ArticlePDF Available

A Shared Framework of Reference, a First Step Toward Engineers’ and Biologists’ Synergic Reasoning in Biomimetic Design Teams

Authors:

Abstract and Figures

www.novatechsetproofs.com/authors/ASME/MD-20-1040.pdf Biomimetic practice requires a diverse set of knowledge from both biology, and engineering. Several researchers have been supporting the integration of biologists within biomimetic design teams in order to meet those biological requirements and improve the effectiveness of biomimetic processes. However, interdisciplinarity practices create well-known communication challenges. Based on functional representations (like SAPPhIRE or FBS), several approaches to model biological information have been investigated in the literature. Nonetheless, actual communication processes within interdisciplinary biomimetic design teams are yet to be studied. Following this research axis, this publication focuses on communication noises and wonders if a shared framework of reference can be defined to improve communication between biologists and engineers? Through the comparison of processes and graphic representations between biology and engineering design, a set of guidelines is defined to structure a shared framework of reference. Within this framework, a new tool referred to as LINKAGE, is then proposed to assist interdisciplinary communication during the biomimetic process.
Content may be subject to copyright.
Eliot Graeff
Arts et Metiers Institute of Technology, LCPI,
HESAM Université
,
F-75013 Paris, France
e-mail: eliot.graeff@ensam.eu
Nicolas Maranzana
Arts et Metiers Institute of Technology, LCPI,
HESAM Université
,
F-75013 Paris, France
e-mail: nicolas.maranzana@ensam.eu
Améziane Aoussat
Arts et Metiers Institute of Technology, LCPI,
HESAM Université
,
F-75013 Paris, France
e-mail: Ameziane.Aoussat@ensam.eu
A Shared Framework of
Reference, a First Step Toward
Engineersand Biologists
Synergic Reasoning in
Biomimetic Design Teams
Biomimetic practice requires a diverse set of knowledge from both biology and engineering.
Several researchers have been supporting the integration of biologists within biomimetic
design teams in order to meet those biological requirements and improve the effectiveness
of biomimetic processes. However, interdisciplinarity practices create well-known commu-
nication challenges. Based on functional representations (like SAPPhIRE or function beha-
vior structure (FBS)), several approaches to model biological information have been
investigated in the literature. Nonetheless, actual communication processes within interdis-
ciplinary biomimetic design teams are yet to be studied. Following this research axis, this
publication focuses on communication noises and wonders if a shared framework of refer-
ence can be dened to improve communication between biologists and engineers? Through
the comparison of processes and graphic representations between biology and engineering
design, a set of guidelines is dened to structure a shared framework of reference. Within
this framework, a new tool referred to as LINKAGE is then proposed to assist interdisciplin-
ary communication during the biomimetic process. [DOI: 10.1115/1.4047905]
Keywords: cognitive-based design, collaborative design, conceptual design, design teams,
multidisciplinary design and optimization
1 Introduction
From Leonardo Da Vinci, to Otto Schmitt, to Benyus [1],
bio-inspiration, or looking at living organisms for inspiration, has
been an approach of increasing interest in the scientic community.
Biomimetics, as the technical prism focusing on bio-inspiration, is a
crucial element in the spread and implementation of these new prac-
tices. Biomimetics is dened as the interdisciplinary cooperation
of biology and technology or other elds of innovation with the
goal of solving practical problems through the function analysis
of biological systems, their abstraction into models and the transfer
into and application of these models to the solution[2].
If the potential of biomimetics has been proven over the past
decades [3,4], it struggles to become an innovation strategy of refer-
ence. Scientic literature underlines interdisciplinary communica-
tion as one of biomimeticsmain challenges [59]. Following a
literature review on biomimetics and communication theories, a
set of impediments to communication are identied and analyzed
in the context of interdisciplinary biomimetic design teams.
This paper then investigates communication challenges between
biologists and engineers and wonders if a common framework of
reference can be dened to support communication between biolo-
gists and engineers in interdisciplinary biomimetic design teams?
To answer the research question, we compared engineering
design and biology regarding processes and representations. The
results of this comparative analysis are formalized as guidelines
building a shared framework of reference. Within this framework,
a tool referred to as LINKAGE is created and its structure and
characteristics are presented. Relying on the abovementioned
guidelines, LINKAGE aims at guiding the biomimetic practice
while assisting interdisciplinary communication.
This study is made up of two phases. First, the theoretical part
leads to the formalization of the common framework of reference
and the design of LINKAGE. Second, the practical part presents
the tools development in the form of an open-access website
along with its evaluation through assessments by experts and work-
shops with professionals. Where this publication describes the the-
oretical part, it does not address the practical part.
2 State of the Art
This section presents the current methodological approaches sur-
rounding biomimetics and focuses on communication issues. For
information on the historical development and legitimacy of the
biomimetic design approach, we refer the reader to thorough
reviews of the literature [10,11].
2.1 Biomimetic Methodologies. Biomimetic processes are of
two types. Either the design phase occurs after a biological discov-
ery, which leads to a new product often with a high added value: the
biology push approach [2], or biomimetics operates as a
problem-solving process: the technology pull approach [2]. The
latter is the core of engineering design processes in industry.
Thus, numerous research projects have been investigating its meth-
odological framework.
The literature describes more than 15 processes allowing the
implementation of a technology pull approach [12], the procedural
model of doing bionics [13], the biomimetic design methodology
[14], the problem-driven analogical process [15], the BID process
[16], etc. Overall, according to Fayemi, biomimetic processes can
be described with eight main steps synthesized into a unied tech-
nology pull biomimetic process [12]. A recent study optimized the
Contributed by the Design Theory and Methodology Committee of ASME for
publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 24,
2020; nal manuscript received June 23, 2020; published online October 12, 2020.
Assoc. Editor: Daniel A. McAdams.
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-1Copyright © 2020 by ASME
unied technology pull process for interdisciplinary teams and risk
management (Fig. 1)[17].
To perform these different steps, practitioners rely on tools.
According to Wanieck et al. [8], these tools can be specic to a bio-
mimetic process, and as a result were designed for biomimetic pur-
poses only, like BioTRIZ [18], or they can be adapted from
engineering, such as 5-Whys [19], or biology, for instance, 16 pat-
terns of nature [20].
We must specify that the tools that are said to come from biology
are neither biological tools nor tools designed to be used by biolo-
gists. They have been mainly designed for engineers to learn about
biological ndings.
Despite those methodological contributions, biomimetics fails to
spread and to reach its full potential.
2.2 Methodological Limitations. Vattam and Goel [21] and
Kruiper et al. [22] summarized three main issues designers currently
face during biomimetic projects:
Findability, that we may interpret as how can design teams nd
suitable biological data for each, or at least most, of their
design projects?
Recognizability, that we may interpret as how can design
teams identify, among the incredible diversity of living
beings, which organisms are relevant for a given design
problem?
Understandability, that we may interpret as how to overcome
the lack of biological knowledge of design teams during the
steps involving biology (step 3 to 7 of the unied process)?
Consequently, the gap between the engineering and biological
elds (knowledge, reasoning, data structure, etc.) is one of the
main issues. In previous work, we drew a distinction between two
types of biologists. Vertical biologiststo refer to biologists who
punctually interact with design team and have an in-depth expertise
in a speciceld (i.e., a vertical knowledge) identied as research-
ers, and Horizontal biologistto refer to integrated teammate
having a broad training in biology (i.e., a horizontal knowledge)
associated with a cross-domain thinking [17,23].
Numerous processes suggest interacting with vertical biologists
[13,16,22,24,25] but only a few prescriptive processes, in the
sense of Wynn and Clarkson [26] and Gericke and Blessing [27],
describe the practice of an interdisciplinary team composed of biol-
ogists and engineers [28,29]. As stated by McCardle et al.,
Promote best practices for interdisciplinary working, ensuring
biologists, life science specialists and experts are effectively
involved with design teams and technologists. This is a major
part of biomimicry practice and training but is not a standard
approach with many industries or universities[7].
A fundamental contradiction thus emerges. Facing these chal-
lenges, the need to further integrate biologists is underlined in the
literature [30,31], but the methodological framework (process and
tools), has not been originally designed to include these unusual
proles. In other words, methodological research on engineering
design have been focusing on the process-centered aspects (the
how?) but have not been much investigating the profession-
centered aspects yet (the who?).
In addition to the inadequacy between process-centered and
profession-centered framework, interdisciplinary teamwork itself
is challenging. As presented in Sec. 1, poor communication
between actors having different backgrounds appears as one on
the main obstacles. Helms et al. explain these obstacles as follows
biologists and engineers typically speak a very different language,
creating communication challenges,”“they typically use different
methods of investigation and often have different perspectives on
design[32]. Fayemi also identies three explanations Their dif-
ferent backgrounds lead to divergent disciplinary or functional
understanding of a concept, whether due to perception, languages,
or thought styles.[12].
To guide our reasoning, Sec. 2.3 presents the foundations of com-
munication theories.
2.3 Communication Theories as a Prism to Consider
Biomimetic TeamsIssues
2.3.1 Origins and Related Concepts. Also known as the infor-
mation theory, the rst theory of communication was proposed by
Shannon in 1948 [33]. This theory describes several fundamental
concepts surrounding communication to quantify a coded informa-
tion through statistical analysis. According to this theoretical cor-
nerstone, communication systems comprise ve elements:
An information source, which is the origin of a message or
group of messages to be transmitted. These messages represent
the information of interest as understood by the source.
A transmitter, which converts the message into a signal
(encoding) suitable for a given channel of transmission.
A channel, the medium which transmits the signal.
A receiver, which interprets the signal to reconstruct the
message (decoding).
A destination, the entity to whom the message is intended.
A sixth transversal element, the noise source,modies the
signal at its emission, during its transmission or at its reception,
altering the encoded message. Shannons theory presents communi-
cation with a technical standpoint which leads to a model that
cannot be generalized to all the system, people debating for
example. Following this seminal theory, several researchers pub-
lished additional contributions and optimizations.
In particular, Berlo extends the model to the communication
between two people [34], Schramm integrates feedback along
with the crucial role of context [35], and Devito characterizes
four types of noise: physical noise, physiological noise, psycholog-
ical noise, and semantic noise [36].
Altogether, these studies constitute a representative part of the
conceptual foundations on communication.
2.3.2 Communication Theory Applied to a Biomimetic Team.
Applied to interdisciplinary design teams, these theories can
be used to question the emergence of communication issues.
Figure 2presents a communication model applied to an engineer
(source/receiver) and a horizontal biologist (source/receiver)
talking (channel/signal) about a biological solution (message/
feedback).
According to the communication theories, a lot of interrelated
factors can prevent a good communication: noises caused by a
lack of common context (commonness, social and cultural frame-
work of reference, etc.), source/receivers defaults (poor communi-
cation skills, attitudes, knowledge, etc.), a badly encoded/decoded
message (wrong code, ill-structured), etc.
Fig. 2 Communication theory applied to a biomimetic design
team (based on Shannon model and communication theories)
Fig. 1 Unied technology pull biomimetics process optimized
for risk management
041402-2 / Vol. 143, APRIL 2021 Transactions of the ASME
The different backgrounds of the actors can be associated with
several of these issues. First, because of the distance between biol-
ogistsand an engineershabits, know-hows and knowledges, their
point of view, and cognitive reasoning vary. This diversity induces
a lack of common cognitive reasoning, also called cognitive disso-
nances.For example, engineers are trained to design solution
through a prescriptive approach and biologists are trained to break-
down and describe phenomena through a descriptive approach, ulti-
mately leading to different considerations when dealing with a
problem as described by Bogatyrev and Bogatyreva [37].
