Smart Materials and Manufacturing Technologies for Soft Systems

S M A R T M AT E R I A L S A N D M A N U FA C T U R I N G T E C H N O L O G I E S F O R S O F T S Y S T E M S p h.d. candidate Dario Lunni s u p e r v i s o r: Matteo Cianchetti t u t o r: Barbara Mazzolai Edoardo Sinibaldi

The BioRobotics Institue Scuola Superiore Sant’Anna

nologies for Soft Systems, © May 2020 s u p e r v i s o r: Matteo Cianchetti t u t o r: Barbara Mazzolai Edoardo Sinibaldi l o c at i o n: Pisa t i m e f r a m e: May 2020

A B S T R A C T

Soft systems are recently gaining increasing attention from the engi-neering and robotics point of view because of the potential capability to adapt to unpredicted conditions. Soft materials are already used in some industrial applications confirming the practical advantage given by the high conformability of such devices. On one side, these systems require a peculiar approach in design, with a deep focus on the choice of materials to allow the integration of new functionalities. On the other side, to integrate new materials in the systems struc-ture new manufacturing technologies are needed. In this envision, the main objective of this thesis is to create intelligent systems im-plementing smart materials through unconventional manufacturing technologies. The studied devices presented here are: a new extruder exploiting innovative additive manufacturing deposition strategy for growing robots, soft bistable structures based on hygroscopic electro-spun nanofibers and soft robotic arms implementing smart sensing used for control.

Regarding the first device, we present a new design for material extrusion as additive manufacturing technology for growing robots. The conceptual design is proposed and based on the deposition of thermoplastic material. To guide the design of the system, we first studied the thermal properties through approximated models con-sidering PLA (poly-lactic acid) as feeding material. The final shape and constituent materials are then accordingly selected. We obtained a simple design that allows miniaturization and a fast assembly of the system; and we demonstrate the feasibility of the design by test-ing the assembled system. We also show the accuracy of our thermal prediction by comparing the thermal distribution obtained from FEM simulations with experimental data, obtaining a maximal error of 8

◦_{C. Preliminary experimental growth results are encouraging}

regard-ing the potentialities of this approach that can potentially achieve 0.15 mm/s of growth speed. Our results suggest that this strategy can be explored and exploited for enabling the growth from the tip of artifi-cial systems enouncing robots’ plasticity.

The second studied device implements hygroscopic nanofibers man-ufactured through electrospinning technology. The system was in-spired by the tissue composition and structure of a plant exploiting bistability: the Dionaea muscipula. The leaves of this plant provide a remarkable example of an optimized structure that, owing to the syn-ergistic integration of bistability, material and geometrical properties, permits to overcome the performance limits of purely diffusive pro-cesses. We present a hygroscopic bistable structure (HBS) obtained by

PEO nanofibers. A hygroresponsive bilayer (HBL) is also obtained by electrospinning of PEO on an unstretched PDMS layer. We mechan-ically characterized the hygroscopic material (Young’s modulus and hygroscopic expansion) so as to predict the response time of a bend-ing HBL in response to a step humidity variation. The HBS response time (1 s) is sensibly lower than the one of purely diffusive HBL (10 s) thanks to bistability. An illustrative implementation is also presented, exploiting a HBS to trigger the curvature of a PDMS optical focusing system. The developed plant-inspired soft bistable structure could be also used for sensing (e. g., humidity), energy harvesting as well as advanced soft robotics applications.

Apart from the plant-inspired devices, we developed model-based control systems for soft arms implementing smart sensing technolo-gies. First, we integrated innovative methodologies to realize a smart sensing system. The system is based on a low-cost plastic optical fiber (POF) embedded in the body structure during the robotic arm fabrication. The POF is used as curvature sensor together with a simplified steady-state model in an Adaptive Extended Kalman Fil-ter (AEKF). Sensory feedback was obtained through acceleromeFil-ters, used as quantitative benchmark for the AEKF. The AEKF estimation turned out to be more accurate (RMS error < 5◦) than the model prediction alone and the soft sensor alone, thus supporting the pro-posed fully soft proprioception strategy. Second, to close the control loop, we developed and tested in simulation a control system for a variable section soft arm. The main goal of this control system was to obtain a target curvature of the arm combining input shaping and feedback integral control in order to overcome modeling errors and constant disturbances.

A C K N O W L E D G E M E N T S

I would like to thank Dr. Matteo Cianchetti and Dr. Barbara Maz-zolai for the opportunity they gave me to be part of the PhD program in BioRobotics and permitting me to work in the laboratories of The BioRobotics Institute and the Centre for Micro-BioRobotics of the Ital-ian Istitute of Technology in Pontedera.

I also want to express special thanks to my Tutor Edoardo Sini-baldi for the help he provided me regarding scientific questions but also for the personal support during the difficult moments faced dur-ing these three years. Edoardo is a good engineer, a mathematician, a writer and a friend.

I want also to thank the people from the laboratory that helped me with technical questions. In particular, Carlo Filippeschi for support-ing me dursupport-ing my activities in the clear room and the funny moments shared together.

