ArticlePDF Available

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

July 2020
Journal of Mechanical Design

DOI:10.1115/1.4047905

Authors:

Eliot Graeff

Ceebios

Nicolas Maranzana

Ecole Nationale Supérieure d'Arts et Métiers

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.

Unified technology pull biomimetics process optimized for risk management

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Communication theory applied to a biomimetic design team (based on Shannon model and communication theories)

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Example of a generic molecular pathway, from signal fixation (molecule A) to the secretion of a response protein (protein p)

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From a functional analysis representation to a nested standpoint. Interacting sub-systems are connected through dashed lines.

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Example of LINKAGE's formalization focused on levels of organization, for the surface of a boat's hull and the surface of Salvinia molesta. Pictures based on Ref. [75].

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Figures - uploaded by Eliot Graeff

Content may be subject to copyright.

Content uploaded by Eliot Graeff

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

Engineers’and 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 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 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 scientiﬁc 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 deﬁned as “the interdisciplinary cooperation

of biology and technology or other ﬁelds 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. Scientiﬁc literature underlines interdisciplinary communica-

tion as one of biomimetics’main challenges [5–9]. Following a

literature review on biomimetics and communication theories, a

set of impediments to communication are identiﬁed 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 deﬁned 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 tool’s 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 uniﬁed 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; ﬁnal manuscript received June 23, 2020; published online October 12, 2020.

Assoc. Editor: Daniel A. McAdams.

uniﬁed 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 speciﬁc 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 ﬁndings.

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 ﬁnd

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 uniﬁed process)?

Consequently, the gap between the engineering and biological

ﬁelds (knowledge, reasoning, data structure, etc.) is one of the

main issues. In previous work, we drew a distinction between two

types of biologists. “Vertical biologists”to refer to biologists who

punctually interact with design team and have an in-depth expertise

in a speciﬁcﬁeld (i.e., a vertical knowledge) identiﬁed as research-

ers, and “Horizontal biologist”to 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

proﬁles. 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 identiﬁes 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 Teams’Issues

2.3.1 Origins and Related Concepts. Also known as the infor-

mation theory, the ﬁrst 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 ﬁve 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,”modiﬁes the

signal at its emission, during its transmission or at its reception,

altering the encoded message. Shannon’s 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/receiver’s 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 Uniﬁed 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-

ogists’and an engineers’habits, 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 source’s and receiver’s 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 engineer’s 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 signiﬁcantly vary. Among other signals,

engineers transmit information in the form of requirements speciﬁ-

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 project’s 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/

receiver’s defaults, like researchers’bad communication skills, are

not addressed.

To summarize, interdisciplinary teams need guidance on seman-

tic and psychological noises to allow actors to keep their speciﬁci-

ties while communicating efﬁciently.

2.4 Lack of Communication, a Challenge for Biologists’

Integration. The inability to communicate efﬁciently 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.

Aﬁrst set of studies focuses on semantic issues during the search

for biological information by engineers. To support this search

phase, Cheong et al. identiﬁed 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 ﬁnal 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 ﬁnd-

ability by linking biological models with technical models and to

generate databases of structured biological information. The ﬁrst

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 speciﬁcally 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 ﬂow-based functional model. Nagel et al.

designed such model, presenting the ﬂows 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 Miller’s Living System Theory [48] and engineering,

using TRIZ [49]. The model “tries hard to allow people, without

any speciﬁc biological knowledge, to arrange by relevancy the

identiﬁed biological systems on their own.”As speciﬁed by

Fayemi, it is a tool made for “designers with limited biological

knowledge”and to delay biologists’intervention.

According to the concepts presented in Sec. 2.3, these contribu-

tions deal with the implementation of generic signals’formats to

support message encoding/decoding and data storage in an artiﬁcial

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 ﬁelds acting

as sources/transmitter, and receiver/destination which is not the case

in the abovedescribed studies.

