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Modeling biological systems to facilitate their selection during a bio-inspired design process

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Abstract and Figures

The bio-inspired design process implies a multiplicity of actors. Engineers and biologists are usually among them. Mobilize cross-disciplinary and/or highly specialized biologists is a complex task and tools have been developed to address this specificity of the biomimetic approaches. However, the selection of biological model(s) of inspiration does not appear to have yet been tackled. This paper aims at proposing a way to define a benefit/effort ratio for considered biology to technology analogies, which should allow designers to sort these analogies on their own, easing the global biomimetic process. For such need, the paper presents a model revolving around the concepts of ideality and resources coupled with Living System Theory principles. The thorough analysis proposed here shows a consideration on what biological systems are, particularly for a bio-inspired design purpose. This analysis feeds the discussion on how biological systems could be appropriately modeled in order for them to be compared with technical ones, which is the initial need for the described model completion.
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Fayemi, Pierre-Emmanuel Ifeolohoum (1,2); Maranzana, Nicolas (1); Aoussat, Ameziane (1);
Chekchak, Tarik (3); Bersano, Giacomo (2)
1: Arts & Métiers ParisTech, France; 2: Active Innovation Management, France; 3: EFREI, France
The bio-inspired design process implies a multiplicity of actors. Engineers and biologists are usually
among them. Mobilize cross-disciplinary and/or highly specialized biologists is a complex task and
tools have been developed to address this specificity of the biomimetic approaches. However, the
selection of biological model(s) of inspiration does not appear to have yet been tackled. This paper
aims at proposing a way to define a benefit/effort ratio for considered biology to technology analogies,
which should allow designers to sort these analogies on their own, easing the global biomimetic
process. For such need, the paper presents a model revolving around the concepts of ideality and
resources coupled with Living System Theory principles. The thorough analysis proposed here shows
a consideration on what biological systems are, particularly for a bio-inspired design purpose. This
analysis feeds the discussion on how biological systems could be appropriately modeled in order for
them to be compared with technical ones, which is the initial need for the described model completion.
Keywords: Bio-inspired design and biomimetics, Design methodology, Biological System,
Pierre-Emmanuel Ifeolohoum Fayemi
Arts & Metiers Paristech
Product Design and Innovation Laboratory (LCPI, EA 3927)
Please cite this paper as:
Surnames, Initials: Title of paper. In: Proceedings of the 20th International Conference on Engineering Design
(ICED15), Vol. nn: Title of Volume, Milan, Italy, 27.-30.07.2015
In a world threatened by an extremely strong, strategic and perpetually growing competition,
innovation used as a differentiating factor is really important in our modern economies (Paul 2011).
Faced with these constraints due to globalization, the cycles of innovations have shortened to such an
extent that we now speak of continual innovation (Boer and Gertsen, 2003). Over the same period, a
new paradigm came into being. Our societies have been confronted to the finiteness of reserves made
available for them by their environment (Tilton, 1996). Due to the paradox between the accelerated
obsolescence of our products through the speeding up of the cycles of innovations and the growing
environmental constraints, designers now have to rethink their activity so they can offer responsible
Many technical challenges are still to be solved. Technology seems to have trouble in rising to the
challenge of solving these said challenges resiliently. Bio-inspiration and its methodological form,
biomimetics, aim at taking advantage of nature’s perspective regarding solving problems it came
across. This problem solving is based on living things’ genetic variability, coupled with the principle
of natural selection which allows, over the generations, the emphasis of some characteristics. This
whole process leads to a mechanism of trial and error. This mechanism would not be working without
the resource which seems to be lacking: time. Without this “time” made available, it consequently
seems interesting to try to understand how nature works by getting to understand what the biomimetic
process is, thus allowing us to free ourselves from the trial and error mechanism.
First of all this article will explain the various biomimetic processes described in different works, then
it will identify a need within the biomimetics toolset and will address it by proposing a consideration
upon biological systems specificities and modeling.
Some biomimetic problems solving processes, also called problem-driven or techno-push, have been,
in this respect, described in various works. Lindemann and Gramann (2004) have offered the bionic
procedural; Bogatyrev (2008) hybridized bio-inspired design and TRIZ in a 6 step model; Lenau
(2009) proposed the biomimetic as a design methodology model; Helms et al. (2009) proposed a
design process providing iterative feedback and refinement loops; Nagel et al. (2014) proposed a
model focusing on the functional establishment of a pattern/model of biological models;
Even though showing significant differences (e.g. whether feedback loops are present or not,
differences in focus, problem-driven/solution-driven aspect or both, etc.) these various processes share
some components (i.e. identification of the technical problem, a transition phase to the biological
domain, a research phase of biological models, an abstraction phase of the biological strategie(s), a
translation phase from the biology field to the technical one).