Thanks to the collegues from the IIT group for the nice time spent together: Francesca, Alessio, Emanuela, Ali, Anand, Francesco, Gio-vanna, Isabella, Vincenzo, Fabian, Andrea degli’Innocenti.

I want to thank also the other PhD students I met in these three years. Gabriella and Goffredo thanks for being so sincere and trans-parent. The amazing Indian guys: Riddhi Das and Saravana (the King) thanks for the unforgettable journey. Afroditi for the interesting and stimulating chats. Benedetta for your kindness and patience. Tom-maso for your acuity and lucidity, and Leonard for the very nice time shared together.

I want to express my deep gratitude to my family that always sup-ported and assisted me during these years. I hope I can make you proud of what I achieved.

C O N T E N T S

1 i n t r o d u c t i o n 1

1.1 Robotics . . . 1

1.2 Bio-inspiration . . . 3

1.3 Soft Robotics . . . 7

1.4 Smart Materials and Manufacturing Technologies . . . 9

1.4.1 Manufacturing Technologies . . . 9

1.4.2 Embedded smart sensing materials . . . 11

1.4.3 Soft integrated actuators . . . 13

2 i n v e s t i g at i o n o f t i p e x t r u s i o n 19 2.1 Introduction . . . 19

2.2 Materials and Methods . . . 21

2.2.1 Conceptual Design of the Tip Extruder . . . 21

2.2.2 Design and Prototyping . . . 23

2.2.3 Thermal Behavior Validation . . . 24

2.3 Results . . . 26

2.4 Discussion . . . 26

2.5 Conclusion . . . 28

3 p l a n t-inspired soft bistable structures 31 3.1 Introduction . . . 31

3.2 Biological Inspiration . . . 32

3.3 Materials and Methods . . . 33

3.3.1 Biological Investigation . . . 33

3.3.2 PDMS substrates fabrication and characterization 35 3.3.3 Electrospinning NanoFiber deposition . . . 37

3.3.4 NF membrane morphology and mechanical char-acterization . . . 38

3.3.5 HBL and HBS dynamic response characterization 40 3.4 Results and Discussion . . . 41

3.5 Complementary Developments related to Manufacturing 47 4 c o n t r o l o f s o f t s y s t e m s 49 4.1 Introduction . . . 49

4.2 Shape estimation based on Kalman filtering . . . 51

4.2.1 Theoretical Background . . . 51

4.2.2 Arm Design, Fabrication and Actuation . . . 53

4.2.3 Arm Sensing and Experimental Setup . . . 53

4.2.4 Model-based shape reconstruction and Kalman Filter . . . 54

4.2.5 Results . . . 57

4.2.6 Conclusion . . . 61

4.3 Closed Loop Shape Control for Bio-inspired Soft Arms 62 4.3.1 Model . . . 62

4.3.2 Control Architecture . . . 63

4.3.3 Results and Discussion . . . 64

4.3.4 Conclusion . . . 65

5 c o n c l u s i o n s 67

5.1 Future perspectives . . . 68

a a p p e n d i x 69

a.1 Model for Bistable Structure (BS) assembly through pre-strained layers . . . 69

a.2 NanoFibers mechanical characterization . . . 69

a.3 Mechano-Diffusive model . . . 70

L I S T O F F I G U R E S

Figure 1 Robotics and Human-Robot interactions . . . . 1

Figure 2 Velcro closure . . . 3

Figure 3 Bioinspired robotic platforms . . . 4

Figure 4 Examples of 3D smart micro/nano structured 5 Figure 5 Artificial Neuron . . . 6

Figure 6 Examples of Soft Robots . . . 8

Figure 7 Soft Sensors . . . 12

Figure 8 Smart materials for actuation . . . 14

Figure 9 Water responsive materials and devices . . . . 17

Figure 10 Biological and artificial robotic systems . . . . 21

Figure 11 Expected thermal distribution and conceptual design . . . 22

Figure 12 Design of the system . . . 25

Figure 13 Thermal simulation . . . 27

Figure 14 Prototype design . . . 27

Figure 15 Dionaea muscipula: biological inspiration . . . . 34

Figure 16 Fabrication process . . . 36

Figure 17 Spin-coating characteristic curve . . . 37

Figure 18 Bistables fabrication process . . . 38

Figure 19 Detailed Electrospinning deposition . . . 39

Figure 20 Setup for elastic properties measurement . . . 39

Figure 21 Setup for curvature measurement . . . 40

Figure 22 Bistability in natural and artificial systems . . 41

Figure 23 PEO-NF membrane morphology . . . 42

Figure 24 Characterization of the PEO-NF membrane . . 43

Figure 25 Indentation test . . . 43

Figure 26 Mechanical and diffusion model . . . 44

Figure 27 Artificial bistable system . . . 45

Figure 28 Optical device . . . 46

Figure 29 Soft arm embedding smart sensing . . . 54

Figure 30 Schematic of the optoelectronic system . . . . 55

Figure 31 Schematic of the steady-state model . . . 57

Figure 32 Curvature of the arm as a function of the pho-totransistor voltage . . . 58

Figure 33 Steady-state and quasi-static test comparison . 58 Figure 34 Estimation of the soft arm orientation . . . 59