The second noise identiﬁed in Sec. 2.3.2 is the lack of common

context (psychological noise). This communication brake focuses

on the conceptual framework (commonness, scientiﬁc 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 ﬁeld-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 students’projects [53]. Through the deﬁnition of

the problem’s 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, Speciﬁcations, 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

ﬁnding 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 ﬁnal 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 identiﬁed

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 scientiﬁc 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-

tiﬁed 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 identiﬁed 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 speciﬁc 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 ﬁrst step in communication, the stability

of the team’s 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 speciﬁcally

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 biologists’and engineers’ways

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 speciﬁ-

cally, we wonder if we can deﬁne 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 ﬁelds, 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 team’s 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 speciﬁcities 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 deﬁnition 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 Choi’sdeﬁnition

[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

whole”to reach a common goal.

To assist communication within these teams, as well as their

“coordinated and coherent”practices, 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.

Stakeholders’mental representations of information, and so the

subject of cognition, are deﬁned 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-

plinary”practice 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 ﬁeld 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 ﬁeld-speciﬁc 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 uniﬁed framework.

In the case of our study, the framework analysis (1) is based on

the literature, the authors’experiences, 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 speciﬁcities of biomimetic practices, and the hypothesis of

this article. Guidelines composing the framework are then synthe-

sized in Table 3(4).

Following this ﬁrst 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 uniﬁed 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-Speciﬁc 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 deﬁned as

the “general input/output relationship of a system whose purpose

is to perform a task”or, at the early stages of the process, as the

“abstract formulation of the task, independent of any particular

solution”[62]. The term “system”is considered after De Rosnay’s

deﬁnition, “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/company’s 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 it’s the consumers’acceptance, 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 users’disposal.

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

identiﬁcation 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 experimenter’s 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 ﬁnal

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 identiﬁed (phase 2, Sec. 4.2). Guidelines

synthesizing the various criteria are then formulated (phase 3,

Sec. 4.2). Five guidelines have been identiﬁed 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, ﬁeld-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 ﬁelds, 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 solution’s validation (concept 11, Table 1) and ﬁnally

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

biologists’system 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 ﬁeld-speciﬁc 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 scientiﬁc processes, this section presents

the identiﬁcation of additional, more biology-speciﬁc, 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 scientiﬁc articles.

Within biological articles, ﬁgures 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 speciﬁc to biology. Through the literature, the

authors’experience in biology and the discussion with biology tea-

chers–researchers, 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 proﬁt from the product’s

sales

Absence of knowledge, and/or ﬁnancial

incentives toward a biological truth

Where projects in engineering are

based on strategic choices and target a

consumer, projects in biology are

based on scientiﬁc knowledge and

target a scientiﬁc 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

doesn’t exist yet

Concrete: The studied system is observed

7. Solving process’inputs Abstracted function and associated

technical problems

Abstracted biological generic concepts

associated with a set of rules

Both proﬁles 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 identiﬁcation of

biological models sharing the same or

close abstracted function/technical

problems

Based on concepts’interaction and

associated biological principles.

Resolution in the conceptual space.

Analogical reasoning may also be used

between biological studies

9. Solving process output Identiﬁed 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. Identiﬁcation 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 system’s 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

efﬁciently the function and what are

users’feedback

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 ﬁndings

13. Final output A product An increase of knowledge Where a system is the ﬁnal 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

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representation that we characterized as observational, nested, and

dynamic.

5.2.1 Observational Representations. This ﬁrst type of repre-

sentation reﬂects biologists’observations. 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

speciﬁcities 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 ﬁelds, 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 ﬁgure,

different magniﬁcations, 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 systems’properties. 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 ﬁrst 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 speciﬁcﬂows, 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 ﬁrst 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 modiﬁcations of

the biological entities: growth, differentiation, evolution, etc.

Finally, mixed processes are identiﬁed. 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 lungs’degradation 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,

(ﬁg. 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 artiﬁcially 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 ﬁeld 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 speciﬁc forms in order to consider functions, such as

“dealing with internal and external energy”or “managing informa-

tion”[71].

In engineering, the types of encoding are strictly characterized,

they follow well-deﬁned structures depending on the phases of

the project and the required level of abstraction. In order to validate

the previously identiﬁed 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

system’s 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

ﬁxation (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 product’s

legitimacy (client brief,

Octopus diagram, etc.)

Represents the consequences

of the forms’and 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

system’s 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.)