These shared elements have led to a model based on the basic outline of mentioned processes (Fayemi
et al., 2014):
Figure 1. Merged problem-driven biomimetic process (Fayemi et al., 2014)
This procedural model, as shown in figure 1, highlights the need for biological knowledge in two
specific places. The first one occurs in the cycle on the left, the one concerning the transition from the
technological to the biological field. The contribution of biological knowledge is used here to
transpose the abstraction of the technical problem over to the biological field, and then to identify the
living systems which could be able to provide answers. Here, the identified need is thus the one of one
or several horizontal biologists (i.e. biologist(s) with cross-disciplinary knowledge), able to create
pathways between the technical and biological fields with the capability to identify a critical potential
relevant pool of living systems. The second biological contribution takes place in the right cycle of
figure 1, it leads back biological knowledge into technical field. Here is the need of one or several
vertical biologists, experts enough in considered biological system to authenticate the relevance of the
analogy, to turn it into an abstracted model and to implement it in the initial problematic situation. The
amount of needed experts is based on identified biological systems at the previous step.
The way of acting makes biologists step in the bio-inspired design process at an early stage. However,
the population of biologists, being able to play the part of horizontal biologists who control the
language and understand both biological and technical issues is very much restricted. It is an illusion
to imagine that in the short term, the critical mass of such profiles will be sufficient enough to answer
the needs generated by the recent keen interest in bio-inspired design. Although we could think that
specialized biologists would be ready to be enlisted because of the enhanced value of their work and
because of the financial contribution given by the industry, which is likely to finance their research.
The situation was found to be substantially different. Cummings and Kiesler (2005) have highlighted
that the specialization on one field led to problems of communication as far as knowledge sharing
from specialists to non-specialists is concerned. The process of mobilizing these vertical biologists
proves to be tedious as well as time consuming.
Democratization of bio-inspired design, in the light of these facts, seems to ask for a careful
consideration on how the transfer of knowledge and the involvement of biologists could be eased in
the heart of its process.
During a knowledge transfer process, a lot of barriers exist: lack of mutual understanding of culture,
context, constraints, goals; insufficient reward system (Siegel et al., 2004); mutual understanding of
processes and outcome; confidentiality (Bruneel et al., 2010). In order to overcome these barriers,
researchers have, over the past decade, developed tools and methodologies to reduce the need of
biologists and/or ease the implementation of biological knowledge within the Bio-Inspired Design
process. Regarding the identification of the pool of potential biological models of inspiration,
Vandevenne et al. (2011), Vattam et al. (2011), Vincent (2014), Nagel (2014), Shu and Cheong (2014)
proposed different approaches, whether they are information-processing, natural language, functional
or TRIZ based. Nevertheless, no methodology or tool tackling the selection of the right biological
model(s) has been found. Therefore, the detailed research is specifically addressing this step.
3.1 Purpose
As shown in the biomimetic process of section 2.2, a pool of biological systems which would actually
provide an answer to an initial technical issue is defined. Thus, each of these potential “solution
systems” has to be researched, and this, thanks to the assistance of a vertical biologist. For every
biological system identified the same number of vertical biologists would potentially have to be
identified, contacted, initiated to the approach and motivated. The amount of work produced is
Another element comes into consideration. In fact, among all the identified biological systems, only
one, or in general a small number of these systems, will be used as a source of knowledge during the
phase of transfer from the biological to the technological field. Regarding all the work which has been
fulfilled, only a small amount has been useful. It is moreover highly probable that this would give rise
to frustration among biologists whose systems covered by their expertise have not been selected.
To address this issue, we offer a model which tries hard to allow people, without any specific
biological knowledge, to arrange by relevancy the identified biological systems on their own. This
approach do not intend to exclude the vertical biologists from the biomimetic process, but only to
delay their intervention.
This scheduling thus allows designers to call up vertical biologists sequentially, thanks to a probability
of good match between initial problem and identified living system. Eventually, it is a substantial
decrease in terms of work and effort to apply to the completion of the biomimetic process.
3.2 Proposed Model
The model revolves around the concept of ideality. According to Altshuller (1984), every technical
system can reach its ideal state. This ideality, or degree of ideality has been described in mathematical
ℎ+ (1)
According to this equation (1), a system, to reach its ideal state, can increase its numerator (increasing
its useful function(s) (Fu)) by taking advantage of unutilized resources to provide additional useful
features) or reduce its denominator (reduce costs (Fc) by eliminating unutilized resource(s) or using
less expensive resource(s) and/or reduce harmful effect(s) (Fh)). In our model, the concept of ideality
is used as a baseline allowing us to measure the positive or negative impact of a solution given out by
a biological system related to an initial technological problem.
Several knowledge transfer strategies may be considered to identify analogies (Zlotin and Zusman,
Systems that perform function(s) similar to the function(s) of the given system.
Systems that perform function(s) capable of replacing the function(s) of the given system or its
Systems that perform function(s) opposite to those of the given system.
New idea(s) and technology that could help carry out auxiliary function(s) or add new feature(s)
to the given system (i.e., enabling technologies).
Idea(s) and concept(s) for eliminating and/or preventing drawbacks or other undesired effects
associated with the given system.
The fact that analogy should prioritize function enables the quantification of the latter, corresponding
to a theoretical estimation of the useful function(s). The useful function(s) can thus correspond or not
to the requirements (e.g.: In the Eastgate centre building case, designers were looking for a passive
cooling system; termites' mounds do provide a passive cooling system). The characterization of the
useful function(s), the benefit, leads to a better understanding of the potential effect of the analogy on
the final design outcome. It also defines a filter which ensure that considered analogies show
relevancy to the initial problem statement.