Figure 35 Absolute RMS error of estimation . . . 60

Figure 36 Averaged relative percentual error . . . 60

Figure 37 Control system schematic . . . 63

Figure 38 Error dynamics comparison using different con-trollers . . . 65

1

I N T R O D U C T I O N

1.1 r o b o t i c s

The field of mechatronics emerged in the first half of the 20th cen-tury with the aim to create autonomous systems able to sense and, accordingly, react to environmental stimuli. The rise of mechatronics enabled the creation of robotics intelligent systems able to exploit the synergistic integration of mechanics, electronics and digital technolo-gies. The push of scientific research in the field of robotics opened advancements in sensors, actuators and control systems that allowed incredible improvements in many fields. Space, aviation, industrial manufacturing, surgical robotics, autonomous vehicles and virtual re-ality are the fields that more received the influences of these techno-logical advancement nowadays.

Performance of robotics systems can by far exceed capabilities of bi-ological systems. In industrial manufacturing robots optimize com-plex accurate manipulation and fast transportation tasks (Figure 1A). Automatic procedures are also used, for example, when unfavorable weather conditions make aircraft landing difficult, optimizing the ex-ploitation of sensorized landing strips.

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Figure 1: (A) Industrial robot used in automotive factory, (B-C) Rigid robotic platforms used to interact with the environment. (D) Collaborative robotics in a factory, worker and automatic system are in close con-tact, (E) iCub research platform testing human-robot interaction measuring EEG signal and eye tracking [2].

Both the previous cited conditions have in common a strictly moni-tored environment, but nowadays automatic robotic systems are mov-ing outside controlled and structured environments and are startmov-ing

to move their first steps in the real world (Figure 1 B-C). The shift of automation from the factory to the real world is accompanied by growth and deepening of the relationship between humans and ma-chines. First indication of this emerging relationship was the birth of the so-called collaborative robotics, namely the branch of industrial robotics that aims to integrate the figure of the robot into the team of skilled workers (Figure 1D). Other indications of this transformation are given by the birth of virtual domestic assistants and the develop-ment of innovative human-robot interactions that exploit, for exam-ple, the measurements of physiological signals as electroencephalog-raphy (EEG) or eye tracking (Figure 1 E). Regarding the assistance task, also the field of autonomous vehicles deserves an important mention, which has undergone an impressive development thanks to the improvement of the integrated artificial intelligence systems.

Most of the robotics research focused on advanced algorithms for sensing, control, decision making and pattern recognition, pushing the borders of artificial intelligence. In this perspective classical rigid mechanical and electronics systems are perfectly capable of satisfy the requirements for the prototyping and development of new intel-ligent robots, but still issues remains that are strictly linked to the mechanical and structural properties of these systems.

First of all, moving outside controlled environments intelligent au-tonomous systems have to improve their flexibility and adaptability to perform tasks for which they were not designed for. Increasing the degrees of freedom (DoF) of robotics system is a way to improve flexibility, but at the same time also complexity increases. Another aspect that has to be taken into account is related to the adaptability and safety of these systems when interacting with objects and bodies [3]. Approaches like bio-inspiration and soft robotics have been

pro-posed as alternatives to rigid robotics, in fact soft systems can face adaptability issues and satisfy intrinsic safety requirements thanks to their intrinsic structure.

1.2 bio-inspiration 3

1.2 b i o-inspiration

The building of intelligent systems able to cope with unstructured environment also actively adapting to unexpected changes cannot ig-nore the study of biological systems. Natural organisms proof that it is possible to create machines that are not just highly performing in very specialized tasks, but are also able to cope with changes in the environment in a customized way.

Figure 2: Velcro closure: a hook-and-loop fastener invented taking inspira-tion from the hooks of the seed of burdock.

In this perspective nature inspires engineers with creative solu-tions developing materials, objects and processes that work at differ-ent scales, and with differdiffer-ent strategies. Natural solutions integrate hierarchical structures with dimensions of features that span form nanoscale to macroscale and characterized by different properties. These solutions were optimized through evolution over millions of years, and results from the complex interplay of geometry, physical and chemical properties of the biological substrates.

Studying these natural examples as model to develop artificial so-lutions pave the way to bio-inspiration and bio-mimetics. These ap-proaches to problem solving are highly interdisciplinary, involving the studies of natural structures and the development of engineer-ing solutions. They require the understandengineer-ing of biological principles, functions and structures and the building of materials, structures and objects exploiting the same principles of natural counterpart. Actually, bio-inspiration demonstrated to be a good approach for artists and engineers also in the past. During the XX century the architect An-tonì Gaudì built up structures and windows being inspired by light reflections in water and architectures of tree branches, but also in aeronautics we can find examples of bio-inspiration in airplane wing tips that, mimicking structures of eagles wings, minimize turbulence increasing flight efficiency.

Also everyday we use solutions that were inspired by natural strate-gies. The Velcro closure (Figure 2) is a hook-and-loop fastener that was invented by the engineer George de Mestral in 1941, after the ob-servation of the hooks of the seed of burdock attached to his jacket.