(ﬁnal steps)

Initial observation of a

biological entity in the real

world (observational

representation, etc.) (early

steps)

Where form’s representation

in engineering is an

embodiment of subfunctions,

form’s representation in

biology is a clue toward the

solving of a research question

Guideline H: Be speciﬁc

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

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and technical visual channels along with signals’encoding (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 speciﬁc on forms. Forms are the ﬁrst 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 ﬂuids 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 teammates’shared 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 teams’communication 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 “prescriptive”versus “descriptive”reasonings in the

literature [37].

First, reasoning shifts regarding processes have been identiﬁed

on processes and summarized into ﬁve guidelines (Table 1).

The analysis then focused on visual communication channels,

identiﬁed in the literature as a potential lever for improving commu-

nication [54], and questioned messages’encoding/decoding,

signal’s 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 biologists’integration, 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 speciﬁc 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-speciﬁc common frameworks based on the

various structuring generic guidelines identiﬁed in Sec. 5.

6.1 LINKAGE’s Goals. LINKAGE’sﬁrst goal is to assist

communication between “classic”design 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 proﬁt-

ability, and recognize the increase of knowledge as an objective

(Guideline E).

The long-term goal of this tool is to increase biomimetic design

teams’effectiveness and to contribute to biomimetics implementa-

tion and spreading.

6.2 LINKAGE’s Formalization. LINKAGE’s building is

presented through examples and following the steps of the uniﬁed

problem-driven process [12]. Guidelines from the common frame-

work used to design LINKAGE are explicitly identiﬁed. 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 ﬁlter a liquid without clog-

ging? usually comes from the following cognitive reasoning:

Through time, residual elements of the ﬁltered liquid accumulate

at the ﬁlter’s 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 ﬁrst 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 ﬁlter is discarded after it clogged, it is

cyclical if the ﬁlter is cleaned and reused. LINKAGE suggests

looking at the problem through the second angle, allowing to

reach back the initial state. Logically, the ﬁlter 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 ﬁrst 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 speciﬁcally 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, speciﬁc 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 LINKAGE’s inputs can come

from functional analysis tools such as “Octopus diagram”from

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

team’s 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 plane’s 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 system’s dynamics in imple-

mented through the states and patterns previously deﬁned.

System’s functional features linked with the problem/solution are

thus represented.

If we take the example of the ﬁlter previously used, the ﬁlter and

liquids are linked during the structural description, but the ﬁlter’s

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 n−2 states. Guide-

lines which embodiments can be directly perceived on the represen-

tation are speciﬁed in red (G.

,G.

and G.

)

(Fig. 10). Through this state-based functional reasoning,

LINKAGE allows interdisciplinary design teams to express the

system’s 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. LINKAGE’sﬁnal structure combines elements from both

ﬁelds 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 fulﬁl its func-

tion through processes.

7 Intended Contributions

Through the description of LINKAGE’s 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, identiﬁed 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-

ciﬁc spaces are dedicated for them at ﬁrst. 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) ﬁt?”may be the ﬁrst 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 LINKAGE’s formalization focused on levels of

organization, for the surface of a boat’s hull and the surface of

Salvinia molesta. Pictures based on Ref. [75].

Fig. 10 Empty version of a LINKAGE representation with guide-

lines’embodiments (G

, and G

)

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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 ﬁeld-

speciﬁc differences, supporting the avoidance of disruptive

overlaps.

LINKAGE structures the combination of prescriptive and

descriptive approaches and is expected to guide the teams’practice

toward the identiﬁcation of several layers of information and several

use phases on a single shared representation.