The model relies on the definition of a technological space, corresponding to the initial issues, and on
a biological space, corresponding to each system likely to offer one or many solution opportunity/ies.
A knowledge transfer can be made through three different ways:
Direct transfer: technology space and biology space are identical. Strongly unlikely to occur.
Transfer with adaptations: solution space has to be adapted to fit the problem space.
Hybridization: both spaces are adapted to fit each other's. Out of scope, related to bio-assistance.
Considering the types of adaptations required, the knowledge would be more or less easy to transfer.
The model foresees enabling the sorting of the various considered analogies thanks to the emphasis on
a benefit/efforts-risks ratio.
In order to achieve this sorting, the definitions of technological and biological space are compared
thanks to the use of resources, another concept described by TRIZ (Altshuller, 1984). A technical
system has a whole range of resources available in order to achieve its ideality. Zlotin and Zusman
(2005) consider the following types of resources for a system: substances resources, field resources,
functional resources, space resources, time resources and informational resources.
The more the biological and technical systems work while using resources which are approximately
the same, the easier the transfer from biological to technical. The quantification of the necessary
adaptations is thus able to enable the identification of the efforts to implement as well as the potential
transfer failures (e.g.: In the Eastgate centre building case, space resource differed, leading to scaling
issues). Three scenarii are conceivable:
Technological system does not provide enough resources to implement the biological principle.
Resources provided by the technological system induce too many negative side effects.
Resources provided by the biological system induce too many negative side effects.
Thus, the principle of this comparison is based on a need for modeling of both the technical system
and the biological system(s).
Modeling technical systems
Regarding the bio-inspired design, two main approaches of modeling technical systems have been
investigated. The former is based on the ontology FBS (Gero, 1990), which describes the design
process in terms of Functions, Behaviors, Structures, and Design Descriptions. The latter is based on
TRIZ, the theory of inventive problem solving. Chen and Chen (2014) have described technical
systems through the substance-field model.
Modeling biological systems
Biological systems modeling proves to be more complex. According to their nature, biological
systems are often multifunctional and their links with their environment are complex. Consequently, it
is difficult to achieve some modeling on a biological system with the right degree of abstraction. This
abstraction has to be enough in order for the modeling to be understandable, but sufficiently accurate
not to lose the relative constraints. Since, it appears as necessary to define clearly the particularities of
the biological systems.
3.3 Specificities of Biological Systems
The consideration of very specific characteristics of living systems and their nested organization are
factors which can lead to the misconception of what is a system. For example the word “system” can
refer to the interaction of the object of the abstraction with its environment, but in some cases it can
also refer to the network of processes within this object. To solve this issue, we suggest to keep the
world “system” only to refer to the object of analysis itself and use the General System Theory
(Bertalanffy, 1968) as a common ground for both biology and technology. The initial step would be to
apply this very simple abstraction scheme which any open system, biologic or not, must follow:
Like any system, all open systems necessarily have a boundary, inputs, outputs and a throughput
function, see figure 2. All systems also have a boundary that can be physical or symbolic. Along with
the entire system, its parts are seen as subsystems. Considering the different parts, the whole is seen as
a supersystem. This organization is valid for any dynamic system, regardless of the particular domain
in which the system is related to and also coincides/corresponds to the Altshuller’s (1984) system
Figure 2. General System Theory, input, output, throughput
We could abstract:
Inputs: the matter, energy and information interacting with or entering the system’s boundaries
Throughputs: the processes used within the system to convert or transform inputs from the
environment into products which are usable by either the system itself or its environment.
Outputs: The product which results from the system’s throughputs.
Biological systems are non-linear open systems, they are dissipative structures far from the
thermodynamic equilibrium. As shown by Ilia Priogine (1997), they are able to create and maintain, at
least for a certain time, their physical and physiological integrity and they do so by continuously
exchanging matter, energy and information with their environment. They are goal directed in a way
that they need to survive and at least for the organism level, reproduce (or maintain their resilience at
higher levels of organizations such as ecosystems). A very important aspect of open systems, is that
some outputs could be directly or indirectly used as new inputs (feedback loops). Feedback loops are
used to make the necessary changes in order to survive, grow or reproduce and explain emerging
properties at higher levels of organization (i.e organism compared to cells) explaining that “the sum
(system) if more, then, the addition of the properties of its parts (subsystems)”. The peculiarity of any
open systems (living or non-living) is that they interact with other systems outside of themselves. Each
level in the hierarchy of supra systems, systems and subsystems, has its own laws, which cannot be
derived from the laws of the lower level. Continuous flows of matter, energy and information cross or
interact with the boundary of the living system (cell membrane, skin, ecosystem’s border, social
structure, etc.) and are processed inside the system (throughput) into structures, processes and
networks of relationships between both. Doing so, biological systems create order (decrease entropy)
inside and export waste (export their entropy) and other products in their supra system (or
In technological systems, we usually expect the products (outputs) and the system of production
(throughputs) to be different aspects of the overall process. It is not the case for biological systems:
they are autopietic. The theory of autopoiesis was coined by Matarana and Varela (1980) and they
have created the term 'Autopoiesis' from "auto" = self, and "poeisis" = creation. Their major
contribution was to clarify that living systems consist of a network of processes of production (and
transformation and destruction) which realize and regenerate the network which produced them. Thus,
life is understood to have a dynamic, cyclic, and self-maintaining organization.