Nowadays, robotics field is strongly influenced by nature that pro-vides engineers optimized examples of integrated intelligent systems. The attempt to imitate animals and insects inspired flying, jump-ing and swimmjump-ing robotics structures, but bio-inspiration in robotics looked also to plant kingdom, leading to the design of growing robots (able to adapt and change their flexible structures) and innovative ac-tuation technologies (Figure 3). In the field of nanostructures many

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Figure 3: Bioinspired robotic platforms. (A) Flying robotic insect [4], (B-C)

Jumping robots [5,6], (D) Growing robots exploiting compressed

air [7], (E) Soft robotics fish [8], (F) Tendril-like structure

expoit-ing osmotic actuation [9], (G) Growing robots based on additive

manufacturing [10]

are the examples of 3D printed smart structures that are taken as examples, and imitated, to reproduce materials with specific prop-erties. For example, since the end of the XX century, the so called lotus effect has been used as source of inspiration to develop super hydrophobic surfaces for self cleaning and drag reduction. Another interesting example used as model for controlling water contact angle with the surface was Salvinia molesta, whose microstructured surface was reproduced by the use of nanoscale 3D printing (Figure 4 A-C). For similar purpose inspiration came also from the shark skin, used as replication model for swimsuit and wings, to achieve drag reduc-tion and increasing performance (Figure 4D-F). Another example of

1.2 bio-inspiration 5

bio-inspired 3D structure is given by Morpho butterflies wings. These integrated nanostructures, acting as photonic crystals, allow the re-flection of a small portion of the visible spectrum, giving the char-acteristic bright blue to the butterfly (Figure 4 G-I). Nanostructures interacting with light are present also on corneas of nocturnal insects and gave inspiration for the creation of anti-reflecting coatings. Re-garding the surface properties of the materials, another interesting and very famous example of bio-inspired 3D microstructure is given by the feet of gecko (Figure 4J-L). The surface of this biological sys-tem exploits many microscopic hairs that increase the Van der Waals forces between its feet and the surface in contact, allowing to increase the shear supported load. Since the discovery of its properties, re-searchers attempted to simulate the gecko’s adhesiveness, opening the way to the concept of hierarchical bio-inspired dry adhesives.

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G H I

J K L

Figure 4: Examples of 3D smart micro/nano structured. (A-B-C) Salvinia Mo-lesta taken as model to create super-hydrophobic surface coating [11]. (D-E-F) Morpho butterfly nanostructures interact with light

allowing the reflection of a small portion of the visible spectrum, corresponding to the blue color [12]. (G-H-I) Shark skin

microstruc-ture is taken as example for the creation of surfaces with high drag coefficient reduction [13]. (J-K-L) Gecko feet nanostructure is

repro-duced to create bio-inspired dry adhesives [14].

Not only the field of micro and nano-structures took inspiration from nature, but also deep and more abstract architectures were taken as model to build artificial systems. For example, since the half of the past century the field of artificial intelligence and neural networks in information technology have been inspired by neurophysiology to build computational architectures able to mimic human brain (

Fig-ure 5). The first studies by Pitts [15] about neurons in architectural

net-works took inspiration from fundamentals of neural activities, then developed by Hebb and Von Neumann [16] in the fist complex

mod-els inspired by the brain structure.

Dendrites Myelinated axon Axon terminal Cell body Inputs Outputs Inputs Outputs

Figure 5: The working principle of a neuron has been virtually modeled and reproduced. The artificial neuron receives one or more inputs (rep-resenting excitatory potentials and inhibitory potentials at neural dendrites), weights separately and sums them to produce an out-put (representing a neuron’s action potential which is transmitted along its axon). The sum is passed through a non-linear function known as an activation function or transfer function.

First studies about AI (Artificial Intelligence) lead to the rise of bio-inspired computing, namely the use of computers to model and simulate the brain to create intelligence.

1.3 soft robotics 7

1.3 s o f t r o b o t i c s

As anticipated inSection 1.2, nature has been taken as source of inspi-ration also for robotics. In particular, animals and plants were taken as examples to build flexible and adaptable systems. Animals exploits soft structures to move effectively in complex natural environments, plants optimize materials to cope with continuously changing sur-rounding environment, this inspired engineers to endow robots with new bio-inspired soft materials and control strategies. The integration of soft and functional materials in robotics system help mechanical and control design of systems as a whole.

The rise of this new approach in robot design, with a focus on the mechanical properties of the structural materials, took the name of "Soft Robotics" [17, 18]. Soft Robotics is a change of the focal point

during the design phase of intelligent systems, in which materials, that were not conventionally used like silicones, polymers and smart materials are integrated in the robot body. The aim of this integration is the simplification of complex systems, moving the focus from the single component of the robot, to the design of an intelligent system as a whole, with special attention on the material side. The use of soft materials gives to robotics systems new capabilities in terms of ma-nipulability enabling the design of tendon driven soft arm, pneumatic soft gripper and universal grippers (Figure 6A-C).

Also mobility in confined space has improved thanks to reconfig-urability and adaptability (Figure 6 D), till to the development of systems able to add material to themselves, giving rise to the so-called growing robots field [7, 10]. These new abilities acquired by

autonomous systems show how unconventional manufacturing tech-nologies are strictly connected with the use and development of new robotics systems. Reconfigurable and growing robots are very inter-esting because promising for the development of adaptive structures with potential applications in telecommunication and aerospace.