Moreover, semantic noises are likely to occur. The most obvious
example here is the linguistic gap between engineers and biologists.
Depending on the sources and receivers backgrounds, a word
might also carry several pieces of information. If a biologist talks
about photosynthesis,the concept associated with this word is
probably much more complex than what might be understood by
an engineer. The biologist then assumes the engineers degree of
knowledge on associated concepts, which can lead to loss of
information.
Furthermore, since the background impacts the process of encod-
ing, the types of signal can signicantly vary. Among other signals,
engineers transmit information in the form of requirements speci-
cations, equations, technical schematics, etc. These forms of encod-
ing might be unknown by biologists and so not easily decoded. The
receiver will detect a signal but will wonder how to deal with it.
Finally, team members may reason on different frameworks of
reference regarding the projects objectives. If they do not share
the same mental model of constraints, expectations and goals, the
overall project is likely to fail [38].
As our study focuses on the methodological aspects, the source/
receivers defaults, like researchersbad communication skills, are
not addressed.
To summarize, interdisciplinary teams need guidance on seman-
tic and psychological noises to allow actors to keep their specici-
ties while communicating efciently.
2.4 Lack of Communication, a Challenge for Biologists
Integration. The inability to communicate efciently is as a well-
known issue in interdisciplinary teams [39] and the integration of
biologists in biomimetic teams is not exception to the rule.
As presented in Sec. 2.3.2, linguistic differences (semantic noise)
leading to vocabulary shortcomings or misunderstanding are widely
recognized and easily understandable issues. In the biomimetic lit-
erature, various publications deal with semantic divergences
through a data-centered prism.
Arst set of studies focuses on semantic issues during the search
for biological information by engineers. To support this search
phase, Cheong et al. identied biological meaningful keywords
and associated them with engineering concepts through computa-
tional tools leading to search algorithms [40]. On the same axis,
Nagel et al. established an engineering-to-biology thesaurus
which aims at providing a list of synonymous biological terms to
the generalized engineering terms of the Functional Basis modeling
lexicon.Applications are oriented toward both the search and the
analysis of biological data without having an extensive back-
ground in biological knowledge[41]. Where this tool has been
evaluated by an expert in biology, it is designed to assist engineers
during their manipulation of biological data. Likewise, the Bio-
mimicry Institute published the Biomimicry Taxonomy [28], a
tool co-designed with biologists to help engineers formulate bio-
logical requests from functional concepts. The aim of this tool
is to support the search for biological data in the AskNature data-
base [42].
Finally, at a more abstract level, Vincent studied the formaliza-
tion of networks of concepts in an ontology [43] with the aim of
building a computational tool supporting searches based on trade-
offs [44].
According to the concepts presented in Sec. 2.3, these contribu-
tions deal with the format of signal (suitable for biology or
technology) and its encoding/decoding. The nal goal is to
improve the effectiveness of engineers(destination) search in data-
bases or ontology (structured sources) or on the web (non-
structured source).
Another part of the literature studies the representation of biolog-
ical systems through causal models. The aims of these approaches
are to enhance understandability through modeling, improve nd-
ability by linking biological models with technical models and to
generate databases of structured biological information. The rst
type of representation is functional model. These models can be pre-
existing and adapted for biomimetics purposes, such as the function
behavior structure (FBS) model for the DANE database [45], or
format specically designed for biomimetic, like the State change,
Action, Parts, Phenomenon, Input, oRgan, Effect (SAPPhIRE)
model for IDEA-INSPIRE [46]. Both constructs aim at identifying
causalities between elements to explain functional properties. The
second type of model is ow-based functional model. Nagel et al.
designed such model, presenting the ows of energy, matter, and
signal between generic functional sub-systems based on the previ-
ously presented thesaurus (e.g., Export, Store, etc.) [24]. Models
are then stored in the Design Repository. In the case study presenting
the approach, Nagel indicates that the model was discussed with an
expert in biology but the contribution of the model on communica-
tion itself is not analyzed. The last model, BABELE, proposed by
Fayemi et al. [23] aims at combining the theoretical approach of
biology, in particular the concept of autopoiesis [47], systemics,
through the Millers Living System Theory [48] and engineering,
using TRIZ [49]. The model tries hard to allow people, without
any specic biological knowledge, to arrange by relevancy the
identied biological systems on their own.As specied by
Fayemi, it is a tool made for designers with limited biological
knowledgeand to delay biologistsintervention.
According to the concepts presented in Sec. 2.3, these contribu-
tions deal with the implementation of generic signalsformats to
support message encoding/decoding and data storage in an articial
destination (databases or Design Repository).
Thus, a lot of work has been done on linguistic models to reduce
the gaps between biology and engineering. However, in all these
approaches, the common idea is to make engineers able to represent
biological information in an engineering framework of reference,
not to improve communication between biologists and engineers
sharing the same project. As previously described, interdisciplinary
communication suggests various actors from different elds acting
as sources/transmitter, and receiver/destination which is not the case
in the abovedescribed studies.
The second noise identied in Sec. 2.3.2 is the lack of common
context (psychological noise). This communication brake focuses
on the conceptual framework (commonness, scientic and cultural
framework of reference) used as a prism to decode the signal and
interpret the message. This type of impediment is particularly chal-
lenging with strong background differences. For instance, between
researchers and industrial stakeholders since they have different
norms, standards, or values[50].
Biomimetics is based on an analogical transfer of biological solu-
tions into technological ones. As described in the literature, this ana-
logical thinking is performed based on relations rather than feature
[51] and so on interconnected systems rather than isolated ones
[52]. To summarize, analogies are dealing with systems embedded
within a eld-related conceptual network. As a result, practitioners
having a different background are unconsciously referring to dis-
tinct framework while trying to communicate. Where this variabil-
ity of prisms represents an asset of interdisciplinary collaboration, it
also leads to impediments preventing proper communication.
Based on this divide, Helms developed the four-box method, and
the associated T-chart [53]. This tool aims to clarify design prob-
lems and biological analogues through contextual information.
The Four-Box method is structured following the four most fre-
quently occurring descriptive elements of problem formulation.
extracted from studentsprojects [53]. Through the denition of
the problems framework, solution search is supported, and
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-3
analogical biological solutions can be compared through both their
features (Functions, Specications, and Performance Criteria) and
their interacting environments (Operational Environment). The
case study presenting the tool is performed by an interdisciplinary
student team but on the communication part, biologists report
nding the tool more useful for communicating knowledge about
biologically systems to their peers.[53], suggesting an intradisci-
plinary interest.
According to the concepts presented in Sec. 2.3, this contribution
deals with the implementation of a generic signal format suitable for
both biology and technology, as well as improving encoding/decod-
ing through contextual data. The nal goal is to improve the effec-
tiveness of the search and selection steps.
Finally, the communication between biologists and engineers in
biomimetic teams has been studied by Hashemi Farzaneh et al.
[54] following a rather similar approach than the one we have
used in this article. Through the description of features identied
in representations from biology and product design, a graphic repre-
sentation is suggested to improve communication between engi-
neers and biologists. This article then follows the same research
axis and considers alternative guidelines from the comparison of
both processes and representations.
According to the concepts presented in the previous section,
these contributions deal with the channel of communication
(visual communication) and the format of encoding (graphic
features).
Following the observations made on the scientic literature, the
communication theories and the current approaches dealing with
interdisciplinary gaps in biomimetics, Sec. 3presents the research
question and the hypothesis developed in this publication.
3 Research Question and Hypotheses
Biomimetics holds great potential but still has not reached its full
potential as it struggles to spread. Biology-centered steps are iden-
tied in the literature as particularly challenging steps. To close the
gap between the biological and engineering world, numerous arti-
cles recommend the integration of biologists. However, this
approach is not without its own obstacles and communication
between engineers and biologists appears as a crucial lever to con-
sider. Several types on noises can be identied to characterize the
communication brakes to solve.
First, the semantic noises. On that matter, the formalization of the
data from biology has been the center of attention in knowledge
transfer through tools but little research has been targeting commu-
nication itself.
Moreover, we would argue that semantic noises are crucial given
that horizontal biologists are not integrated enough within design
teams. We assume that a few weeks of shared work would be the
best solution to assimilate specic concepts and create shared
semantics. Once operational, horizontal biologists should act as a
bridge between technical and biological experts. Where semantic
tools can be a fundamental rst step in communication, the stability
of the teams composition between projects and so to maintain
social bonds between actors [50] appears as a long-lasting strategy.
A second type of noise, psychological noises, more specically
cognitive dissonances (the lack of common framework of reference
and message structure) also plays a part in communication issues
within interdisciplinary teams. Some previous work focused on
cognitive dissonances but overall this research axis remains
mainly unexplored.
As a result, this article deals with biologistsand engineersways
of reasoning and structuring information, as they witness their
respective processes of converting message into signal. The
research axis of this article is to question how to improve commu-
nication within interdisciplinary design teams, and more speci-
cally, we wonder if we can dene a common framework of
reference to support communication between biologists and engi-
neers in interdisciplinary biomimetic design teams?
The hypothesis of this article is that such a framework of refer-
ence would reduce cognitive dissonances, improving the ability
of the team to communicate. Based on guidelines extracted from
the comparison of visual representations and cognitive frameworks
used in both elds, a framework is built. Within this framework, a
tool is then designed to guide the reasoning and the generation of
shared mental models to improve teams effectiveness [55]. The
underlying assumption here is that cognitive dissonances represent
an issue only if they disturb each other by overlapping. If cognitive
dissonances are structured as complementary approaches then they
represent the added value of these stakeholders, the abovemen-
tioned variability of prisms, and therefore the core of biomimetics
reasoning. We assume that solving the lack of common framework
of reference and message structure will lead teams to better commu-
nicate and to take advantage of these specicities through a synergic
team functioning.
Through the design of this tool based on a framework of reference
combining biological and engineering approaches, we propose a
space for interdisciplinary interactions to support communication.
4 Material and Methods
This section summarizes the denition of various concepts used
throughout the article and presents the method used to build the
shared framework of reference.
4.1 Concepts Associated With the Study. As modeled in
Sec. 2.3.1, interdisciplinary communication is strictly understood
in this article as a two-way process between actors having a differ-
ent background. In this context, and according to Choisdenition
[56,57], interdisciplinarity is an approach which analyses, synthe-
sizes, and harmonizes links between disciplines into a coordinated
and coherent whole.Thus, interdisciplinary teams are considered
to be composed of interdependent contributing actors [58] with
various backgrounds and structured as a coordinated and coherent
wholeto reach a common goal.
To assist communication within these teams, as well as their
coordinated and coherentpractices, this publication relies on
the design of a shared framework of reference. From a generic
standpoint, a framework of reference is understood in this article
as a set of ideas, conditions, or assumptions that determine how
something will be approached, perceived, or understood[59].
The aim of this construct is to give a set of guidelines through
which information can be properly interpreted and assimilated.
Stakeholdersmental representations of information, and so the
subject of cognition, are dened as mental models[60]. Sharing
a same framework should allow actors to position themselves in a
common conceptual space, making them able to use similar and/
or complementary prisms of analysis [55].
Based on the framework, a tool incorporating the various guide-
lines is designed to create an interface through which the team can
establish a shared mental model and reason in a synergic way. The
aim of this tool is to support communication to reach a transdisci-
plinarypractice of biomimetics. The mental model shared by the
team is neither an engineering nor a biological model but trans-
cends each of their traditional boundaries[56]. In this new confor-
mation, actors share a reciprocal interdependence driven by goals
that include integrated input/output and affecting the other disci-
plines by reorientation[58]. The team itself is then considered
as a whole, having a common transdisciplinary eld of expertise.