This has many implications if we need to understand biological processes and related outputs. These
interactions are not simple because they are based on complex networks of interdependencies inside
the system, and between the system and its supra system. But in order to solve specific human
problems we could consider some part of the system’s complexity as a "black box", something which
takes in input, and produces output, without us being able to explain what happens in between. In
contrast, if we could explain the system's internal processes linked to the production of a biological
artefact of interest, we might call it a "white box".
An important point would be to define if the system of interest is autopoietic by itself or the result of
another autopoietic supra system. For example a specific bone could be almost considered as a closed
system which is the outcome of the autopoietic supra system “individual”. We could decide that if the
structure and shape of the bone is our core interest, we consider that even if we are aware
that autopoietic processes are involved, they could be for a time left in the conceptual “black box” and
we will explore processes inside this black box only if our abstraction led us to do so. But we should
emphasize that this conceptual black box could hide very important processes explaining some key
characteristics of the structure of forms. For instance, during the growth process, the hierarchical
structuring of shapes and microstructures are created in a stepwise manner but using a unique process.
The construction of complex organs is often based on very similar building blocks, like collagen fibrils
in bones which have units with a few hundred nanometre thickness and can be assembled to a variety
of bones with very different functions (Currey, 2002). Such growth process is part of autopoiesis,
environment sensitive, leading to final products which can be different considering the environment
Maturana and Varela define a living system as “an organized structure”, structure being “components
and relations in a particular unity or “thing” (Matarana and Varela, 1980). They define “organization”
as “existing relations among component of a system for it to be a member of a specific class”. Another
important aspect of the Santiago theory developed by Maturana and Varela is the concept of cognition,
which is for them equivalent to “living process” or the act of “doing”. Capra (1997) suggests
redefining the definition of structure as “the physical embodiment of the system’s pattern of
organization”; and defines pattern of organisation as “configuration of relationships that determines
the system’s essential characteristics”. Life process is, for him, “activity involved in the continual
embodiment of the system’s pattern of organization » (similar to cognition as defined by Gregory
Bateson (1979)).
Both authors refer to living systems, and thus autopoietic systems. But in biomimetics, the system
analysed is, most of the time, not autopoietic but an agent of what is, or used to be, an autopoietic
supra system. Understanding how our system of interested is related to a nested hierarchy
of autopoietic sub or supra systems is key (i.e cell, organism, group, society, ecosystem).
To make the juncture between General System Theory, TRIZ terminology and autopoiesis, we
propose the following definitions:
System: organized structures inside a boundary and involved in a network of processes in order
to achieve something (goal).
Structure: components organized in space and time in a particular shape (form). If the system
is autopoietic, it is the physical embodiment of the system’s pattern of organization.
Pattern of organization: configuration of network of relationships which determines the system’s
essential characteristics inside the system, or involving other components in the system’s
Process: activity involved in the pursue of the system’s goal, inside the system itself (throughput)
or with components of its environment. If the system is autopoietic, it is the activity involved in
the continual embodiment of the system’s pattern of organization (life and cognition).
3.4 Biological systems modeling
Modeling biological systems is a rather complex task. Compared to technical systems, biological ones
can hardly be seen as parts associated to functions. During the course of evolution, living systems have
responded to a multitude of dynamic boundary conditions, which we often don’t a priori know, but
might all be important to explain the development of the structures and patterns observed. This implies
that, biological systems have been optimized under certain conditions and unknown requirements,
making it hard to understand why a living system is organized that way. Thus, a provided function
should not be the starting point of the identification and the selection of a possible analogy and
therefore of the biological abstraction.
3.4.1 Existing approaches
Several attempts to model biological systems within a BID process have been identified in literature.
The SAPPhIRE model (Chakrabarti et al., 2005) has been developed to model a system and its
environment with a high abstraction level. It prodives an holistic representation and allow designers to
identify different levels of abstraction to represent biological information (Chakrabarti et al, 2014).
Nagel (2011) modeled biological systems with their three most basic instinctual actions (i.e. protect,
reproduce and sustain) as the entry point. Vattam et al. (2011) developed a knowledge-based CAD
system called DANE (Design by Analogy to Nature Engine). The DANE approach focuses on
describing in a detailed way the internal structure and functions of a system. Based on both SAPPhIRE
and DANE, Baldussu et al. (2014) proposed the integrated SAPPhIRE-DANE model, combining both
the function and causal approaches in a single model, correlating components of a system to
The main objective of these three models is to provide a functional and/or causal models of living
systems, allowing designers to facilitate the transfer of biological knowledge to the technical domain.
This objective differs from the one of the model detailed in this article, which does not focus on the
transfer step of biological to technical but on its previous one, the selection of the right analogies.
Given this difference, these models may present issues to fit the research purpose. By providing a
causal explanation of how a system accomplishes a function, these models analyze solutions. This
specificity leads the comparison of a problem description (i.e. technical system models) with a
solution description (i.e. biological system models).