The ﬁrst step of the biomimetic design problem is the deﬁnition 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 deﬁnition 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 elements’interaction involved

in the system’s change of state should lead the team to deﬁne 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

deﬁnition 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 Guidelines’embodiment on LINKAGE tool

Guidelines Embodiment

Guideline A: Embed subjective elements in external constraints Characterization of the super-system and of the system’s various states

(Fig. 10,G

)

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

)

Guideline D: Present problems and solutions within their

spatiotemporal contexts

Deﬁnition of super-systems (depend on space and time) and states (depend on

duration) (Fig. 10,G

)

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

scientiﬁc value

Guideline F: Consider system’s evolution through state-based

reasoning to represent functions

Evolution patterns through states (Fig. 10,G

)

Guideline G: Model systems through nested structures Nested structure and interaction through double-head arrows (Fig. 10,G

)

Guideline H: Be speciﬁc on forms A speciﬁc space is dedicated for each level to characterize the features of

involved elements (Fig. 10,G

)

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 LINKAGE’s 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 Deﬁnition 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 identiﬁed, 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 speciﬁc 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 scientiﬁc rele-

vance of gathered data while the rest of the team is guided to deﬁne

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 ﬁtting 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 ﬁeld 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, biologists’system 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 noises’source, the encoding and decoding pro-

cesses (Sec. 2.3.2).

First, the variability of mental models representing the project’s

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 frameworks’differences, a relevant

message can be inadequately encoded or decoded. This aspect

does not appear speciﬁc to interdisciplinary teams, but the risk of

non-compatible mental representations seems higher in such cases.

Through the incorporation of the project’s 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 deﬁned by the

co-designed LINKAGE representations.

Second, semantic noises were identiﬁed 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 ﬁeld-

speciﬁc representations. As a result, LINKAGE should give teams

the ability to encode ﬁeld-speciﬁc 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 speciﬁc encoding/decoding processes. Section

7.3 presents what LINKAGE’s speciﬁcities are compared with the

other available tools.

7.3 LINKAGE, Why Isn’t 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 ﬁelds, this publication inves-

tigates interdisciplinary team’s internal communication. Following

guidelines involving a series of cognitive shifts, we presented a

framework designed for practitioners to ﬁnd 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 proﬁle

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 ﬁelds 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 difﬁculty for users to combine differ-

ent forms of information (image, video, scientiﬁc 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

Speciﬁc aim Gives a way to perform one of the biomimetic

process’step

Structure the information gathered by the team and guide the communication

process to reach a synergic reasoning

Fitting user proﬁle Mostly unidentiﬁed 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 speciﬁc Contribution identiﬁed 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

tool’s 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 identiﬁcation and the selec-

tion of biological models.

On a more general note, our current work focuses on the remain-

ing issues about biologists’integration, which is the ill-deﬁned hor-

izontal biologist proﬁle.

The difﬁculty 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 proﬁle?) before determining and training (How

to match design team expectations?) a ﬁtting new proﬁle 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 proﬁle of interest is

a biologist per se or another type of proﬁle (engineer, designer,

etc.) having followed speciﬁc training in biology remains unsolved

and will be extensively studied in coming articles.

9 Conclusion

This publication dealt with the integration of a new proﬁle 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

reﬂection. Anticipating on those challenges to fully beneﬁt from

biologists’valuable expertise appears as a requirement to support

their proper integration.

Through the literature review, we identiﬁed 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 ﬁeld-speciﬁc vocabulary.

The research question studied in this article was: “Can we deﬁne

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 ﬁrst 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 users’feedback, 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(10–12), pp. 654–680.

[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. 427–436.

[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. 153–199.

[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. 12–13.

[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. 53–66.

[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, “Biomimetics—Using Nature to Inspire Human

Innovation,”Bioinspir. Biomimet.,1(1), pp. P1–P12.

[11] Vincent, J. F. V., 2009, “Biomimetics—A Review,”Proc. Inst. Mech. Eng., Part

H,223(8), pp. 919–939.

[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. 355–360.

[14] Lenau, T., 2009, “Biomimetics as a Design Methodology—Possibilities and

Challenges,”International Conference on Engineering Design, ICED,

Standford, pp. 121–132.

[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. 127–152.

[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(3–4), pp. 211–234.

[17] Graeff, E., Maranzana, N., and Aoussat, A., 2019, “Engineers’and Biologists’

Roles During Biomimetic Design Processes, Towards a Methodological

Symbiosis,”International Conference on Engineering Design –ICED, Delft,

The Netherlands, Aug. 5–8, pp. 319–328.

[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. 471–482.

[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. 19–22, pp. 347–356.

[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 27–30, pp. 225–234.

[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. 521–535.