4.2 Methodology. The formulation of a common framework
follows a methodology inspired from the previously described pub-
lications of Hashemi Farzaneh et al. [54] and Fayemi et al. [23] and
synthesized as follows:
(1) Analysis of the eld-specic frameworks.
(2) Comparison of key features: concepts, conditions, con-
straints, assumptions, etc.
041402-4 / Vol. 143, APRIL 2021 Transactions of the ASME
(3) Selection and/or combination of features and formulation of
guidelines.
(4) Synthesis of the guidelines generating unied framework.
In the case of our study, the framework analysis (1) is based on
the literature, the authorsexperiences, and discussions with
experts. Key features are then extracted into two aspects, visual
representations and processes (2). The formulation of guidelines
(3) follows elements extracted from the communication literature,
the specicities of biomimetic practices, and the hypothesis of
this article. Guidelines composing the framework are then synthe-
sized in Table 3(4).
Following this rst phase, we designed LINKAGE through the
embedment of the guidelines composing the new framework.
LINKAGE aims to support the synergic reasoning and communica-
tion of team members through the collaborative modeling of shared
representations of problems and solutions.
5 Formalization of the Common Framework
To identify key features to turn into structuring guidelines, we
studied the processes generating the data used in biomimetic pro-
jects. The unied biomimetic problem-driven process [12]is
taken as a reference for biomimetic processes and a generic biolog-
ical research process as described by Bernard et al. [61] is taken as a
reference to study biological research processes (phase 1, Sec. 4.2).
Features are then listed and compared with underline cognitive dis-
sonances between actors (phase 2, Sec. 4.2). Based on this compar-
ative analysis, guidelines are formulated (phase 3, Sec. 4.2).
Following the same methodology, graphic representations are
analyzed (phase 1, Sec. 4.2) and compared (phase 2, Sec. 4.2)to
validate features extracted from processes. Additional guidelines
are also formulated (phase 3, Sec. 4.2).
5.1 Analysis of the Field-Specic Frameworks and
Comparison of Key Features on Processes. The overall idea of
engineering design lies in the ability of design teams to create a
system performing a given set of functions. Function is dened as
the general input/output relationship of a system whose purpose
is to perform a taskor, at the early stages of the process, as the
abstract formulation of the task, independent of any particular
solution[62]. The term systemis considered after De Rosnays
denition, A system is a set of elements in dynamic interaction,
organized according to a goal[63].
Design processes thus guide design teams from a design brief to a
product which functions answer initial requirements. This brief is
based on client/companys understanding of what products
would be the most likely to be purchased by consumers?,oron
larger scale human needs/challenges[64]. Design practice then
appears subjective and profoundly dependent on the perception of
needs by clients, and so ultimately users. The approach intrinsically
depends on design choices leading to the variability of products
designed to perform the same task. From a technical standpoint,
the main validation parameter of these choices is the compliance
with the product requirements and from a strategic and economic
standpoint its the consumersacceptance, and so the commercial
success. Whether they solve a problem, or offer new functionalities
without an original problem, the rationale behind products then
appears as the provided service, the functions at the usersdisposal.
As Simon stated The engineer, and more generally the designer, is
concerned with how things ought to be () in order to attain goals,
and to function[65].
In biological research, projects emerge from observations and the
identication of a missing piece of biological knowledge. The
choice between research axes appears subjective because highly
dependent on funding, but the research results are supposed to be
inherently objective. Biological research process has the fundamen-
tal aim of answering a research question, in the words of Claude
Bernard, An experimenters mind must be active, i.e., must
question nature[61]. Comparing with design, biological studies
are seeking the truth and approaching it as nearly as possible.
Facing a research question, choices are called hypotheses and are
tested through experiments. The rationale behind biology then
appears to be knowledge. This knowledge can be turned into inno-
vation in a second time, but the core output of biological science
remains the understanding of living beings.
Based on 13 criteria, from the origin of projects to their nal
outputs, processes of references are then described in detail
(phase 1, Sec. 4.2) (Table 1). These key features are then compared,
and cognitive shifts are identied (phase 2, Sec. 4.2). Guidelines
synthesizing the various criteria are then formulated (phase 3,
Sec. 4.2). Five guidelines have been identied through the compar-
ative analysis presented in Table 1.
Guideline A: Embed subjective elements in external constraints.
We suggest engineers to embed subjective elements of the project
(concepts 2 and 3, Table 1) as external constraints. Doing so, biol-
ogists may approach the problem with objective questions (concept
3, Table 1), trying to understand how a biological system (concept
2, Table 1) would react facing given constraints (concept 1 and 2,
Table 1).
Guideline B: Combine prescriptive and descriptive approaches.
We suggest interdisciplinary teams to establish phases for prescrip-
tive (concept 5, Table 1) reasoning leading to functional scenarios
(concept 4, Table 1) describing the system-to-be (concept 6,
Table 1) as if it was real (concept 6, Table 1) allowing, in a
second phase, biologists to raise alternative technical questions
(concept 4, Table 1) or to describe (concept 4, Table 1) biological
solutions.
Guideline C: Expose the cognitive links to bridge functional,
structural, material and behavioral abstracted concepts. We
suggest team mates to explain their reasoning (concept 8,
Table 1) on a shared representation, allowing the various actors to
make connection with their own, eld-related, conceptual frame-
work (concept 7, Table 1) and generate hypotheses to be turned
into solutions (concept 9).
Guideline D: Present problems and solutions within their spatio-
temporal contexts. In both elds, context is key. First, it dictates
requirements for the design project and environmental constraints
from a biological standpoint (concept 10, Table 1). Second, it
ensures the solutions validation (concept 11, Table 1) and nally
it underlines the possibility of evolving conditions, questioning
the validity and adaptability of the biological/technical systems
(concept 12, Table 1).
Guideline E: Dedicate spaces for both product design and knowl-
edge gathering while supporting their synergic contribution. Since
biologistssystem of reward is based on knowledge and engineers
system of reward is based on the generation of well-functioning
products (concept 13, Table 1), we suggest interdisciplinary
teams to implement a biomimetics innovation strategy along with
a knowledge management strategy. Stakeholders should feel recog-
nized for both their team and eld-specic work to prevent frustra-
tion while maintaining a collaborative, non-competitive, team spirit.
Section 5.2 then presents several biological graphic representa-
tions that have been analyzed and compared with intermediate
representations from engineering design [66]. Since biological
process is similar to most scientic processes, this section presents
the identication of additional, more biology-specic, guidelines.
5.2 Analysis of Biological Information Representation. In
research, communication mainly occurs through conferences or
through the encoding of results in the form of scientic articles.
Within biological articles, gures representing biological systems
synthesize biological knowledge and thus holds a fundamental
role in communication. Figures data measurements, such as
graphs, matrix, etc. were not studied in this publication since they
do not appear specic to biology. Through the literature, the
authorsexperience in biology and the discussion with biology tea-
chersresearchers, we pointed out three common biological types of
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-5
Table 1 Comparative analysis on processes and guidelines synthesis
Concepts In Engineering In Biology Cognitive shifts Guidelines
1. Origin of a project Potential prot from the products
sales
Absence of knowledge, and/or nancial
incentives toward a biological truth
Where projects in engineering are
based on strategic choices and target a
consumer, projects in biology are
based on scientic knowledge and
target a scientic truth
Guideline A: Embed subjective elements in
external constraints
2. Input of the project Client/marketing brief A biological system or phenomenon to
understand
3. Project orientation Subjective Objectives
4. Type of problem How to design a system that
perform+function?
How do+biological system +perform
a function?
Where biologists deeply characterize
the system to describe its functioning,
engineers adopt a functional
reasoning and look to prescribe a
system that would represent a solution
Guideline B: Combine prescriptive and
descriptive approaches
5. Type of reasoning Prescriptive Descriptive
6. System studied in the project Conceptual: The studied system
doesnt exist yet
Concrete: The studied system is observed
7. Solving processinputs Abstracted function and associated
technical problems
Abstracted biological generic concepts
associated with a set of rules
Both proles use abstraction to solve
their problem. However, where
engineers use abstraction as a
formalized prerequisite for analogical
thinking, biologists, through their
reasoning, unconsciously abstract the
various parts of an existing system to
generate hypotheses on their potential
functional interaction, and ultimately
generate knowledge
Guideline C: Expose cognitive links to bridge
functional, structural, material, and
behavioral abstracted concepts8. Solving process For biomimetics, analogical reasoning
through the identication of
biological models sharing the same or
close abstracted function/technical
problems
Based on conceptsinteraction and
associated biological principles.
Resolution in the conceptual space.
Analogical reasoning may also be used
between biological studies
9. Solving process output Identied solution Generated hypothesis
10. Embodiment Generation of numerical models and
prototype to verify that requirements
are met
Design of a protocol and experiment
based on the hypothesis. Identication of
data to be measured
Most of the time, biologists would not
perform biological experiments
during biomimetic design processes
and will directly look at knowledge.
However, the systems adequacy and
properties will be tested. For the tests
to be relevant, contextualization in
both space and time/state appears
required
Guideline D: Present problems and solutions
within their spatiotemporal context
11. Testing phases Test both if the solution performs
efciently the function and what are
usersfeedback
Test if the model (in vitro/in vivo) reacts
the way it was predicted
12. End of the project steps The design of the product is
improvement. The detailed design and
industrialization phase then begin
Based on the results, the hypothesis is or
is not validated, the researcher generates a
rapport or an article to explain its ndings
13. Final output A product An increase of knowledge Where a system is the nal outputs in
product design, the system is the
initial input in biology. Where
information and knowledge are the
inputs in product design, they are the
outputs in biology
Guideline E: Dedicate spaces for both
product design and knowledge gathering
while supporting their synergic contribution
041402-6 / Vol. 143, APRIL 2021 Transactions of the ASME
representation that we characterized as observational, nested, and
dynamic.
5.2.1 Observational Representations. This rst type of repre-
sentation reects biologistsobservations. Among these are obser-
vational drawings (through microscopes or with the naked eye)
allowing biologists to describe biological specimens, present
results and characterize newly discovered species. Figure 3gives
an example of an observational drawing describing the anatomic
specicities of the wings of Pantarbes, a genus of bee.
Observational drawings are nowadays strongly supplemented
with numeric alternatives, photographs, electronic microscopy,
2D or 3D imaging, etc.
Among other elds, observational representations are used in
animal or vegetal anatomy, histology, morphology, etc.
At a conceptual level, this type of representation underlines the
necessity for biologists to communicate on the biological concept
of form [67].
5.2.2 Nested Representations. The second type of representa-
tion aims to describe organization of biological structures at multi-
ple levels. These schematics thus represent, on the same gure,
different magnications, zooming in a nested biological structure
in order to point out its complex hierarchical organization. These
representations are often used in anatomic descriptions to give
insights on the origin of systemsproperties. Figure 4gives the
example of the various levels of genetic condensation.
At a conceptual level, these representations deal with the biolog-
ical concept of structure [67].
5.2.3 Dynamic Representations. Dynamic representations can
be divided in at least two categories. The rst category represents
biological entities in successive states, explicitly modeling given
changes of state and so dynamic phenomena. Analyzing these
representations, we determined at least four main dynamic patterns
that can be distinguished: cyclical, acyclical, mixed, and
multi-cyclical.
In cyclical processes, biological systems return to their original
state. Figure 5presents an example of a cyclical process with the
behavior of ionic channels during the propagation of an action
potential. The separation of ions, their specicows, and their com-
bined transports allow the propagation of an action. Processes with
a cyclical pattern often constitute functional sub-units.