Another interesting aspect of soft systems is resistance to impact and intrinsic dissipation of the material. Thanks to the distribution of loads over larger areas obtained by high deformations the maxi-mum stress perceived by the material is reduced, and the viscoelastic properties of the material allow the reduction and damping of vibra-tions allowing the design of intrinsically safe robots (Figure 6E) and innovative system for damping (Figure 6F) [3,19].

Finally, using soft materials design complexity is reduced when me-chanical compliance is required. This is particularly important when humans and robots share the same environment and interact. Soft materials are more compatible with the mechanics of human body, so elastomers and textile (also containing smart materials) can be worn without discomfort or interference with natural locomotion (Figure 6

as gloves shown inFigure 6I and for assistive robots, allowing these systems to be intrinsically safe.

The focus of soft robotics on the material properties for the design of the systems pushes robotics engineers to use not only just soft ma-terials for their elastic properties, but also smart mama-terials to integrate sensors and actuators directly in the robot body. This change of the pivotal role on the material side allows the "closing of the loop" of automatic robotic systems directly at the material level, simplifying the overall design and implementation of intelligent control systems.

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Figure 6: (A) Bio-inspired soft octopus arm [18]. (B) Soft gripper [23]. (C)

Universal gripper exploiting granular jamming stiffness transition [24]. (D) Soft robot exploiting reconfigurability for locomotion and

shape changing [25]. (E) Intrinsically safe Soft robotic arm used for

manipulation tasks [26]. (F) Jumping system exploiting optimized

damping [19]. (G) Soft-wearable exosuit for force enhancement

(adapted from [20]), (H) Smart textiles (adapted from [21]), (I) Soft

pneumatic wearable glove used for handling assistance (adapted from [22]).

1.4 smart materials and manufacturing technologies 9

1.4 s m a r t m at e r i a l s a n d m a n u f a c t u r i n g t e c h n o l o g i e s f o r s o f t s y s t e m s

Intelligence is traditionally related to brain and nervous systems. But a broader definition of intelligence is the capability of a system to perceive external stimuli, elaborate these signals and react accord-ingly to the environment. Many animals are considered intelligent for their abilities to elaborate, analyze, communicate and learn from the experience. Some plants are also considered intelligent (even if without nervous system) because of their ability to detect changes in the environment and adapt in response to these stimuli [27].

Intel-ligence has already been demonstrated by artificial devices created from smart basic electronics like logic gates and transistors. Artificial intelligence systems are nowadays able to achieve super human per-formances, for example in playing games (i.e. AlphaGo, DeepMind) [28], or at least to reach equal human performance level (i.e. Google

Image Recognition) [29]. Of course AI systems have several issues in

terms of performance and energy consumption, deep learning neural networks can be easily fooled, for example, giving high confidence predictions for unrecognizable images and require a large amount of energy to complete the learning process [30, 31]. By the way, the

us-age of artificial intelligence can improve the usability of autonomous untethered systems, assisting during a determined task, or optimize control strategies. For these reasons, more and more "intelligence" (in the broad sense) started to be integrated in robotics systems not just designing an electronic "brain", but also assembling their bodies us-ing the so-called "smart materials". The smartness of a material can be created using stimuli-responsive molecules, namely materials whose molecules react when particular environmental conditions manifest. In particular, the aim of this approach is to allow the designed sys-tem to react in a controlled way to achieve a determined task. These smart materials are usually embedded in the body structure, so re-quiring versatile manufacturing technologies to be integrated during the assembly and building of such systems.

1.4.1 Manufacturing Technologies

Soft robots integrating smart materials are often fabricated using tech-niques such as casting or molding, but in these cases some limita-tions are faced during the realization of complex shapes, especially using multiple materials [32]. Recently, huge advancements have been

achieved by the development of the additive manufacturing (AM) [33]. AM is undergoing a huge expansion thanks to the birth of new

techniques of material deposition. A widespread AM technique is the Fused Deposition Modeling (FDM), that is a versatile and affordable 3D printing technology in which a heated extruder nozzle melts a

fila-ment and deposit it layer-by-layer until the printed part is completed [34]. The miniaturization of 3D printing technologies allows the

depo-sition of materials in a controlled way opening the possibility to use it not only for material depositing, as in the traditional 3D printing systems, but also to push the printing head trough the environment. In this perspective FDM allows these systems to print themselves, re-pair and in a certain sense "to grow" [10]. Another very interesting