3.4.2 Modeling biological systems through Miller's Living System Theory
In order to model biological systems to allow a direct comparison with technical ones, a new model is
tackled. This model is based on the Miller's Living System Theory (1978) which is a subset of the
general systems theory, tackling the living ones. It offers a frame focusing on how living systems can
be described as matter and energy organization schemes. Miller's analysis range across 7 systemic
levels (i.e. cell, organ, organism, group, organization, society, supranational system), identifying 19
scale-invariant subsets of a living system.
Table 1. The 19 scale-invariant subsets and their application to human cardiac muscle
(Miller, 1978)
The initial purpose of the theory was to identify through a cross-level analysis systemic hypotheses
that could be tested thanks to the model (e.g how a cell could react and solve an information input
overload). However, as shown in table 1, the theory serves here, through its 19 scale-invariant subsets,
as a way to draw an abstract model of biological systems. This abstraction model tends, on the
philosophical perspective, to the Altshuller's Law of System Completeness (1984). One of the
downside of Miller's theory is the lack of measurement tools to address the modeled systems, which is
here compensated by the use of resources as defined by Zlotin and Zusman (2005).
Such model should be able to provide the following specificities:
Address the different considered biomimetics systemic levels (e.i. system, structure, pattern of
organization and process).
Be compatible with technical systems description.
Can be used by designers with limited biological knowledge.
Can integrate several functions of a biological system.
Provide a potential for scalability.
In order to assess the disclosed biological modeling viability to support the step of analogy selection,
the model should be verified by a laboratory experiment. The basic parameters of the experimentation,
which should ensure that the established guidelines are met, will be outlined in the following
4.1 Hypotheses
As shown by the established guidelines, several hypotheses can be considered. For the present article,
the following hypotheses will be addressed:
A pattern for modeling living systems irrespective of their biomimetics systemic level can be
The model is suitable for a various range of profile (ranging from high degree of biological
knowledge to limited).
The model leads to the identification of functional subsystems and thus, to the characterization of
their resources.
Meeting these requirements will ensure that this model is suitable for its bio-inspired design purposes.
4.2 Experimental setting
To investigate the mentioned hypotheses, a laboratory experiment is set with the following parameters:
Systemic levels
The experiment will tackle each considered systemic level, as mentioned in section 3.3. The examined
biological systems will be the emperor penguin for the systemic level, the Cell membrane for the
structure level, the temperate deciduous forest for the pattern of organization level, and the protein
synthesis for the process level.
The experiment will include several types of participants. The first one will be vertical biologists, one
per systemic level, who will initially define the positive control (i.e. modeling the biological system
belonging to their expertise). Vertical biologists will also define a body of text, which according to
them, contains sufficient information for other participants to model biological systems. The others
types of participants will be horizontal biologists, two per systemic levels, biology students, twelve
groups of two students, designers, two per systemic levels, and engineering students, twelve groups of
two students. For each profiles of participant, one person, or group for students, will be used to define
a negative control by modeling systems without the model.
In order, for the participants, to get aware of the different concepts and theories, an initial 1 hour
training upon TRIZ and Living System Theory will be provided. Following this training the modeling
of the considered biological system will be tested. No time limit will be set for the establishment of the
reference model by the vertical biologists while there will be a 2 hours limit for other participants. At
the end of the experiment, participants will be asked to fulfil a questionnaire in order to complete
information on difficulties met during the modeling process and obtain their perceived value of the
given model.
Evaluation of results
The results of the modeling systems will be evaluated according to their match with vertical biologists'
referential modeled systems.
The focus of the evaluation will rely both on the quality (i.e. impact of the gradient of biological
knowledge on the use of the model) and on the quantity (i.e. amount of participants able to use the
model) of the generated biological system models.
A new model focusing on facilitating the selection of the right analogies is described through the
article. By hybridizing concepts of ideality, resources and system modeling, designers should be able
to rank the identified biological systems themselves. Beyond this specific focus on the selection of
biological system(s), the model also allows designers to pre-analyze biological systems. This pre-
analysis is conducted, not towards a specific function, but with a focus on the global system. In this
respect, the described model could also be seen as a translation tool, allowing people with few or non-
biological background to establish a base of common language that should ease the contact initiation
and the collaboration with vertical biologists. Considering the established biomimetic process
mentioned in section 2, sorting the envisaged analogies could theoretically reduce by half, the
abstraction requirements. Designers could thus perform biomimetic projects with a single abstraction
and concretization step, reducing the complexity of such approach.
The initial validation of the model has to be made through the study of the biological system
modeling. Several other experiments are required to validate the whole model. Following this initial
experiment, another one should assess the criteria used to compare technical to biological analogies.
Finally a third experiment should focus on the process automation, which should enhance the model's
scalability. This automation should require the development of a specific ontology revolving around
the scale-invariant subsets.
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... Because it can be hard to understand why a biological system is organised as it is through evolution, engineering functions are not always 1 The aim of a circular economy is to align product design, as well as business models, cities and so on, to a regenerative 'ecosystem' in which leakage of resource inputs, waste, emissions, and energy are minimised [56]. Circular design methods aim to achieve this by slowing, closing, and narrowing material and energy loops, e.g., through design for maintenance, reuse, disassembly, recycling etc. a good starting point for biom* [57]. There is a need for a method to describe the developmental history of nature-inspired solutions [54], as well as a need to avoid oversimplification during the analysis of biological systems [13]. ...