[25] Sartori, J., Pal, U., and Chakrabarti, A., 2010, “A Methodology for Supporting

‘Transfer’in Biomimetic Design,”Artif. Intell. Eng. Des. Anal. Manuf.

AIEDAM,24(4), pp. 483–505.

[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. 34–59.

[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.

15–18.

[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 19–22.

[30] Snell-Rood, E., 2016, “Interdisciplinarity: Bring Biologists Into Biomimetics,”

Nature,529(7586), pp. 277–278.

[31] Graeff, E., Maranzana, N., and Aoussat, A., 2019, “Biomimetics, Where Are the

Biologists?,”J. Eng. Des.,30(8–9), pp. 289–310.

[32] Helms, M. E., Vattam, S. S., and Goel, A. K., 2009, “Biologically Inspired

Design: Process and Products,”Des. Stud.,30(5), pp. 606–622.

[33] Shannon, C. E., 1948, “A Mathematical Theory of Commun ication,”Bell Syst.

Tech. J.,27(3), pp. 379–423.

[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. 1–578.

[36] Devito, J. A., 2013, Interpersonal Communication Book, 13th ed., Vol. 1,

Pearson, London, pp. 1–432.

[37] Bogatyrev, N. R., and Bogatyreva, O. A., 2015, “TRIZ-Based Algorithm for

Biomimetic Design,”Procedia Engineering, Vol. 131, D. Cavallucci, G.

Cascini, J. Duﬂou, P. Livotov, and T. Vaneker, eds., Elsevier, New York, pp.

377–387.

[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. 1–5.

[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. 15–18, pp. 117–128.

[42] Biomimicry Institute, 2002, “AskNature—Innovation Inspired by Nature,”

AskNature, pp. 27–29. 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. 269–285.

[44] Vincent, J., and Cavallucci, D., 2018, “Development of an Ontology of

Biomimetics Based on Altshuller’s 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. 14–25.

[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.

115–122.

[46] Chakrabarti, A., Sarkar, P., Leelavathamma, B., and Nataraju, B. S., 2005, “A

Functional Representation for Aiding Biomimetic and Artiﬁcial Inspiration of

New Ideas,”Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM,19(2), pp. 113–132.

[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 Scientiﬁc Knowledge From Academicians to

Practitioners: Qualitative Evidence From the Commercialization of University

Technologies,”J. Eng. Technol. Manag.,21(1–2), pp. 115–142.

[51] Falkenhainer, B., Forbus, K. D., and Gentner, D., 1989, “The Structure-Mapping

Engine: Algorithm and Examples,”Artif. Intell.,41(1), pp. 1–63.

[52] Gentner, D., 1983, “Structure-Mapping: A Theoretical Framework for Analogy,”

Cogn. Sci.,7(2), pp. 155–170.

[53] Helms, M. E., and Goel, A. K., 2014, “The Four-Box Method of Problem

Speciﬁcation 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. 17–20, 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 27–30, ICED, pp. 215–224.

[55] Mathieu, J. E., Goodwin, G. F., Heffner, T. S., Salas, E., and Cannon-Bowers,

J. A., 2000, “The Inﬂuence of Shared Mental Models on Team Process and

Performance,”Psychol. Assoc. Inc,85(2), pp. 273–283.

[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. 387–403.

[58] Lotrecchiano, G., 2010, “Complexity Leadership in Transdisciplinary (TD)

Learning Environments: A Knowledge Feedback Loop,”Int. J. Transdiscipl.

Res., 5(1), pp. 29–63.

[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. 9683–9687.

[61] Bernard, C., and Wolf, S., 1999, Experimental Medicine, 1st ed., Vol. 1,

Routledge, New York, pp. 23–32.

[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 19–22, pp. 173–182.

[65] Simon, H. A., 1969, Th e Sciences of the Artiﬁcial, 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. 1–25.

[67] Wainwright, S. A., 1988, “Form and Function in Organisms,”Integr. Comp.

Biol., 28(2), pp. 671–680.

[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. 43–43.

[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. 1–475.

[71] Waterman, T. H., 1968, “Systems Theory and Biology—View of a Biologist,”

Systems Theory and Biology, 1st ed., Vol. 1, M. D. Mesarović, ed., Springer,

Berlin, Heidelberg, pp. 1–37.

