
Echolocation, sound wave
Chemical
Calorie, metabolism, glucose, glycogen, ligand, nutrient, starch, fuel, sugar,
mitochondria, lipid, gibberellin
Electrical
Electron, potential, feedback, charge, field
Electromagnetic
Optical
Light, infrared
Solar
Light, sun, ultraviolet light
Hydraulic
Pressure, osmosis, osmoregulation
Magnetic
Gravity, field, wave
Mechanical
Muscle contraction, pressure, tension, stretch, depress
Rotational
Translational
Pneumatic
Pressure
Thermal
Temperature, h eat, infrared, cold
Overall increasing degree of specification
... 4 "TS" stands for "topic" and "PY" stands for "published year" in the Web-of-Science search query. 5 The final literature set includes these references: [4][5][6][7]13,[15][16][17][18][19]30,41,50,[53][54][55][56][57][58][59][60][62][63][64][65][66][67][68][69][70][71][72][77][78][79][80][81][82][83][84][85][86][87][88][89]91,92]. ...
... In recent years, many efforts have been made to develop data-driven methods, tools, and databases for BID. Cheong et al. [77] and Nagel et al. [78] developed the engineering-to-biology thesaurus by mining meaningful keywords from biological text aligned with the engineering functional terms in the functional basis. The thesaurus serves as the basis for engineers to find biological analogies and identify the functional reasoning linking two domains when solving design problems. ...
... In this case, the technological and engineering semantic networks (e.g., TechNet and B-link) that were recently developed based on engineering data can serve as infrastructure to support broad DbA studies. Expert system Heuristic rules-based strategy [53,56,57,58,60,65,67,77,78,79,80,87,91] Regarding the other methods, clustering algorithms have been used to represent design data by categories and facilitate the retrieval process [7,41,72]. Vector Space Method represents the original data as a vector of subitems, which also benefits both the encoding and retrieval subphases [5,15,50,84,85]. ...
Preprint
Full-text available
Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.
... In recent years, many efforts have been made to develop data-driven methods, tools and databases for BID. Cheong et al. [62] and Nagel et al. [63] respectively developed the engineering-to-biology thesaurus by mining meaningful keywords from biological text aligning with the engineering functional terms in the Functional Basis. The thesaurus serves as the basis for engineers to find biological analogies and make the functional reasoning between two domains when solving design problems. ...
... Copyright © 2021 by ASME V002T02A028-6 [10,75] Recurrent Neural Network (RNN) [39] Bi-directional Recurrent Neural Network (Bi-RNN) [48,73] Long short-term memory (LSTM) [47] Clustering Agglomerative Hierarchical Clustering (AHC) [28] Relevance score-based clustering (RSC) [7,57] Classification Support Vector Machine (SVM) [47,66] Naive Bayes (NB) [ [68,69] Probability analysis Dijkstra's shortest path algorithm [40] Bayesian inference approach [57] Expert system Heuristic rules-based strategy [38,39,67,71,72,77,41,42,48,52,58,[62][63][64] The rapid advancements of deep learning-based AI techniques may provide many powerful tools for data-driven DbA. For example, much research has been done on visual and semantic analogy question solving [86,[89][90][91][92] by discovering an extendable mapping from an image and word pair and then applying it to another image and word pair to find analogies. ...
Conference Paper
Full-text available
Design-by-Analogy (DbA) is a design methodology that draws inspiration from a source domain to a target domain to generate new solutions to problems or designs, which can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. Herein, we survey the prior data-driven DbA studies and categorize and analyze individual study according to the data, methods and applications in four categories including analogy encoding, retrieval, mapping, and evaluation. Based on such structured literature analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field.
... MBE [11] (multi-biological effects) is another method that integrates the idea of effects in TRIZ, biological coupling [42] and BID, and it is able to support design tasks that involve multiple functional requirements [43]. MBE was proposed for integrating biological strategies into engineering design, and it contains several methods and tools that inherit the characteristics of TRIZ, biological coupling and systematic product design theory for engineering designers [44]. ...
... Therefore, useful functions that have been trimmed are used as keywords to search for feasible biological prototypes. During the search for solutions, taxonomy (such as an engineering-to-biology thesaurus [43]) can help to overcome barriers in terminology between engineering and biology. The steps to determine the search keywords are as follows: ...
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The application of design knowledge determines the innovativeness of a technical scheme obtained by trimming (a tool for problem analysis and solving in TRIZ). However, limitations in the knowledge, experience and expertise of designers constrain the range of design knowledge that they can apply, thus reducing the effectiveness of trimming. In this paper, biological strategies are introduced to the trimming process to compensate for limitations imposed by the insufficient professional knowledge of designers, thereby improving design innovation. Therefore, this paper proposes a new design method that combines the trimming method and bio-inspired design (BID). First, a trimming analysis of the target system is carried out. Taking the missing functions of the trimmed system as a potential breakthrough point, a keyword search mode based on “V(verb)O(object)P(property) + the effect/features of the associated function” is used to search for biological prototypes in the biological knowledge base. Second, a fuzzy comprehensive evaluation method is used to analyze the biological prototypes from three dimensions, namely, compatibility, completeness and feasibility, and the best-matching biological prototype is selected. Finally, the biological solution is transformed into an engineering design scheme through a resource derivation process based on structure–function–attribute analogies. The proposed method can expand the range of design solutions by adding biological strategies as a new resource to solve trimming problems. The feasibility and effectiveness of the method are verified by redesigning a steel tape armoring machine.
... The terminologies obtained need to be filtered before retrieving biological entities, as some candidate terminologies may not be biological terminologies. The current most commonly used method for filtering biological terminologies is to use biological dictionaries to identify biological terms [45], such as the Oxford American dictionary [46], Henderson's dictionary of biological terms [47], the Oxford Dictionary of Biology [48], and many others. However, these methods can be very inefficient in filtering candidate biological terminologies, if only the meanings of biological terminologies can be obtained. ...
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... Cheong et al. [77] and Nagel et al. [78] developed the engineering-to-biology thesaurus by mining meaningful keywords from biological text aligned with the engineering functional terms in the functional basis. The thesaurus serves as the basis for engineers to find biological analogies and identify the functional reasoning linking two domains when solving design problems. ...
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Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.
... Studies supporting the abstract functional features of biological prototypes have been developed to facilitate the search for appropriate biological analogies. These tools and methods involve the automatic keyword extraction mechanism, facilitated by the natural language analysis [70]; the Engineering-to-biology thesaurus [71] that bridges the difference between terminologies used in biological and technological functions; and the Biomimicry Taxonomy, which abstracts biological information in terms of the three-level granular function [13]. ...
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Due to the complexity of contemporary technology, product and system design efforts often require intensive organization and communication within teams; the design venture must accordingly be carefully planned and systematically executed, integrating the various aspects of the design process into a logical and comprehensible whole. The present comprehensive and systematic treatment of this methodology proceeds by clarifying the design task, establishing the function structures of a conceptual design, and finally determining the definitive layout embodying the design. Illustrative examples of actual product design processes and their results are presented and evaluated.