... The manual instantiation of such function-based models was found to support the understanding of biological systems [30,65,98]. But the question remains whether function-based models are suited for capturing biological information, as it is not straightforward to understand why a living system is organised as it is through evolution [57]. ...
... Modelling [142], Structure-Behaviour-Function models [34], SAPPhIRE models [129] and the Living System model proposed by Fayemi et al. [57,143] -may be used side-by-side to shed light on different aspects of a biological system [30,75,98,118,144]. The process of designing and developing design understandings has been called distributed cognition [59]. ...
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Engineering inspired by biology – recently termed biom* – has led to various groundbreaking technological developments. Example areas of application include aerospace engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains at large, despite the existence of a plethora of methods and design tools. In recent years computational tools have been proposed as well, which can potentially support a systematic integration of relevant biological knowledge during biom*. However, these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all the gaps in the biom* process. This thesis investigates why existing CAB tools fail, proposes a novel approach – based on Information Extraction – and develops a proof-of-concept for a CAB tool that does enable a systematic approach to biom*. Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated sentences, 3) a novel Information Extraction approach that combines the outputs from a supervised Relation Extraction system and an existing Open Information Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis by domain-experts, and ii) also improve the access to biology information for non-experts.
... Moreover, various aspects have been identified to specifically characterize biological systems [51][52][53][54]. Those concepts (e.g., multiple systemic scales, multifunctionality, etc.) have been used to support the formalization [55,56], comparison, and selection [51] of biological systems through specific models. ...
... Moreover, various aspects have been identified to specifically characterize biological systems [51][52][53][54]. Those concepts (e.g., multiple systemic scales, multifunctionality, etc.) have been used to support the formalization [55,56], comparison, and selection [51] of biological systems through specific models. It also led to the formalization of design guidelines such as "life principles" [57,58], and can even be directly integrated within approaches using abstracted biological strategies through the concept of trade-offs [43] at the origins of the ontological approaches (Section 1.2.3) [6,36]. ...
... The design of specifications surrounding this interdisciplinary collaboration led to the characterization of two different profiles to consider. A first one having a vertical, specific and deep biological knowledge, mainly embodied by researchers in biology, and a new profile having a horizontal and broad biological knowledge specifically adapted for biomimetics, which is not currently formalized and so do not properly exist yet [51,68]. ...
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Facing current biomimetics impediments, recent studies have supported the integration within biomimetic teams of a new actor having biological knowledge and know-how. This actor is referred to as the "biomimetician" in this article. However, whereas biology is often considered a homogenous whole in the methodological literature targeting biomimetics, it actually gathers fundamentally different fields. Each of these fields is structured around specific practices, tools, and reasoning. Based on this observation, we wondered which knowledge and know-how, and so biological fields, should characterize biomimeticians. Following the design research methodology, this article thus investigates the operational integration of two biological fields, namely ecology and phylogenetics, as a starting point in the establishment of the biomimetician's biological tools and practices. After a descriptive phase identifying specific needs and potential conceptual bridges, we presented various ways of applying biological expertise during biomimetic processes in the prescriptive phase of the study. Finally, we discussed current limitations and future research axes.
... Les contributions de cette thèse comprennent également un outil d'aide à la décision permettant de faciliter le choix des outils au cours du processus biomimétique, le BiomimeTree, et un outil de modélisation des systèmes biologiques Biomimetics Analyzer of Biologically Expertized literature for Engineers (BABELE) (Fayemi et al., 2015), visant à faciliter la sélection de modèles biologiques d'intérêt par des concepteurs non formés en biologie. ...
... Dans les autres cas, c'est l'utilisation d'outils qui vient pallier l'absence des biologistes (Fayemi et al., 2015). Par ailleurs, que les équipes fassent intervenir un biologiste ou non, les processus spécifiques sont structurés autour d'outils poursuivant, avec différentes approches, les mêmes buts : accéder, extraire et transférer les données biologiques. ...
... Parmi d'autres outils d'analyse on peut également citer, l'Analyse des taches (Nolan, 1989), T-chart , les Courbes en S de la TRIZ (Kucharavy et al., 2011), BABELE (Fayemi et al., 2015), La matrice de comparaison des pinnacles (Badarnah et al., 2015), etc. ...
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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.
... 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 specific field (i.e., a vertical knowledge) identified as researchers, 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]. ...
... 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 communication 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." ...
... 4.2 Methodology. The formulation of a common framework follows a methodology inspired from the previously described publications of Hashemi Farzaneh et al. [54] and Fayemi et al. [23] and synthesized as follows: ...
Full-text available 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.
... The bioinspired design tools and databases depend on the content of databases and the designers' ability to manipulate data sources [72]. Causal models exist, and they provide a causal explanation of how a system accomplishes a function [73]. How to leverage nature's knowledge effectively? ...