Multi-cyclical pattern can also be observed. During action poten-
tial generation phase, a rst type of cycle can occur when the stimu-
lation remains under a threshold (failed initiation on the center of
Fig. 5). Once the threshold is exceeded, an action potential is gen-
erated, and the second type of cycle emerges as previously pre-
sented. The overall phenomenon is then multi-cyclical.
In acyclical processes, biological systems undergo irreversible
changes (evolutionary process of species, the growth of an individ-
ual, etc.) and then do not usually return to their original state
(Fig. 6).
Acyclical processes are often the result of several biological func-
tioning units as they induce deep and synchronized modications of
the biological entities: growth, differentiation, evolution, etc.
Finally, mixed processes are identied. For example, a cyclical
process, such as the cardiac cycle, might evolve progressively
through time, strengthening thanks to exercises, or evolve in a
more sudden way in the case of a stroke. As all cyclical processes
ultimately change because of aging, the combination of cyclical
and acyclical patterns is the most complex, but the most accurate
one.
Since a biological structure can be highly variable between two
individuals at the same age, the concept of time is highly relative
in biology. Biologists then rather refer to states instead of time
when describing a biological process. For example, a highly
damaged lung could be caused by 20 years of smoking or caused
by 40 years of extreme work conditions. What we consider here
is the state of the lungsdegradation process, not its age per se.
However, duration can be, and often is, critical in processes. As a
Fig. 3 Observational representation of the wings of Pantarbes,
a genus of bee. From Comstock, The Wings of insects,1918,
(g. 368).
Fig. 4 Nested representation of genetic material: (a) chromo-
somes, (b) 300 nm chromatin loops, (c) 30 nm chromatin, and
(d) 2 nm double-strained DNA
Fig. 5 Example of a cyclical process, here the behavior of ionic
channels during the propagation of an action potential. Based on
Ref. [68].
Fig. 6 Example of an acyclical process representation, six of
the developing stages of a chick. Image by Franz Kreibel
adapted from Ref. [69].
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-7
result, biologists often articially determine t=0 at the starting
point of processes with a cyclical pattern.
In the second category of dynamic representation, the different
states are not explicitly represented but are suggested in the sche-
matics, mainly through arrows (Fig. 7).
They focus on the dynamics of interactions between various ele-
ments (molecular, living organisms, external parameters, etc.). For
example, they model molecular pathways or ecosystemic relation-
ships for example. These schematics allow the representation of
successive change of states, like chain reactions in cellular
biology or microbiology. Combined with the recent emergence of
computational approaches, these representations lead to the emer-
gence of the eld of systems biology that would not be studied in
this article. The dynamic type of representation thus represents
the biological concept of function [67].
It has to be underlined that other types of representations exist in
biology, such as phylogenetic trees, models in systems biology [70],
etc. Nonetheless, through this quick description of three types of
representation, the importance of a multiparameter prism (scales,
interactions, states) appears as a key aspect of biological
representations.
In biology, it is a well-known fact that forms and structures allow
function [67]. The representations abovementioned lead biologists
to structure specic forms in order to consider functions, such as
dealing with internal and external energyor managing informa-
tion[71].
In engineering, the types of encoding are strictly characterized,
they follow well-dened structures depending on the phases of
the project and the required level of abstraction. In order to validate
the previously identied cognitive shifts, the characteristics of these
intermediate representations [66] have been compared with the
observations made on biological representations.
5.3 Comparison of Biological and Engineering
Representations. This section presents several key concepts
explaining potential cognitive dissonances (phase 1, Sec. 4.2) and
compares them (phase 2, Sec. 4.2) to synthesize guidelines (phase
3, Sec. 4.2) (Table 2).
Four new guidelines are thus synthetized. Guideline F: Consider
systems evolution through state-based reasoning to represent func-
tions. Biological literature uses state-based reasoning to represent
the dynamics of processes leading to functions. We suggest engi-
neers to formalize problems in a similar format to align biological
Fig. 7 Example of a generic molecular pathway, from signal
xation (molecule A) to the secretion of a response protein
(protein p)
Table 2 Comparative analysis on graphic representation and guidelines synthesis
Concepts In engineering In biology Cognitive shifts Guidelines
1. Objective of the
representation
Explain the objectives to meet
and how to design a system or
parts of the system performing
the determined functions
Represent the characteristics,
structures, and functioning of
biological organism and
phenomena in order to
describe observations,
hypothesis, or results
Technical representations
explain what is expected and
prescribe how to meet these
expectations where biologists
representations describe
biological systems and the
way they function
Validate the guideline B
(from Table 1): Prevent
noises coming from
overlapping channel of
communication
2. Concept of
functions
Represents the products
legitimacy (client brief,
Octopus diagram, etc.)
Represents the consequences
of the formsand structures
evolution through states
(various patterns)
Where, in biology structured
forms allow functions and
give an evolutive advantage to
biological systems, in
engineering, structures and
forms are design to perform a
pre-established function
Guideline F: Consider
systems evolution
through state-based
reasoning to represent
functions
3. Concept of
structures
Association of functional
sub-units from various
sub-systems (FAST diagram,
etc.)
Deeply intricate association of
elements of the biological
system [72] developed along
the evolutionary process
Where engineers reason on
structure with a sequential
approach linked with
subfunctions, biologists
zoom-in biological nested
structure to understand how
functions are performed
Guideline G: Model
systems through nested
structures
4. Concept of
forms
Final embodiment of the
technical elements performing
the subfunctions (CAD, etc.)
(nal steps)
Initial observation of a
biological entity in the real
world (observational
representation, etc.) (early
steps)
Where forms representation
in engineering is an
embodiment of subfunctions,
forms representation in
biology is a clue toward the
solving of a research question
Guideline H: Be specic
on forms
5. Systemic
approach
Taking a systemic standpoint
increases the number of
constraints and so the
functional complexity
The resolution of a biological
problem is intrinsically linked
with the consideration of
various systemic levels, and of
environmental elements [73].
Living systems are complex
systems
We can see a shift toward the
consideration of the systemic
reasoning which represents an
increase of complexity in
engineering but a requirement
to solve problems in biology
Guideline I: Support a
systemic standpoint
041402-8 / Vol. 143, APRIL 2021 Transactions of the ASME
and technical visual channels along with signalsencoding (concept
2, Fig. 2).
Guideline G: Model systems through nested structures. We
suggest using nested modeling to support communication by align-
ing visual channels and by creating a conceptual link between func-
tional sub-units and biological sub-systems at various
organizational scales (concept 3, Fig. 2).
Guideline H: Be specic on forms. Forms are the rst visible
clues toward the understanding of biological phenomenon. Where
biologists may understand forms as characteristic features, engi-
neers may also analyze forms from their technical standpoint.
Doing so, they can bring additional expertise, on uids dynamics
for instance, and identify functional properties. Synergic practices
should then lead to the creation of new communication paths by
allowing practitioners to identify which messages, and types of
signal, are relevant, and for which teammates (destination)
(concept 4, Fig. 2).
Guideline I: Support a systemic standpoint. We suggest adopting
a systemic standpoint to align the teammatesshared mental models
through an increasing number of characterizing conceptual links
(concept 5, Fig. 2).
Section 5.4 concludes Sec. 5and summarizes the main elements
structuring the common framework of reference (phase 4, Sec. 4.2).
5.4 Synthesis on the Guidelines Structuring the Common
Framework of Reference. Through the comparative analyses pre-
sented in Sec. 5, nine guidelines have been formalized to structure a
common framework of reference to assist interdisciplinary biomi-
metic design teamscommunication and practice.
In the words of Simon The natural sciences are concerned with
how things are () design on the other hand is concerned with how
things ought to be[65]. Among his main difference is often
referred to as prescriptiveversus descriptivereasonings in the
literature [37].
First, reasoning shifts regarding processes have been identied
on processes and summarized into ve guidelines (Table 1).
The analysis then focused on visual communication channels,
identied in the literature as a potential lever for improving commu-
nication [54], and questioned messagesencoding/decoding,
signals characterization, and mental models. Four more guidelines
have thus been added to the common framework of references.
The synthetized framework combines both ways of looking at
problems or solutions to support biologistsintegration, interdisci-
plinary communication, and promote a synergic teamwork:
Guideline A: consider subjective elements as embedded in
external constraints.
Guideline B: combine prescriptive and descriptive approaches.
Guideline C: expose cognitive links to bridge functional, struc-
tural, material, and behavioral abstracted concepts.
Guideline D: present problems and solutions within their spa-
tiotemporal contexts.
Guideline E: dedicate spaces for both product design and
knowledge gathering while supporting their synergic
contribution.
Guideline F: consider problem/solution dynamics through a
state-based evolution
Guideline G: model systems through nested structures
Guideline H: be specic on forms
Guideline I: support a systemic standpoint
Based on this common framework of reference established,
Sec. 6describes the designing of LINKAGE, a new tool destined
for biomimetic interdisciplinary team.
6 LINKAGE
Link an Interpretation based on Natural science Know-how with
an Applied Goal from Engineering (LINKAGE) is a tool intended
to design project-specic common frameworks based on the
various structuring generic guidelines identied in Sec. 5.
6.1 LINKAGEs Goals. LINKAGEsrst goal is to assist
communication between classicdesign teams and horizontal
biologists.
Its second goal is to optimize the biomimetic design process by
guiding the reasoning of the practitioners.
Lastly, LINKAGE aims to offer a way to store both structured
information, and coupled technological challenge and biological
solution, to capitalize on biomimetic studies, maximize their prot-
ability, and recognize the increase of knowledge as an objective
(Guideline E).
The long-term goal of this tool is to increase biomimetic design
teamseffectiveness and to contribute to biomimetics implementa-
tion and spreading.
6.2 LINKAGEs Formalization. LINKAGEs building is
presented through examples and following the steps of the unied
problem-driven process [12]. Guidelines from the common frame-
work used to design LINKAGE are explicitly identied. Their
embodiments are then summarized at the end of Sec. 6(Table 3).
6.2.1 Step 1: Problem Analysis. As presented previously, tech-
nology pull processes are based on design problems. For example,
how to design a system that selectively lter a liquid without clog-
ging? usually comes from the following cognitive reasoning:
Through time, residual elements of the ltered liquid accumulate
at the lters surface and clog it, as a result, how can we prevent
this phenomenon? Following the guideline A, we embed this
problem as external constraints. Since problems are dynamics, the
modeling leads to scenarios. As guided by the guideline F, these
scenarios follow state-based dynamics rather than linear time-based,
dynamics. In order to visually represent the dynamics, states are
linked with arrows. Teams may question the problem through the
various patterns existing in biological representation (Sec. 5.2.3).
Whatever the scenarios, LINKAGE suggest closing the loops to
implement a homeostasis-based reasoning.
Applying this rst step to our example, the original problem is
decomposed in three states, Initial state(clean), In use(not
clean but not clogged) and Clogged(not functional). The evolu-
tion pattern is acyclical if the lter is discarded after it clogged, it is
cyclical if the lter is cleaned and reused. LINKAGE suggests
looking at the problem through the second angle, allowing to
reach back the initial state. Logically, the lter will be discarded
eventually so the mixed pattern can be used to represent the
whole life cycle of the system.
Facing this scenario, the design team can approach the problem
from an objective standpoint since the subjective orientation is
already imposed by the states. In this rst step, the system is not
characterized yet, the design problem is.
6.2.2 Step 2 and 6: Abstraction of Design Problems or
Biological Solutions. Step 2 and 6 of the process both aim to
model systems. As a result, they are described simultaneously.
Following the guideline G, the LINKAGE tool allows systems
representation at multiple organization scales leading to a nested
structure. Based on the guideline I, this approach is also associated
with systemics reasoning, more specically the ontological axis
component [74].