AM technique is Direct Ink Writing (DIW), in which a viscoelastic ink of a polymeric precursor is pushed trough a nozzle for selective deposition and is polymerized during a post-printing treatment. This technique allows for example to print hydrogels for sensors, actua-tors and for the so-called 4D printing, in which 3D printed structures can react to chemical concentration variation [35, 36]. Also sintering

technique has been developed to produce polymeric systems, in par-ticular Selective Laser Sintering (SLS). A layer of powdered material is locally fused by a laser head following a predefined trajectory, the material fuses together and after the thermal treatment it solidifies as a unique piece [33]. When one layer is completed, another layer

of material is uniformly deposited over the first one and the pro-cess restart. SLS techniques allow to fabricate porous structure also with complex design thanks to the powdered material used as pas-sive physical support. An example of SLS application is a 3D-printed soft robotic hands have been produced integrating actuators, sensors and structural component [37]. Laser technologies is used also to

polymerize materials starting from a liquid resin, this technique is named Stereolithography (SLA) and comprehends two-photon poly-merization and micro-stereolithography [38]. Essentially the light is

focused to photopolymerize a solid object, layer-by-layer from a liq-uid polymeric precursor. The use of this technique is particularly in-teresting for micro-scale applications, thanks to the high resolution of such technology and allows, for example the synthesis of nanostruc-tured materials with surface properties as presented in Section 1.2

[11]. Another technology that do not belong to the traditional

addi-tive manufacturing technologies but recently found application for the assembly of soft sensors, actuators and also scaffolds, is electro-spinning [39]. This technique is again based on the use of a polymeric

precursor that is charged by a high voltage generator and, thanks to the strong electric field generated, is attracted towards a grounded collector. While accelerated toward the collector the solvent in the precursor evaporates and a thin polymeric fiber remains attached to the metallic collector. The prolonged deposition of fibers results in the assembly of polymeric membranes. Different techniques are used to deposit aligned or patterned fibers, and even accurate control over the fibers deposition can be achieved through near-field electrospin-ning [40]. The combination of manufacturing techniques allows the

1.4 smart materials and manufacturing technologies 11

integration in the body structure of different smart materials for sens-ing and actuation.

1.4.2 Embedded smart sensing materials

The use of smart material allows the integration of sensors in the structure of the systems, simplifying the overall design and avoid-ing the necessity of the assembly of a sensors network. Examples of material integrated sensors exploit piezoelectric, piezoresistive and resistive effect. Piezoelectric materials can be integrated directly in the mechanical structure as composite to detect stress and strain. In particular, low stiffness polymeric piezoelectric materials (PVD-F/TrFe) can be integrated in soft robot body or as wearable devices [41, 42]. Resistive sensors measure the resistance variation caused

by changes in geometry or resistivity. Conductive liquids embedded in elastomeric structures or stretchable polymers can be exploited to produce stretchable sensors. Examples of conductive liquids are low-melting point metal and alloys (eutectic indium, gallium-indium-tin alloy) and ionic liquids [43,44]. Drawbacks of liquid

met-als include the complexity of fabricating microchannels and the risk of leakage, moreover usability is limited to certain temperature and their density is much higher than the elastomeric matrix [45]. Ionic

liquid are cheap and light, but are characterized by low conductiv-ity respect to liquid metals and encounter large drift when subject to temperature changes [46,47]. Piezoresistivity is the change of

electri-cal resistance of a material due to structural deformations. Piezore-sistivity of metals and metal alloy can increase a few times due to deformation while semiconductors materials such as silicon and ger-manium can enhance the resistance change of several orders of mag-nitude thanks to the change of the bandgap on inter-atomic spacing. Also nanoscale materials as carbon nanotubes (CNT) or zinc oxide nanowire (ZnONW) show high piezoresitivity [48, 49]. The main

is-sue of the usage of semiconductors and nanostructured materials is related to the limited stretchability of semiconductors and metals. For this reason piezoresistive sensors for wearable and soft robots are based on elastomeric matrix filled with conductive materials. These composite materials are usually very easy to manufacture, and have tunable mechanical and electrical properties, but are characterized by hysteresis, non linearity and slow responses [50]. There is usually a

trade-off between the electrical properties and the mechanical prop-erties of these composite because increasing the quantity of filler im-proves the electrical behavior of the material but at the same time it affects stretchability.

Optical sensors are also based on smart sensing materials that are integrated in the robot body (Figure 7 A-E). Optical strain sensors exploit a light source (photodiode), a light sensor (photodetector or

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A

Figure 7: (A) Stretchable waveguides [51], (B) Transparent waveguide [52],

(C) Soft innervated waveguide [51], (D) Foam and optical fibers

assembly [53], (E) Optical sensorized surface [54], (F) FBG-based

shape sensing [55].

spectrophotometer) to detect light variations and a light transmission medium (e.g. optical fiber, elastic waveguide) [52, 56]. The strain

ap-plied to the transmission light medium, caused by bending or com-pression of the optic fiber, generates mode field distortions that will contribute to a reduction of the total internal reflected light rays. For commercial step-index plastic optical fibers (SI-POF) bending losses can be considered negligible for small curvature, but they become the main attenuation contribution when the bending radius is smaller than a certain critical radius [51]. These sensors are cheap and easy to

integrate, but accurate measurements are hard to achieve especially because these systems are not able to discriminate the position along the fiber of the strain and the kind of deformation. More complex and expensive optical sensors comprehend fiber Bragg gratings (FBG) that can be integrated in composite structures to detect stress and strain (Figure 7 F), temperature variation or vibration [53, 57]. When the

structure deforms, the integrated FBG follows the same deformations causing a variation in the wavelength light reflected from the grating. Multiple FBGs can be fabricated on different longitudinal positions of the fiber, so different strain distribution along the fiber can be mon-itored with electronics located at the end of the fiber [54, 58]. FBGs

show great potentials to develop high performance soft sensors with-out any electronics located at the sensing site. Main drawbacks of this

1.4 smart materials and manufacturing technologies 13

technology are the expensive manufacturing process, limited stretch-ability and complex signal conditioning electronics.