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The biological knowledge capture and its representation in bioinspired design are challenging as knowledge is widely scattered, bulky and complicated due to its cross-domain nature. There is a dearth of bioinspired design firms, and the roles of their actors are ignored. Various causal and functional models developed hardly represent collective knowledge and are less useful for designers. To overcome these challenges, we have used the Zachman Framework in two ways. The Zachman Framework is primarily used to represent complex objects with much information from an architectural perspective. Firstly, by using the original Zachman Framework, we represent knowledge transfer in a bioinspired design organization. Secondly, we modify the Zachman Framework to represent the biological entities. We present a complete description, methodology, and approach for both these cases. The goal of first approach is to organize and represent captured knowledge transfer and make it readily available for stakeholders for making design decisions. The second approach as modified framework is significant as it can represent knowledge of any biological entity in its entirety as a knowledge capsule. The contribution of this paper is to propose approaches for using the Zachman Framework that provides a mechanism to ensure that the holistic bioinspired knowledge activities are able to drive the bioinspired design cycle and applicable to all bioinspired design studios. The guidance provided by the adapted Zachman Framework can help designers in deciding whether to attend or to ignore the biological entity, supporting the learning environments and validating for the knowledge addition in real-time applications.
... The selection step then relies on the comparison of those evaluations to identify the best fitting strategies. For more information on the selection criteria to consider, we refer the reader to several articles that have been describing this phase Fayemi et al. 2015;Weidner et al. 2018). ...
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Biomimetics has been a subject of increasing interest but, where it has proven its scientific relevance and innovative potential from a theoretical standpoint, it remains rarely used in practice. Facing this lack of implementation, our work aimed at asking practitioners for their help to better understand the remaining impediments preventing biomimetics’ blooming. Thus, practitioners’ feedback and experts’ opinion on risks, adequacy and weaknesses of the current biomimetic practices were gathered and structured to present an extensive descriptive phase on biomimetic processes. Key levers for improvements, such as the need for a better risk management, the need for biological expertise and the need for clear guidance during the process, were then identified. Based on these insights various methodological contributions are prescribed. Among these inputs, the duration of the various steps of the biomimetic process was estimated through industrial projects’ feedback, semantics misunderstandings were tackled, and the integration of a new transdisciplinary profile combining an expertise in both design and biology is proposed. From these improvements, a new version of the unified problem-driven biomimetic process is proposed. A final descriptive phase performed through the evaluation of the new process by professionals underlined its relevancy along with the remaining research axes. Through the integration of a new profile matching the practitioners’ current needs and the adaptation of the process to their feedback, this article aims at proposing a biomimetic process fitting the reality of biomimetic practice in order to support its implementation.
... A non-biologist may expect 'bleaching' to refer to cleaning, sterilizing, or whitening (Nagel, 2014). As a result, terminology from the engineering domain does not always provide a good starting point for the identification of relevant biological information (Fayemi et al., 2015;Kruiper et al., 2017). A more appropriate approach that allows for cross-domain search, without relying so much on domain-specific semantics, focuses on TRADE-OFF relations (Vincent, 2016;Kruiper et al., 2018). ...
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Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.
... A non-biologist may expect 'bleaching' to refer to cleaning, sterilizing, or whitening (Nagel, 2014). As a result, terminology from the engineering domain does not always provide a good starting point for the identification of relevant biological information (Fayemi et al., 2015;Kruiper et al., 2017). A more appropriate approach that allows for cross-domain search, without relying so much on domain-specific semantics, focuses on TRADE-OFF relations (Vincent, 2016;Kruiper et al., 2018). ...
Conference Paper
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Nature has inspired various groundbreaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.
Biologically inspired design entails analogical transfer of design knowledge from natural systems to technological systems. The goal of Intelligent Biologically Inspired Design (IBID) project is to help engineers build a precise understanding of a biological system described in a text document and thereby acquire biological cases needed for biologically inspired design.
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Bien que prometteuses et connaissant une évolution croissante, la mise en œuvre de la conception biomimétique et de l’approche du biomimétisme reste complexe et rencontre de nombreux freins méthodologiques et pratiques. Dans ce contexte, cette thèse de doctorat explore comment l’intégration de designers dans les équipes de conception, permet de favoriser le déploiement de la conception biomimétique. Cet axe de recherche nous a permis de définir le rôle des designers dans le cadre de projet en conception biomimétique notamment pour faciliter le transfert de connaissances et la génération de concepts inspirés du vivant. Pour favoriser leur intégration et pour structurer les apports globaux du Design pour la conception biomimétique, des préconisations méthodologiques et organisationnelles sont proposées. De plus, un ensemble de modifications sur le processus de conception biomimétique problem-driven unifié ont été formalisées afin qu’il s’adapte aux pratiques de conception et d’innovation. Les résultats de ces recherches nous permettent d’enrichir conjointement le champ scientifique et le champ industriel de la conception biomimétique. Ces travaux ouvrent des perspectives de recherche à court, moyen et long terme pour développer les recherches concernant le rôle et les impacts des designers et du Design en conception biomimétique, sur le développement du cadre méthodologique et enfin sur la bascule entre la biomimétique et le biomimétisme.
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Statistical analysis of the mechanisms and processes in biological organisms (derived from published, peer-reviewed, research papers) reveals that there are ‘design’ rules which could be used to facilitate technical design, thus producing biologically inspired design without the necessity for the designer using such a system to invoke biology or biological expertise since this has already been done when the rules were extracted. Even so, this is not a necessary and sufficient condition for good design. Four principles derived from the Russian system TRIZ (widely used in technology as an objective system for solving problems inventively) are highlighted and summarised as Local Quality; Consolidation or Merging; Dynamics; Prior Cushioning. More design rules, derived in the same way, are needed to expand the importance of information (sensu lato) and materials, two aspects that the TRIZ system currently does not deal with adequately.