In practice, LINKAGE presents at least three nested systemic
levels: the super-system, system, and subsystem. Yet, the tool sug-
gests considering further levels. Within LINKAGE, specic spaces
dedicated for each systemic level questions users, leading them to
deeply characterize the various elements to consider (guideline H)
and their interconnections (guideline C).
Interestingly, during these phases LINKAGEs inputs can come
from functional analysis tools such as Octopus diagramfrom
the APTE method (Fig. 8) which are well-known bydesign teams.
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-9
It should then be easily compatible and implementable with design
teams preexisting practice.
One interest of LINKAGE is to force design team to further con-
sider the space of the super and sub-systems and so create intercon-
nection at multiple scales. For example, how are weather
conditions impacting a planes wing? On which subparts? On
which organizational layers?(guideline G).
Based on this new nested representation, users can model either
technological systems (step 2) or biological systems (step 6),
through textual descriptions and structured graphic representations
(observational representation, blueprint, etc.) (Fig. 9).
Based on the guideline F, the systems dynamics in imple-
mented through the states and patterns previously dened.
Systems functional features linked with the problem/solution are
thus represented.
If we take the example of the lter previously used, the lter and
liquids are linked during the structural description, but the lters
function only appears once the liquid get from one side (state 1)
to the other side (state 2).
Figure 10 illustrates a mixed LINKAGE representation com-
posed of three systemic levels, a three-step cyclical phenomenon
(A, B and C) and an acyclical phenomenon of n2 states. Guide-
lines which embodiments can be directly perceived on the represen-
tation are specied in red (G.
A
,G.
C
,G.
D
,G.
F
,G.
G
and G.
H
)
(Fig. 10). Through this state-based functional reasoning,
LINKAGE allows interdisciplinary design teams to express the
systems evolution in a framework that is more coherent with bio-
logical processes. Doing so, LINKAGE assists teammates commu-
nication through the implementation of a common format (visual
representation) and guides the abstraction of external constraints,
functional features, and structural elements during modeling steps.
To summarize, LINKAGE aims at allowing users to visualize
various systemic levels, along with their interactions, at various
states. LINKAGEsnal structure combines elements from both
elds and supports the constitution of a shared space of representa-
tion and mental models (Table 3).
6.3 LINKAGE, a Tool Fitting the Current Biomimetic
Conceptual Framework. Various biomimetic innovation axes
are characterized in the literature. Existing theoretical concepts
can be linked with the structure of LINKAGE (Table 4).
The biological concept of function [67], which is the basis of
engineering reasoning, appears as the synthesis of the whole repre-
sentation as the form and structure allow the system to full its func-
tion through processes.
7 Intended Contributions
Through the description of LINKAGEs potential outputs on
communication, team collective reasoning, modeling, and abstrac-
tion, this section describes the main expected contributions of the
tool.
7.1 Contributions to Prevent the Cognitive Dissonance.
Cognitive dissonances, identied in the literature as a main
source of communication noises (Sec. 2.3.2), is the disruptive over-
lapping of variable cognitive frameworks (cultural, reasonings
habits, practical goals, etc.).
When unusual teammates are integrated in design teams, no spe-
cic spaces are dedicated for them at rst. Cognitive dissonances
then appear to be linked to the fact that several people are trying
to do the same things at the same time with different frameworks
without acknowledging or understanding these differences. So
Where can I (and my ideas) t?may be the rst question horizon-
tal biologists will ask themselves. Indeed, since the design reason-
ing and methodological framework were established as working
Fig. 8 From a functional analysis representation to a nested
standpoint. Interacting sub-systems are connected through
dashed lines.
Fig. 9 Example of LINKAGEs formalization focused on levels of
organization, for the surface of a boats hull and the surface of
Salvinia molesta. Pictures based on Ref. [75].
Fig. 10 Empty version of a LINKAGE representation with guide-
linesembodiments (G
.A
,G
.C
,G
.D
,G
.F
,G
.G
, and G
.H
)
041402-10 / Vol. 143, APRIL 2021 Transactions of the ASME
wholes, no pieces appeared missing and so no spaces were
available.
Through the formalization of an expended common framework
(Sec. 5), new spaces are likely to become available, allowing the
integration of alternative reasonings and approaches represented
by horizontal biologists. This step is intended to recognize eld-
specic differences, supporting the avoidance of disruptive
overlaps.
LINKAGE structures the combination of prescriptive and
descriptive approaches and is expected to guide the teamspractice
toward the identication of several layers of information and several
use phases on a single shared representation.
The rst step of the biomimetic design problem is the denition of
the design problem and can be associated with the generic question
what do we actually want to do?. Such questioning is linked with a
prescriptive approach. However, relevant design objectives rely on
context and requirements and so the denition of elements and
their interaction at various systemic levels. This descriptive approach
can be represented by the following question: What is it that we are
looking at?.The implementation of a dynamic evolution of the
model and the characterization of the elementsinteraction involved
in the systems change of state should lead the team to dene the
functions through a descriptive standpoint: What does the system
need to do to reach the following state in the given context?Pre-
scriptive design requirements are then embedded in the context.
Foundations of the project may thus be determined in a shared
framework, and the analysis of the model should lead to the techni-
cal problems by wondering how to get from state nton+1at each
systemic level?
Again, a descriptive approach is expected to be used by the team
during this step, which should lead to the abstraction of the design
problem on several layers of analysis. Keeping the interactions
patterns while abstracting the elements composing the system,
sub, and super-system levels should then allow the design team
to perform more easily an overall abstraction of the technological
problems.
The following step in the biomimetic design process is the trans-
position to biology. Based on the context and abstracted elements,
the design team should be able to describe a biological solution
space. Thanks to its states-based and nested structure, this space
denition is intended to depend not only on prescriptive functional
constraints but also on the descriptive contextual constraints. Each
super-system that includes our systemic level of interest can bring
information.
The source of biological models may either be a horizontal biol-
ogist or already existing tools. We will not go further into details on
that subject in this article.
Table 3 Guidelinesembodiment on LINKAGE tool
Guidelines Embodiment
Guideline A: Embed subjective elements in external constraints Characterization of the super-system and of the systems various states
(Fig. 10,G
.A
)
Guideline B: Combine prescriptive and descriptive approaches Separation in space (embed constraints versus description on multiple levels)
and separation in time (phases of use)
Guideline C: Expose cognitive links to bridge functional,
physicochemical and behavioral abstracted concepts
The network of concepts designed at different scales of the model and at
various states of the problem/solution (Fig. 10,G
.C
)
Guideline D: Present problems and solutions within their
spatiotemporal contexts
Denition of super-systems (depend on space and time) and states (depend on
duration) (Fig. 10,G
.D
)
Guideline E: Dedicate spaces for both product design and knowledge
gathering while supporting their synergic contribution
Capitalization of the results through a database or an ontology based on
models formalized with LINKAGE. Recognize generated knowledge for their
scientic value
Guideline F: Consider systems evolution through state-based
reasoning to represent functions
Evolution patterns through states (Fig. 10,G
.F
)
Guideline G: Model systems through nested structures Nested structure and interaction through double-head arrows (Fig. 10,G
.G
)
Guideline H: Be specic on forms A specic space is dedicated for each level to characterize the features of
involved elements (Fig. 10,G
.H
)
Guideline I: Support a systemic standpoint Based on Le Moigne [74]:
The nested structure refers to the ontological axis
The evolution patterns through states refers to the functional axis (cyclical
pattern) and the genetic axis (acyclical pattern)
The network of concepts that can be abstracted refers to the teleological axis
Table 4 Synthesis of biological and biomimetic concepts alignment with LINKAGEs design
LINKAGE Biology Biomimetics
Description of elements at each level of organization Form [67]Form [1]
Structure [23]
Morphology [24,54]
Interactions within and between systemic levels,
double-head arrows
Structure [67](Eco)system [1]
Relational [54]
Pattern of organization [23]
Pattern of evolution between states and single-head
arrows
Processes [67]Change [54]
Processes [1,23]
Analyses of representations made with LINKAGE Denition of internal or external constraints
and of solving strategies
Abstraction of technical problems (step 2) or
biological solutions (step 6) [64]
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-11
Once a biological model is identied, it should be possible to
model it on the exact same framework, using the same approach,
then with the technological problem. From the biological model,
the team is again oriented toward a descriptive standpoint. The
team will then face the following question: How does the biolog-
ical model perform this function?(descriptive).
The reversal of the expert roles between engineers and biologists
at this specic step is expected to be supported by LINKAGE and
should then be acknowledged by the team. As a result, even if they
adopt different roles, team members should be able to represent
information on a shared representation. LINKAGE is then intended
to bring an expression support for horizontal biologists to be heard
and understood while the team shares a common mental model.
In a rather similar way than with the technological aspects, the bio-
logical model may be analyzed and compared with the design
problem. Horizontal biologists should then ensure the scientic rele-
vance of gathered data while the rest of the team is guided to dene
the proper level of abstraction of the biological strategies. The simi-
larity of representation of both design problem and solution should
ease the transfer toward technological application leading the team
back to a prescriptive reasoning, based on the chosen biological
model, how to design a system tting our functional requirements?.
Thanks to its common framework, LINKAGE tool may be used
in either a technology pull or a biology push approach. The abstrac-
tion and transfer steps are not eld related which should make
LINKAGE a tool adaptive to any kind of project.
Finally, as the build of such representations, and more broadly of
a biomimetic project, demands time and resources, it appears as a
fundamental aspect for companies to capitalize on the acquired
knowledge. In addition, biologistssystem of reward is based on
knowledge. Consequently, we support the idea that a biomimetics
innovation strategy must be implemented along with a knowledge
management strategy. Such strategy is intended to be made avail-
able by LINKAGE. Its representations are likely to gather a lot of
interconnected information that may be used as a storage format,
enlarging a database or an ontology with structured and interrelated
information.
The synthesis of a common framework of reference and its imple-
mentation through LINKAGE thus aim to decrease cognitive disso-
nances all along the biomimetic process.
7.2 Contributions to the Encoding and Decoding
Processes. This Sec. 7.2 explores LINKAGE targeted contribu-
tions on a second noisessource, the encoding and decoding pro-
cesses (Sec. 2.3.2).
First, the variability of mental models representing the projects
objectives, constraints, and the system to design can lead to misun-
derstandings (psychological noise). As a result, the encoded mes-
sages may be irrelevant.
Moreover, because of frameworksdifferences, a relevant
message can be inadequately encoded or decoded. This aspect
does not appear specic to interdisciplinary teams, but the risk of
non-compatible mental representations seems higher in such cases.
Through the incorporation of the projects objectives within the
states and the formalization of information in a shared framework
of reference (constraints, elements to consider, interactions, and
functions), LINKAGE aims at supporting a common understanding
of the project. The tool should also allow teammates to encode and
decode signals through the same overall prism dened by the
co-designed LINKAGE representations.
Second, semantic noises were identied as an issue. Where voca-
bulary has not been considered in this article, LINKAGE contribu-
tions on contextualization is here discussed.
The tool should lead the team to represent concepts in intercon-
nected networks. The increase in the number of characterizing inter-
actions may help teammates to correctly interpret concepts, and so
to decrease semantic noises. This reasoning may also lead the team
to identify understanding errors. Furthermore, the context surround-
ing potential missing information may be better characterized,
leading to the encoding of a clear communication signal to be
exchanged with external experts from engineering or biology (ver-
tical biologist).
Finally, the model is designed to act as an interface between eld-
specic representations. As a result, LINKAGE should give teams
the ability to encode eld-specic message through common repre-
sentations. The tool may then act as new communication channel.
LINKAGE then targets the reduction of semantic noises through
contextualization and the formalization of new communication
channels having specic encoding/decoding processes. Section
7.3 presents what LINKAGEs specicities are compared with the
other available tools.