1.4.3 Soft integrated actuators

Apart from the use of smart materials as integrated sensors, advanced stimuli responsive materials can be used as integrated actuators in soft body structures. The properties of such materials allow these systems to show smart behaviour in response to environmental con-ditions or to be activated using non-conventional control systems like lasers or chemical compound. Another aspect that has to be taken into account during the assembly is the manufacturing process used to integrate such materials in the systems, in fact new technologies have been developed to deposit and process such materials during the manufacturing process. Soft integrated actuators can exploit dif-ferent activation mechanisms and are here grouped in thermally, mag-netically, electrically, photo-responsive and chemically sensitive mate-rials.

t h e r m a l ly r e s p o n s i v e m at e r i a l s Thermally responsive ma-terials can be activated by infrared (IR) or near-infrared (NIR) light, thermal radiation or Joule effect. The use of lasers allow these mate-rials to be activated remotely also exploiting selective heating. When exploiting Joule effect the use of electro-thermal stimuli allows the materials to be easily integrated with classical electronic control sys-tems (Figure 8 A). Thermal actuators based on material expansion can be fabricated integrating light-absorbent and conductive carbon nanoparticles, Carbon Nanotubes (CNTs), or graphene in soft materi-als. CNTs are used while been dispersed in a matrix or adding them as a thin film, allowing thermal activation while maintaining the ma-terial flexibility [59,60]. Actuators based on thermally responsive

ma-terials are usually less efficient and slower than mama-terials based on others principles.

Other thermal responsive materials exploit Liquid-Crystals (LC) molecules, also called mesogens, which are typically polar rod-like molecules. At low temperature mesogens are aligned, but when LCPs are heated the initial ordered mesogens alignment transforms in an amorphous phase causing a large strain in the direction of align-ments as shown in Figure 8 B [61]. The mesogens alignment can be

achieved applying mechanical stretching, magnetic or electrical fields followed by the crosslinking of the polymeric material [62]. Also

ther-mal Shape-memory materials are exploited for actuation, typically a shape can be fixed under certain condition, and then recovered by temperatuchanging. The recovery of the "memorized" shape re-sults in a stiffness variation that spans several orders of magnitude. Shape-Memory polymers (SMPs) can be easily deformed at

temper-ature above the glass transition point, maintaining the strain when cooled down. Increasing the temperature again will make the poly-mer recover its original shape [63]. Heat can be generated through

Joule effect after making the material conductive, for example adding conductive fillers (e.g. CNTs, polypirrole). The shape memory effect is widely used also in metal based materials, in particular nickel titanium (NiTi) Shape Memory Alloy (SMA) is commonly used in robotics and soft robotics. The working principle is the same as for SMPs and is based on thermal cycle: when certain temperature is reached they recover a predetermined shape generating mechanical work [64]. Due to their limited weight and compactness SMA are

often used as mini-actuators [65] or in those application in which

weight is an issue (for example in morphing wings for aircraft) [66].

Principal drawbacks of this technology are: energy efficiency (∼10%), strong non-linearities and fatigue resistance.

A

B C

D

E

Figure 8: (A) Thermally activated origami robot [67], (B) Thermally

respon-sive LCs [62], (C) Actuation mechanism of a magnetic-driven

sys-tem incorporating micromagnets (white circles) [68], (D) DEA

op-erating principle [69], (E) Biomimetic Ionic Eletroactive Polymer

(IEAP) robot [70],

m a g n e t i c a l ly r e s p o n s i v e m at e r i a l s Another broadly de-veloped class of shape changing materials exploit response to mag-netic stimuli. Magmag-netically responsive materials usually consists of magnetic particles or discrete magnets filled in a compound with a defined profile of magnetization. The integration can be achieved through the casting of elastomeric material in a mold containing

mag-1.4 smart materials and manufacturing technologies 15

netic sensitive particles. The combination of cutting technique and the application of strong magnetization field during the manufacturing process allows the control over the intensity and direction of the mag-netization profile. Such profile allows the control of the system using an external magnetic field [68, 71], because of the tendency of these

particles to align with the field lines. This approach is particularly suitable in building up micro-scale robots used in small, enclosed environment (Figure 8 C), having the magnetic field the possibility to penetrate through a wide range of materials [72, 73] and having

very fast response time compared to other stimuli-responsive materi-als [74].

e l e c t r i c a l ly r e s p o n s i v e m at e r i a l s The easiest way to inte-grate smart materials with classical electronic system is to use electri-cally responsive materials. Driving actuators by electric signals allow accurate control and modulation of actuation. Electrically responsive materials include: non-ionic Electro-Active Polymers (EAPs) such as dielectric elastomers, ferroelectric polymers, electrostrictive polymers and Liquid Crystal Elastomers (LCE), on the other hand there are ionic EAPs, subject to movements of ions and their conjugated sub-stances, grouped in ionic conducting polymers and ionic polymer-metal composite (IPMCs). Regarding the first class, one of the most important group is composed by the Dielectric Elastomers Actua-tors (DEAs). DEAs working principle is based on the exploiting the Coulomb force attracting two flexible electrodes on either side of a compressible dielectric membrane as shown in Figure 8 D [75, 76].