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Biological systems provide insight into sustainable and adaptable design, which often leads to designs that are more elegant, efficient, and sustainable. There are, however, significant hurdles to performing bioinspired design. This chapter presents a design tool, the engineering-to-biology thesaurus, that addresses several challenges engineers may encounter when performing bioin-spired design, allowing engineers without advanced biological knowledge to leverage nature's ingenuity during engineering design. Along with the thesaurus tables, detailed information on the thesaurus model, structure, population, term placement, term placement review, and limitations is provided. Applications of the design tool are discussed. Examples are provided to demonstrate the goals and applications of the design tool followed by a review of integration with computational design tools.
Conference Paper
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Although bio-inspiration is a well-known instrument for innovation, the problem-solving process that leads to the solution has not been fully investigated yet. The purpose of this article is to understand what bio-inspiration is, by defining its relative concepts and boundaries. After the outlining of a generic biomimetic process, a direct correspondence with TRIZ tools is presented. Each phase of the proposed process has been classified according to the type of tool that is needed. For the two first class, an ideal set of features has been defined.
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This work presents a new eco-innovative biomimetic design tool by integrating the algorithm for inventive problem solving (Russian acronym: ARIZ) with biologically significant vocabularies to search for related biological cases. ARIZ includes a complete procedure for analyzing problem model and related resources, resolving conflicts and generating solutions. This tool searches for biological key words by using the Noun1-Noun2-Verb model in replace of the Su-Field model for analyzing system conflicts and finds related biological cases. Analyzing these biological cases and available resources helps designers to design eco-products based on biomimetic concepts. An example is used to demonstrate the capability of the proposed method.
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Biomimetic engineering requires reliable and relevant sources of biological information as a spring-board for technology. However, biological classifications are made by biologists for biologists. There are two main groups of biological taxonomies: generic (based on phylogeny) and morphological (based on morphology). But this arrangement of information is useless for engineering, which needs an arrangement based on a functional-morphological analysis requiring a totally different classification. We show that it is possible to create an extremely focused framework, which removes the enormous diversity of living forms, illustrated by the development of such a framework for mechanisms operating with liquids at the micro scale.
Biologically inspired design (BID) is an emergent area of research for understanding design with biological mechanisms as inspiration, and for supporting systematic BID for developing creative designs. Understanding and supporting the processes of analogical transfer, whereby potential biological material is identified and adapted to solve engineering problems, is the focus of this chapter. Two questions are asked: At what level does analogical transfer take place? How to support analogical transfer? Our empirical studies show that transfer generally takes place at four levels of abstraction: state change, organ, attribute, and part. When unaided, BID is dominated by transfer at part, attribute, and organ levels, which reduces potential for creativity. This led to development of new guidelines for supporting systematic analogical transfer, an Integrated Framework for designing to encourage transfer at each level of abstraction, and a computational tool called ‘Idea-Inspire’ to provide analogically relevant biological stimuli for inspiration at any of these levels. Comparative studies using these interventions show significant increase in the number of transferred designs when aided by these interventions and a shift in the majority of the transfer to state change and organ levels, thereby increasing the potential for greater creativity.
This is a comprehensive and accessible overview of what is known about the structure and mechanics of bone, bones, and teeth. In it, John Currey incorporates critical new concepts and findings from the two decades of research since the publication of his highly regarded The Mechanical Adaptations of Bones. Crucially, Currey shows how bone structure and bone's mechanical properties are intimately bound up with each other and how the mechanical properties of the material interact with the structure of whole bones to produce an adapted structure. For bone tissue, the book discusses stiffness, strength, viscoelasticity, fatigue, and fracture mechanics properties. For whole bones, subjects dealt with include buckling, the optimum hollowness of long bones, impact fracture, and properties of cancellous bone. The effects of mineralization on stiffness and toughness and the role of microcracking in the fracture process receive particular attention. As a zoologist, Currey views bone and bones as solutions to the design problems that vertebrates have faced during their evolution and throughout the book considers what bones have been adapted to do. He covers the full range of bones and bony tissues, as well as dentin and enamel, and uses both human and non-human examples. Copiously illustrated, engagingly written, and assuming little in the way of prior knowledge or mathematical background, Bones is both an ideal introduction to the field and also a reference sure to be frequently consulted by practicing researchers.
Many applications of Biologically-Inspired Design (BID) are well-known and research is increasingly focusing on methodologies towards systematic BID. However, currently no ideation tool exists that is able to leverage the large textual biological resources in a scalable way to propose a selection of biological strategies that are interesting for a specific design problem under focus. This paper first identifies the main bottleneck preventing the realization of such a scalable BID ideation tool by analyzing the state-of-the-art in systematic BID. It is observed that most work focuses on developing detailed models of, both biological and engineering systems, which enable support during knowledge transfer between the two domains. However, the automated instantiation of these models for a large collection of biological strategies currently remains an open question and domain experts are necessary to complete this time-consuming and expensive task. Therefore, a new approach is proposed that uses a conceptual representation of the biological domain to identify candidate biological strategies as input for the transfer phase.