7.3 LINKAGE, Why Isnt it Just Another Tool? This last
section summarizes in a short table the main aspects differentiating
LINKAGE from other biomimetic tools presented in Sec. 2
(Table 5). The key difference of our work lays in its research
axis. Through the designing of a common framework of reference
for stakeholders coming from different elds, this publication inves-
tigates interdisciplinary teams internal communication. Following
guidelines involving a series of cognitive shifts, we presented a
framework designed for practitioners to nd spaces to express them-
selves. LINKAGE appears as an embodiment of these guidelines,
guiding interdisciplinary teams during the biomimetic process.
We truly believe that performing the conceptual shift needed to
design tools for biomimetic interdisciplinary teams is crucial for
the actual integration of biologists (whatever their actual prole
might be) in design teams and during the practice of biomimetics.
These interdisciplinary interactions are the very core and wealth
of biomimetics and are requirements for teammates to transcend
their elds to form a transdisciplinary whole in the long run.
8 Perspectives
As presented in Sec. 1, this study on communication within inter-
disciplinary biomimetic design teams is composed of two parts.
This article presents the theoretical foundation behind the tool,
but we are currently working on its practical implementation.
Since there is an inherent difculty for users to combine differ-
ent forms of information (image, video, scientic references,
description, etc.) on paper, and because it should make dissemi-
nation and capitalization much easier, a computerized version
of the tool is currently under development. It should also allow
Table 5 Comparison of LINKAGE and already existing biomimetic tools on several methodological characteristics
Existing tools Linkage
Specic aim Gives a way to perform one of the biomimetic
processstep
Structure the information gathered by the team and guide the communication
process to reach a synergic reasoning
Fitting user prole Mostly unidentied Full potential with interdisciplinary biomimetic teams
Conceptual framework Engineering design and biomimetics Common framework of reference combining, engineering design,
biomimetics, and biology through structuring guidelines
Scope Mostly step specic Contribution identied at least during steps 2, 3, 6, and 7
041402-12 / Vol. 143, APRIL 2021 Transactions of the ASME
the automation of some redundant steps to increase the tools
ergonomics.
Furthermore, this computerized version will allow us to test the
tools impact at a greater scale and implement feedback loops to
keep on improving its ergonomics, design new features and imple-
ment the knowledge capitalization through closed or open databases
depending on the use.
As previously described, we are also working on the potentiali-
ties offered by LINKAGE regarding the identication and the selec-
tion of biological models.
On a more general note, our current work focuses on the remain-
ing issues about biologistsintegration, which is the ill-dened hor-
izontal biologist prole.
The difculty here arises because only a few tools and processes
are designed to include biologist. Taking such a starting point, it
locked the door for biologists, leading engineers themselves to
have trouble formulating realistic expectations for biologists. As a
result, it appeared necessary to start by focusing on the overall
framework adjustment (Why? When? Where? How to include
this potential new prole?) before determining and training (How
to match design team expectations?) a tting new prole having a
background in a biology and cross-disciplinary skills.
All along this article, we talked about horizontal biologists to
ease the reading but the question whether the prole of interest is
a biologist per se or another type of prole (engineer, designer,
etc.) having followed specic training in biology remains unsolved
and will be extensively studied in coming articles.
9 Conclusion
This publication dealt with the integration of a new prole having
an expertise in biology within biomimetic design teams. This study
takes the hypothesis of an already integrated actor and wonders
about his/her ability to act in a symbiotic way with the rest of the
team. Communication, as a crucial aspect of teamwork and a well-
known issue during pluridisciplinary project, is at the center of our
reection. Anticipating on those challenges to fully benet from
biologistsvaluable expertise appears as a requirement to support
their proper integration.
Through the literature review, we identied various noises at the
origin of communication problems and chose to focus on expres-
sion spaces (channel), cognitive dissonance,encoding format, and
decoding process, and more generally on the framework of refer-
ence leaving aside the issue of eld-specic vocabulary.
The research question studied in this article was: Can we dene
a common framework of reference to support communication
between biologists and engineers in interdisciplinary biomimetic
design teams?
By looking at both design or biological processes and graphic
representations, the rst contribution of this article is the extraction
of various concepts and cognitive shifts that were turned into nine
guidelines structuring a framework of reference common for both
integrated biologists and classic design team members. Second,
through the embodiment of those guidelines (Table 3),
LINKAGE, an interdisciplinary tool leading to support the synergic
reasoning of the biomimetic design team was designed. Finally, its
compatibility with preexisting conceptual frameworks and its tar-
geted contributions are described.
The second part of this study, combining applications, tests
results, and usersfeedback, will be soon addressed along with
the release of a computerized version of LINKAGE. This open-
access tool will be available to the greatest number possible in
the form of a website.
Data Availability Statement
The authors attest that all data for this study are included in the
paper.
References
[1] Benyus, J. M., 1997, Biomimicry: Innovation Inspired by Nature, Quill, New
York.
[2] ISO/TC266, 2015, Biomimétique-Terminologie, Concepts et Méthodologie.
[3] Keshwani, S., Lenau, T. A., Ahmed-Kristensen, S., and Chakrabarti, A., 2017,
Comparing Novelty of Designs From Biological-Inspiration With Those From
Brainstorming,J. Eng. Des.,28(1012), pp. 654680.
[4] Ahmed-Kristensen, S., Christensen, B. T., and Lenau, T., 2014, Naturally
Original: Stimulating Creative Design Through Biological Analogies and
Random Images,International Design Conference, DESIGN, Dubrovnik,
pp. 427436.
[5] Yen, J., Helms, M. E., Goel, A. K., Tovey, C., and Weissburg, M., 2014,
Adaptive Evolution of Teaching Practices in Biologically Inspired Design,
Biologically Inspired Design, 1st ed., Vol. 1, A. K. Goel, D. A. McAdams, and
R. B. Stone, eds., Springer-Verlag London, London, pp. 153199.
[6] Chirazi, J., Wanieck, K., Faye mi, P. E., Zollfrank, C., and Jacobs, S., 2019, What
Do We Learn From Good Practices of Biologically Inspired Design in
Innovation?,Appl. Sci.,9(4), p. 650.
[7] McCardle, J., Angus, R., and Trott, J., 2019, Transdisciplinary Design Practices
in Education: A Complex Search for Innovation in Nature,International
Conference on Engineering and Product Design Education - E&PDE, Glasgow,
UK, Sept. 1213.
[8] Wanieck, K., Fayemi, P. E., Maranzana, N., Zollfrank, C., and Jacobs, S. R.,
2017, Biomimetics and Its Tools,Bioinspir. Biomimet. Nanobiomater.,6(2),
pp. 5366.
[9] Jacobs, S. R., Nichol, E. C., and Helms, M. E., 2014, “‘Where Are We Now and
Where Are We Going?The BioM Innovation Database,ASME J. Mech. Des.,
136(11), p. 111101.
[10] Bar-Cohen, Y., 2006, BiomimeticsUsing Nature to Inspire Human
Innovation,Bioinspir. Biomimet.,1(1), pp. P1P12.
[11] Vincent, J. F. V., 2009, BiomimeticsA Review,Proc. Inst. Mech. Eng., Part
H,223(8), pp. 919939.
[12] Fayemi, P.-E., Wanieck, K., Zollfrank, C., Maranzana, N., and Aoussat, A., 2017,
Biomimetics: Process, Tools and Practice,Bioinspir. Biomimet.,12(1),
p. 11002.
[13] Lindemann, U., and Gramann, J., 2004, Engineering Design Using
Biological Principles,International Design Conference, DESIGN, Dubrovnik,
pp. 355360.
[14] Lenau, T., 2009, Biomimetics as a Design MethodologyPossibilities and
Challenges,International Conference on Engineering Design, ICED,
Standford, pp. 121132.
[15] Goel, A. K., Vattam, S., Wiltgen, B., and Helms, M. E., 2014, Information-
Processing Theories of Biologically Inspired Design,Biologically Inspired
Design, 1st ed., Vol. 1, A. K. Goel, D. A. McAdams, and R. B. Stone, eds.,
Springer Verlag London, London, pp. 127152.
[16] Weidner, B. V., Nagel, J., and Weber, H. J., 2018 , Facilitation Method for the
Translation of Biological Systems to Technical Design Solutions,Int. J. Des.
Creat. Innov.,6(34), pp. 211234.
[17] Graeff, E., Maranzana, N., and Aoussat, A., 2019, Engineersand Biologists
Roles During Biomimetic Design Processes, Towards a Methodological
Symbiosis,International Conference on Engineering Design ICED, Delft,
The Netherlands, Aug. 58, pp. 319328.
[18] Vincent, J. F. V., Bogatyreva, O. A., Bogatyrev, N. R., Bowyer, A., and Pahl,
A. K., 2006, Biomimetics: Its Practice and Theory,J. R. Soc. Interface,3(9),
pp. 471482.
[19] Ohno, T., 1978, Toyota Production System: Beyond Large-Scale Production,
Productivity Press, Cambridge, MA.
[20] Hoagland, M. B., and Dodson, B., 1995, The Way Life Works, Crown, New York.
[21] Vattam, S., and Goel, A., 2013, Seek ing Bioinspiration Online: A Descriptive
Account,International Conference on Engineering Design ICED, Seoul,
South Korea, Aug. 1922, pp. 347356.
[22] Kruiper, R., Vincent, J. F. V., Abraham, E., Soar, R., Konstas, I., Chen-Burger, J.,
and Desmulliez, M., 2018, Towards a Design Process for Computer-Aided
Biomimetics,Biomimetics,3(3), p. 14.
[23] Fayemi, P.-E., Maranzana, N., Aoussat, A., Chekchak, T., and Bersano, G., 2015,
Modeling Biological Systems to Facilitate Their Selection During a Bio-Inspired
Design Process,International Conference on Engineering Design ICED,
Milan, Italy, July 2730, pp. 225234.
[24] Nagel, J. K. S., Nagel, R. L., Stone, R. B., and McAdams, D. A., 2010,
Function-Based, Biologically Inspired Concept Generation,Artif. Intell. Eng.
Des. Anal. Manuf. AIEDAM,24(4), pp. 521535.
[25] Sartori, J., Pal, U., and Chakrabarti, A., 2010, A Methodology for Supporting
Transferin Biomimetic Design,Artif. Intell. Eng. Des. Anal. Manuf.
AIEDAM,24(4), pp. 483505.
[26] Wynn, D., and Clarkson, J., 2005, Models of Designing,Design Process
Improvement, 1st ed., Vol. 1, J. Clarkson, and C. Eckert, eds., Springer,
London, pp. 3459.
[27] Gericke, K., and Blessing, L., 2011, Comparisons of Design Methodologies
and Process Models Across Disciplines: A Literature Review,International
Conference on Engineering Design ICED, Copenhagen, Denmakrk, Aug.
1518.
[28] Baumeister, D., Tocke, R., Dwyer, J., Ritter, S., and Benyus, J., 2013, Biomimicry
Resource Handbook: A Seed Bank of Knowledge and Best Practices, CreateSpace
Independent Publishing Platform, Missoula.
Journal of Mechanical Design APRIL 2021, Vol. 143 / 041402-13
[29] Freitas Salgueiredo, C., and Hatchuel, A., 2014, Modeling Biologically Inspired
Design With the C-K Design Theory,International Design Conference,
DESIGN, Dubrovnik, Croatia, May 1922.
[30] Snell-Rood, E., 2016, Interdisciplinarity: Bring Biologists Into Biomimetics,
Nature,529(7586), pp. 277278.