[72] Karsenti, E., 2008, “Self-Organization in Cell Biology: A Brief History,”Nat.

Rev. Mol. Cell Biol.,9(3), pp. 255–262.

[73] Jacob, F., 1993, The Logic of Life: A History of Heredity, New edition, Princeton

University Press, Princeton, pp. 1–362.

[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. 2325–2328.

041402-14 / Vol. 143, APRIL 2021 Transactions of the ASME

Linkage, an Online Tool to Support Interdisciplinary Biomimetic Design Teams

Article

Full-text available

Oct 2021
J MECH DESIGN

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.

Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL-MAIRCA based on fuzzy rough numbers

Article

Full-text available

Jun 2021
INT J INTELL SYST

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.

Evaluation methods of biomimetic development: how can we compare and select topics?

Article

Mar 2023

Biomimetics is a technology that utilises biological structures for manufacturing, and is attracting attention globally in a wide range of fields. However, there are challenges in sharing and communicating development between researchers with different specialties. Biomimetic developments have conventionally been expressed in terms of unique or unconnected indicators, which makes it difficult to understand for outsiders and compare different topics. In this study, evaluation guidelines were developed to effectively share and compare. As the method is directly related to actual development, it is a new methodological framework used in the target-setting phase. Several indicators and criteria with both biology and engineering were set up, proposing ways of evaluating and expressing development topics. And, actual evaluations and comparison were performed on several examples. This study discusses the possible outcomes from the evaluated results, the applied use and the challenges of the evaluation method. A major advancement came with the use of composite indicators to evaluate results, which can be easily compared with other topics. It is expected to contribute to smoother communication and process, and to support decisions on the direction of development.

Eyes Toward Tomorrow Program Enhancing Collaboration, Connections, and Community Using Bioinspired Design

Article

Full-text available

Aug 2021

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.

METRICS FOR BIO-INSPIRATION BASED ON TAXONOMIES

Article

Full-text available

Aug 2021

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.

Interdisciplinary Effects in Modeling Technical and Technological Programs of the University

Chapter

Jan 2022

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.

Innovation bio-inspirée : Modélisation d'un processus interdisciplinaire de conception biomimétique outillé et intégration d'un nouvel acteur, le Biomiméticien.

Thesis

Full-text available

Jul 2020

Eliot Graeff

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.

On the Ethics of Communication Between Humans and AI

Chapter

Full-text available

Sep 2022

What is the status of communication with a virtual entity? To answer this question, we must turn to the philosophy of language. Here, the philosopher of language Paul Grice with his “intentionalist semantics” is particularly important to show that virtual entities, for example in the form of chatbots, do not “intend” anything with their utterances. They have no mental states, so they can neither make decisions nor communicate. No matter how much bots suggest they are communicating agents, they are only ever capable of apparent communicative acts. They may look like communicative acts, but they are not. Without intentionality, there is no meaning.

TRANSDISCIPLINARY DESIGN PRACTICES IN EDUCATION: A COMPLEX SEARCH FOR INNOVATION IN NATURE

Conference Paper

Full-text available

Sep 2019

Engineers’ and Biologists’ Roles during Biomimetic Design Processes, Towards a Methodological Symbiosis

Conference Paper

Full-text available

Aug 2019

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.

Biomimetics, where are the biologists?

Article

Full-text available

Jul 2019

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.

What Do We Learn from Good Practices of Biologically Inspired Design in Innovation?

Article

Full-text available

Feb 2019

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.

Establishing Analogy Categories for Bio-Inspired Design

Article

Full-text available

Nov 2018

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.

Development of an Ontology of Biomimetics Based on Altshuller’s Matrix: 18th International TRIZ Future Conference, TFC 2018, Strasbourg, France, October 29–31, 2018, Proceedings

Chapter

Full-text available

Oct 2018

Towards a Design Process for Computer-Aided Biomimetics

Article

Full-text available

Jun 2018

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.

Facilitation method for the translation of biological systems to technical design solutions

Article

Full-text available

Jan 2018

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.

The Logic of Life, A History of Heredity

Article

Dec 2022

François Jacob

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

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