[31] Graeff, E., Maranzana, N., and Aoussat, A., 2019, Biomimetics, Where Are the
Biologists?,J. Eng. Des.,30(89), pp. 289310.
[32] Helms, M. E., Vattam, S. S., and Goel, A. K., 2009, Biologically Inspired
Design: Process and Products,Des. Stud.,30(5), pp. 606622.
[33] Shannon, C. E., 1948, A Mathematical Theory of Commun ication,Bell Syst.
Tech. J.,27(3), pp. 379423.
[34] Berlo, D. K., 1960, The Process of Communication: An Introduction to Theory
and Practice, Holt, Rinehart and Winston, New York.
[35] Schramm, W., 1955, The Process and Effects of Mass Communi cation, 1st ed.,
Vol. 1, University of Illinois Press, Urbana, IL, pp. 1578.
[36] Devito, J. A., 2013, Interpersonal Communication Book, 13th ed., Vol. 1,
Pearson, London, pp. 1432.
[37] Bogatyrev, N. R., and Bogatyreva, O. A., 2015, TRIZ-Based Algorithm for
Biomimetic Design,Procedia Engineering, Vol. 131, D. Cavallucci, G.
Cascini, J. Duou, P. Livotov, and T. Vaneker, eds., Elsevier, New York, pp.
377387.
[38] Nagel, J., Schmidt, L., and Born, W., 2018, Establishing Analogy Categories for
Bio-Inspired Design,Designs,2(4), p. 47.
[39] Chown, B., 2017, The Design Team as a System of Systems,2017 IEEE
International Systems Engineering Symposium (ISSE), IEEE, pp. 15.
[40] Cheong, H., Chiu, I., Shu, L. H., Stone, R. B., and McAdams, D. A., 2011,
Biologically Meaningful Keywords for Functional Terms of the Functional
Basis,ASME J. Mech. Des.,133(2), p. 021007.
[41] Nagel, J. K. S., Stone, R. B., and McAdams, D. A., 2011, An Engineering-
to-Biology Thesaurus for Engineering Design,ASME 2010 International
Design Engineering Technical Conference & Computers and Information in
Engineering Conference, Montreal, Canada, Aug. 1518, pp. 117128.
[42] Biomimicry Institute, 2002, AskNatureInnovation Inspired by Nature,
AskNature, pp. 2729. https://asknature.org/, Accessed Nov. 23, 2018.
[43] Vincent, J. F. V., 2014, An Ontology of Biomimetics,Biologically Inspired
Design, 1st ed., Vol. 1, A. K. Goel, D. A. McAdams, and R. B. Stone, eds.,
Springer, London, pp. 269285.
[44] Vincent, J., and Cavallucci, D., 2018, Development of an Ontology of
Biomimetics Based on Altshullers Matrix,Automated Invention for Smart
Industries. TFC 2018. IFIP Advances in Information and Communication
Technology, Vol. 541, D. Cavallucci, R. De Guio, and S. Koziołek, eds.,
Springer, Cham, pp. 1425.
[45] Vattam, S., Wiltgen, B., Helms, M. E., Goel, A. K., and Yen, J., 2010, DANE:
Fostering Creativity in and through Biologically Inspired Design,Design
Creativity 2010, 1, Vol. 1, T. Taura, and Y. Nagai, eds., Springer, London, pp.
115122.
[46] Chakrabarti, A., Sarkar, P., Leelavathamma, B., and Nataraju, B. S., 2005, A
Functional Representation for Aiding Biomimetic and Articial Inspiration of
New Ideas,Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM,19(2), pp. 113132.
[47] Maturana, H. R., and Varela, F. J., 1980, Autopoiesis and Cognition: The
Realization of the Living, Vol. 42, Springer Netherlands, Dordrecht.
[48] Miller, J. G., 1978, Living Systems, 1st ed., Vol. 1, McGr aw-Hill, New York.
[49] Altshuller, G. S., 1984, Creativity as an Exact Scien ce: The Theory of the Solution
of Inventive Problems, Gordon and Breach Science Publishers, New York.
[50] Siegel, D. S., Waldman, D. A., Atwater, L. E., and Link, A. N., 2004, Toward a
Model of the Effective Transfer of Scientic Knowledge From Academicians to
Practitioners: Qualitative Evidence From the Commercialization of University
Technologies,J. Eng. Technol. Manag.,21(12), pp. 115142.
[51] Falkenhainer, B., Forbus, K. D., and Gentner, D., 1989, The Structure-Mapping
Engine: Algorithm and Examples,Artif. Intell.,41(1), pp. 163.
[52] Gentner, D., 1983, Structure-Mapping: A Theoretical Framework for Analogy,
Cogn. Sci.,7(2), pp. 155170.
[53] Helms, M. E., and Goel, A. K., 2014, The Four-Box Method of Problem
Specication and Analogy Evaluation in Biologically Inspired Design,
Volume 7: 2nd Biennial International Conference on Dynamics for Design;
26th International Conference on Design Theory and Methodology, Buffalo,
NY, Aug. 1720, ASME, p. V007T07A005.
[54] Hashemi Farzaneh, H., Helms, K., and Lindemann, U., 2015, Visual
Representations as a Bridge for Engineers and Biologists in Bio-Inspired
Design Collaborations,Proceedings of the International Conference on
Engineering Design ICED, Milan, Italy, July 2730, ICED, pp. 215224.
[55] Mathieu, J. E., Goodwin, G. F., Heffner, T. S., Salas, E., and Cannon-Bowers,
J. A., 2000, The Inuence of Shared Mental Models on Team Process and
Performance,Psychol. Assoc. Inc,85(2), pp. 273283.
[56] Choi, B., and Pak, A., 2006, Mult idisciplinarity, Inter-Disciplinarity and
Trans-Disciplinarity in Health Research,Clin. Investig. Med., 29(6), pp. 351
364.
[57] Alvargonzález, D., 2011, Multidisciplinarity, Interdisciplinarity,
Transdisciplinarity, and the Sciences,Int. Stud. Philos. Sci.,25(4), pp. 387403.
[58] Lotrecchiano, G., 2010, Complexity Leadership in Transdisciplinary (TD)
Learning Environments: A Knowledge Feedback Loop,Int. J. Transdiscipl.
Res., 5(1), pp. 2963.
[59] Merriam-Webster Dictionary, 2020, Frame of Reference.https://www.merriam-
webster.com/dictionary/frame of reference, Accessed Mar. 20, 2020.
[60] Gentner, D., 2002, Psychology of Mental Models,International Encyclopedia
of the Social & Behavioral Sciences, N. J. Smelser , and P. B. Baltes, eds.,
Pergamon Press, Oxford, pp. 96839687.
[61] Bernard, C., and Wolf, S., 1999, Experimental Medicine, 1st ed., Vol. 1,
Routledge, New York, pp. 2332.
[62] Pahl, G., Beitz, W., Wallace, K., Blessing, L. T. M., and Bauert, F., 1996,
Engineering Design: A Systematic Approach, Springer, New York.
[63] De Rosnay, J., 1977, Le Macroscope. Vers Une Vision Globale (The
Macroscope Towards a Comprehensive Vision),Popul.,32(6), p. 1319.
[64] Fayemi, P. E., Maranzana, N., Aoussat, A., and Bersano, G., 2014, Bio-Inspired
Design Characterisation and Its Links with Problem Solving Tools,International
Design Conference, DESIGN, Dubrovnik, Croatia, July 1922, pp. 173182.
[65] Simon, H. A., 1969, Th e Sciences of the Articial, MIT Press, Cambridge, MA.
[66] Bouchard, C., Camous, R., and Aoussat, A., 2005, Nature and Role of
Intermediate Representations (IR) in the Design Process: Case Studies in Car
Design,Int. J. Veh. Des.,38(1), pp. 125.
[67] Wainwright, S. A., 1988, Form and Function in Organisms,Integr. Comp.
Biol., 28(2), pp. 671680.
[68] Forehand, C. J., 2009, Medical Physiology: Principles for Clinical Medicine, 3rd
ed., Vol. 1, R. A. Rhades, D. R. Bell, eds., Lippincott Williams & Wilkins,
Philadelphia, pp. 4343.
[69] Sedgwick Minot, C., 1907, The Problem of Age, Growth, and Death,The
Popular Science Monthly,71(3), pp. 211.
[70] Haefner, J. W., 2005, Modeling Biologica l Systems:Principles and Applications,
2nd ed., Springer, New York, pp. 1475.
[71] Waterman, T. H., 1968, Systems Theory and BiologyView of a Biologist,
Systems Theory and Biology, 1st ed., Vol. 1, M. D. Mesarović, ed., Springer,
Berlin, Heidelberg, pp. 137.
[72] Karsenti, E., 2008, Self-Organization in Cell Biology: A Brief History,Nat.
Rev. Mol. Cell Biol.,9(3), pp. 255262.
[73] Jacob, F., 1993, The Logic of Life: A History of Heredity, New edition, Princeton
University Press, Princeton, pp. 1362.
[74] Le Moigne, J. L., 1994, La Théorie Du Système Général : Théorie de La
Modélisation (General System Theory: Theory of Modelling), Presses universi-
taires de France, Paris.
[75] Barthlott, W., Schimmel, T., Wiersch, S., Koch, K., Brede, M., Barczewski, M.,
Walheim, S., Weis, A., Kaltenmaier, A., Leder, A., and Bohn, H. F., 2010,
The Salvinia Paradox: Superhydrophobic Surfaces With Hydrophilic Pins for
Air Retention Under Water,Adv. Mater.,22(21), pp. 23252328.
041402-14 / Vol. 143, APRIL 2021 Transactions of the ASME
... This article presents the second part of a study on the development of LINKAGE, a tool which aims at supporting interdisciplinary biomimetic teamwork during the analysis and abstraction of technological problems and biological solutions. Designed to assist communication between teammates with a background in engineering and in biology, the tool is structured to embody a shared cognitive framework of reference defined in the first and theoretical part of this study [1] and presented in Sec. 1.2. ...
... Sections 1.1-1.3 present a synthesis of the extensive theoretical work performed in the first part of this study [1]. ...
... Fayemi et al. also identify three explanations "Their different backgrounds lead to divergent disciplinary or functional understanding of a concept, whether due to perception, languages, or 'thought styles'" [14]. These elements can be associated with various "communication noises" from communication sciences among which cognitive dissonance, encoding format, and decoding process [1]. ...
Article
Full-text available
Full text access : https://www.novatechsetproofs.com/authors/ASME/MD-20-1753.pdf Implementation of biomimetics in practical innovation strategies still faces various impediments. Multidisciplinary communication is one of the most recognized one. Enabling teammates having various cognitive and conceptual frameworks to properly exchange information is a key lever for optimization. In a previous study, we performed a comparative analysis of biologists' and engineers' cognitive and conceptual frameworks in order to support the establishment of a shared framework of reference within biomimetic teams. This theoretical work led us to consider various guidelines, embodied in a tool, LINKAGE, guiding the team along the biomimetic process, and more specifically during analysis and abstraction steps. This article presents a first version of this free access computerized tool, LINKAGE 1.2 (https://linkage-lcpi.com). After the description and positioning of LINKAGE, comparing to other existing tools, a testing phase involving 19 professionals divided into 5 interdisciplinary teams is presented. The results of this evaluation lead to the validation of some of the tool's objectives while underlining some lines of improvements. Various perspectives on the tool's development are also presented.
... BID is inherently interdisciplinary 10 . To facilitate the knowledge analogy between the engineering world and biological systems, designers should be equipped with a strong background in the engineering discipline as well as good expertise in the biological domain 11 . ...
... Conduct expert surveys and collect the expert's risk assessments. The individual fuzzy DRM given by each deci- Medium high (MH) (5,7,9) High (H) (7,9,10) Very high (VH) (9, 10, 10) sion maker is built as: ...
Article
Full-text available
Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts’ subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended MCGDM (multi-criteria group decision-making) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker’s risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended DEMATEL (decision-making trial and evaluation laboratory) is presented to determine the criteria weights and a fuzzy rough number extended MAIRCA (multi-attribute ideal real comparative analysis) is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.
... In our BioDesign lecture, we provide students the tools to use a solution-driven, bottom-up approach to extract biological principles for translation from original biological discoveries. Our approach directly addresses previously stated obstacles most often observed in engineering and design courses (Graeff et al. 2020(Graeff et al. , 2021Lenau et al. 2018;Nagel et al. 2019). "In practice, it has been acknowledged that challenges exist for designers and engineers in accessing and interpreting principles in life sciences through academic literature" (Rovalo et al. 2020). ...
Article
Full-text available
The goal of our i4’s Toward Tomorrow Program is to enrich the future workforce with STEM by providing students with an early, inspirational, interdisciplinary experience fostering inclusive excellence. We attempt to open the eyes of students who never realized how much their voice is urgently needed by providing an opportunity for involvement, imagination, invention, and innovation. Students see how what they are learning, designing, and building matters to their own life, community, and society. Our program embodies convergence by obliterating artificially created, disciplinary boundaries to go far beyond STEM or even STEAM by including artists, designers, social scientists, and entrepreneurs collaborating in diverse teams using scientific discoveries to create inventions that could shape our future. Our program connects two recent revolutions by amplifying Bioinspired Design with the Maker Movement and its democratizing effects empowering anyone to innovate and change the world. Our course is founded in original discovery. We explain the process of biological discovery and the importance of scaling, constraints, and complexity in selecting systems for bioinspired design. By spotlighting scientific writing and publishing, students become more science literate, learn how to decompose a biology research paper, extract the principles, and then propose a novel design by analogy. Using careful, early scaffolding of individual design efforts, students build the confidence to interact in teams. Team building exercises increase self-efficacy and reveal the advantages of a diverse set of minds. Final team video and poster project designs are presented in a public showcase. Our program forms a student-centered creative action community comprised of a large-scale course, student-led classes, and a student-created university organization. The program structure facilitates a community of learners that shifts the students' role from passive knowledge recipients to active co-constructors of knowledge being responsible for their own learning, discovery, and inventions. Students build their own shared database of discoveries, classes, organizations, research openings, internships, and public service options. Students find next step opportunities so they can see future careers. Description of our program here provides the necessary context for our future publications on assessment that examine 21st century skills, persistence in STEM, and creativity.
... Already several search tools have been developed which can be roughly categorized as three different approaches: direct consultation of a biologist, using a specially prepared database and using natural language biological documents for identifying bio-inspiration (Willocx et al., 2020). A major drawback of consulting an expert biologist is the limit of his or her current knowledge and the possible bias to his/her own research domain (Shu et al., 2011;Graeff et al., 2020). The major issue with database approaches is the enormous effort that is required for the population of the database with biological strategies Graeff et al., 2019a). ...
Article
Full-text available
Using bio-inspiration allows engineers to use the knowledge implicitly built up by natural evolution. Current tools for providing engineers with bio-inspiration yield many biological working principles. Starting from the Linnaean taxonomy, which can be seen as a design revision history, this work proposes metrics for a working principle based on the observations of that working principle in different organisms. A first metric measures the reinforcement of a working principle via the number of observations (publications/submissions to a database) made by biologists. Furthermore, biological strategies that evolve independently and use the same working principle might be more resilient and globally applicable, prompting the proposal of a metric measuring the spread in the taxonomy. Finally, bio-novelty measures the biological novelty, inversely related to the biological diversity employing the working principle. To illustrate the use of the metrics, they are applied to the working principles identified in the ‘temporary attachment’ category of AskNature.
Chapter
The article presents the views of employers and students about the demand for interdisciplinary knowledge in technical and technological programs of higher education. The regional survey was attended by 29 experts from among the chief and leading engineers of agricultural enterprises and 47 graduates of engineering programs in 2019 of the Stavropol Territory; 176 people from among students of 2nd–4th courses of faculties of economics, biotechnology. The survey database was processed in SPSS Statistics (version 21) Using the principal component extraction method with Varimax Rotation and Kaiser Normalization, models of engineering education were generated, and we determined the boundaries of the use of an interdisciplinary approach in the training of engineering personnel. An algorithm for mathematical modeling of engineering educational programs is proposed, taking into account the views of stakeholders and an interdisciplinary approach in the formation of graduates’ competencies.
Thesis
Full-text available
La recherche scientifique sur les méthodologies de conception biomimétique fait aujourd’hui face à la difficulté majeure de concrétiser sa mise en place dans les pratiques d’innovation. Dans ce contexte, et face aux nombreux freins qui en découlent, cette thèse de doctorat soulève la question des métiers associés à la pratique de la biomimétique. Plus spécifiquement, l’absence de professionnels formés d’une part à la conception biomimétique et d’autre part aux sciences du vivant rend particulièrement complexe l’accès et l’analyse des données issues de la biologie. À travers un ensemble d’études descriptives et prescriptives, le profil du biomiméticien, professionnel spécialisé en biomimétique et en biologie, est formalisé. La préconisation de ses activités pratiques, impliquant notamment des concepts, des méthodes et des outils issus de la biologie, menent alors à la définition des compétences de ce nouveau membre des équipes de conception biomimétique. Pour assurer son intégration, un processus de conception biomimétique technology-pull interdisciplinaire et un outil accompagnant la pratique collaborative des équipes sont proposés.
Conference Paper
Full-text available
The strength of biomimetics comes from its ability to draw from life mechanisms and strategies to design innovative solutions. In spite of recent methodological progresses, more specifically on tools and processes, biomimetics' implementation still faces strong difficulties. Among other things, design teams have a hard time finding and selecting relevant biological strategies. Facing these challenges, we consider an alternative, yet well recognized, approach: the integration of profiles having a training in natural science within biomimetic design teams. As biologists aren't used to work in design teams, there is a need for a process actually guiding their practice in biomimetics and determining the way they will interact with the “traditional” design team. After studying the literature and asking for experts' opinion on the matter, we introduced a biomimetic design process considering this new profile as an integral part of biomimetic design teams. With the final goal of making biomimetics implementable, this proposed theoretical process is currently tested in both a student and an industrial project in order to optimize our methodological contribution with practical feedbacks.
Article
Full-text available
Engineering design, as the science framing the practice of design through the elaboration of tools and processes, is constantly evolving towards new innovative strategies. To thrive in their extremely competitive environment, it appears that both industrial and natural worlds are highly dependent on innovation, optimisation and selection. These commonalities have led designers to look to living beings for inspiration. This innovation strategy, referred to as biomimetics, isn’t a new approach but its methodological aspects are still under development. This article deals with biologists’ contribution throughout the biomimetic design process. After introducing the context and the experimental protocol, we investigated the impact of possessing a background in biology during the practice of biomimetics and compared our findings with experts’ opinion. The main idea of this article is to show that to forego the integration of biologists is highly restrictive and may be one of the reasons explaining the difficulties of implementing biomimetics in the industrial context. Hence, this article argues for a new methodological framework taking into account biologists, allowing biomimetic teams to become truly interdisciplinary.
Article
Full-text available
Biologically inspired design (BID) is an emerging field of research with increasing achievements in engineering for design and problem solving. Its economic, societal, and ecological impact is considered to be significant. However, the number of existing products and success stories is still limited when compared to the knowledge that is available from biology and BID research. This article describes success factors for BID solutions, from the design process to the commercialization process, based on case studies and market analyses of biologically inspired products. Furthermore, the paper presents aspects of an effective knowledge transfer from science to industrial application, based on interviews with industrial partners. The accessibility of the methodological approach has led to promising advances in BID in practice. The findings can be used to increase the number of success stories by providing key steps toward the implementation and commercialization of BID products, and to point out necessary fields of cooperative research.
Article
Full-text available
Biological systems have evolved over billions of years and cope with changing conditions through the adaptation of morphology, physiology, or behavior. Learning from these adaptations can inspire engineering innovation. Several bio-inspired design tools and methods prescribe the use of analogies, but lack details for the identification and application of promising analogies. Further, inexperienced designers tend to have a more difficult time recognizing or creating analogies from biological systems. This paper reviews biomimicry literature to establish analogy categories as a tool for knowledge transfer between biology and engineering to aid bio-inspired design that addresses the common issues. Two studies were performed with the analogy categories. A study of commercialized products verifies the set of categories, while a controlled design study demonstrates the utility of the categories. The results of both studies offer valuable information and insights into the complexity of analogical reasoning and transfer, as well as what leads to biological inspiration versus imitation. The influence on bio-inspired design pedagogy is also discussed. The breadth of the analogy categories is sufficient to capture the knowledge transferred from biology to engineering for bio-inspired design. The analogy categories are a design method independent tool and are applicable for professional product design, research, and teaching purposes.
Article
Full-text available
Computer-Aided Biomimetics (CAB) tools aim to support the integration of relevant biological knowledge into biomimetic problem-solving processes. Specific steps of biomimetic processes that require support include the identification, selection and abstraction of relevant biological analogies. Existing CAB tools usually aim to support these steps by describing biological systems in terms of functions, although engineering functions do not map naturally to biological functions. Consequentially, the resulting static, functional view provides an incomplete understanding of biological processes, which are dynamic, cyclic and self-organizing. This paper proposes an alternative approach that revolves around the concept of trade-offs. The aim is to include the biological context, such as environmental characteristics, that may provide information crucial to the transfer of biological information to an engineering application. The proposed design process is exemplified by an illustrative case study.
Article
Full-text available
Innovative designs can be limited by the experience and knowledge of the individual designers. These limitations can be combated by integrating knowledge from other fields of study. However the vernacular used by the respective fields creates a gap, which requires a process of facilitated translation to bridge. Specifically, biology is a field that has been used for inspiration for design solutions by engineers. This field, also known as biomimicry, has already proven to be a successful tool for identifying innovative inspiration for design but is under-utilized due to the difficulty of translating between biology and engineering. The proposed bioinspired design process discussed in this paper uses sketching of biological systems to create visual analogies that facilitate the translation from a biological system to technical design solution. Sketching of the inspiring biological system re-conceptualizes the information to aid in comprehension as well as establishes a clear path to cross-domain analogy identification to result in by innovative technical design solutions. This paper focuses on the theory underlining the proposed bioinspired design process, and as such, reviews existing literature to identify the opportunity, states the supporting evidence, and presents the approach for addressing the opportunity with an example and a case study to demonstrate applicability.
Article
This research aims to understand the significance of biological-analogies in fostering novelty by comparing biological-analogies with other design methods for idea generation. Among other design methods, brainstorming was chosen here as benchmark. Four studies were conducted to compare: (i) the levels of abstraction at which concepts were ideated using biological inspiration (represented using biocards) with that using traditional brainstorming; and (ii) the novelty of concepts produced by using these two design methods. Concepts produced in these studies were evaluated for levels of abstraction at which they were ideated, average novelty, and proportion of high-novelty concepts. Results suggest that concepts generated using biocards were ideated at higher abstraction levels than those using brainstorming, but neither were at the highest abstraction levels. The average novelty of concepts produced using biocards was found to be greater than that using brainstorming; however, no statistically significant difference was found in the proportion of high-novelty concepts. We suspect the lack of biological knowledge and cultural difference among the subjects involved in our studies as the two reasons behind the results. The results demonstrate that the design methods substantially influence the novelty of concepts generated, while indicating the need for better training in effective use of biological-analogies.