The second class comprehends ionic EAPs. Usually polymers are good insulator, but when subject to a process called "doping", these materials can exhibit good conductivity. The "doping" process allows to obtain a "conjugated" polymer that alternate single and double bonds along the polymer chain, allowing the sharing of electrons among different atoms. The movement of electrons in the material can be influenced by an external electric field, allowing to control the oxidation state of the polymer [77]. Conductive polymers can exhibit

large dimensional change dependent on oxidation state of the mate-rial as shown in Figure 8 E. Many actuation systems have been built up exploiting this intrinsic electrical to mechanical work conversion [70, 78]. Ionic Polymer-Metal Composites (IPMC) actuators consists

of an ion-conductive polyelectrolyte membrane endowed with flexi-ble electrodes on both side. In the membrane the positive ions are free to move, while the negative ones are bonded with carbon chains of the polymer. When an electric field is applied the positive charges accumulates near the electrode, exerting stress on the surrounding molecules due to the high concentration [79].

p h o t o r e s p o n s i v e p o ly m e r s Photoresponsive Polymers are of great interest for soft robotics and drug delivery applications because they can be accurately controlled remotely, also at the micro and nanoscale. In these materials the so-called photochromic molecules, namely molecules that capture the light stimuli and respond with change of physical or chemical properties, are fundamental. Photore-sponsive polymers can respond with strain, shape changing, wettabil-ity variation, transition and phase-separation temperatures changes upon radiation. These reactions are reversible changing the wave-length of the light source or removing the source. The photosensi-tivity of these materials if given though fillers of photosensitive func-tional group such as: photoisomerizable molecules like azobenzenes (tipically found in LCEs) [80], molecules that under go ionic

dissoci-ation [81], and photoreactive molecules such as cinnamates that are

usually found in SMPs [82].

c h e m i c a l ly r e s p o n s i v e m at e r i a l s Chemically responsive ma-terials comprehend a wide range of mama-terials that exploits differ-ent mechanisms to achieve the change of shape. The capability to transform chemical energy in mechanical energy is called chemo-mechanical motion, and soft materials have the ability to diffuse chem-ical compounds in the network matrix allowing different chemchem-ical reactions. The chemical reactions happening in the polymer network can alter the osmotic pressure or chain affinity and eventually lead to material size and shape changes [83]. Response time of these

ma-terials are strongly affected by the diffusion rate into the mama-terials. Diffusion rate can be controlled controlling the porosity of the mate-rials or by exploiting surface treatments [84,85]. An effective strategy

to improve diffusivity in membranes is exploiting electrospinning de-position, thanks to the high surface to volume ratio of nanofibers, electrospun membrane results to be highly sensitive to environmen-tal chemicals [86].

Water responsive materials Water responsive materials are among the most interesting chemically responsive categories because of the pervasiveness and general safety of water. Water responsive mate-rials can involve the use of SMPs, LCPs, gels, papers and allowed the creation of a wide range of soft system as grippers, walkers, en-ergy harvesting systems and wearable devices [21,86,87]. Hydrogels

have been widely used as core material for humidity responsive ac-tuators because of their capability to swell upon an increase of water molecules concentration in air. For example, hydrogels have been suc-cessfully used to switch nanostructured pattern in response to humid-ity changes [88]. In addition to swelling, hydrogels can also contract

when their temperature raises above the so called lower critical so-lution temperature as depicted in Figure 9 A, starting experiencing

1.4 smart materials and manufacturing technologies 17

dehydration [89]. This properties coupled to temperature controlled

systems allow the assembly of thermally responsive hygromorphic materials. The use of thermally responsive materials allows the ac-tuation of these systems with NIR light can improve the response of actuation of these systems [90]. Otherwise, the use of conductive

material allows the integration of such system with electronic sys-tems, and exploiting Joule effect achieve temperature control as show in Figure 9 B. This approach allows the assembly of bending actu-ators and grippers [87]. Water responsive polymers have been used

A

B D

C E

F

Figure 9: (A) Oriented Hydrogel Fibers [91], (B) Electrically controllable

hy-groscopic actuator [87], (C) Mobile robot exploiting humidity

gra-dient to move [86], (D) Jumping device exploiting hydrogel [92],

(E) Smart textile [21], (F) Energy harvesting from humidity level

variation [21]

to build up locomotion soft robots, as shown in Figure 9 C, able to convert humidity variation in movement of a crawling soft system [86, 93]. Also more complex microstructures have been assembled

able to jump when touched by water drops [92], as depicted in Fig-ure 9 D. Also LCPs and SMPs based materials were exploited for water responsive systems [94, 95]. Another interesting approach to

create water sensitive materials is using humidity-responsive bacteri-als spores, examples are shown in Figure 9E-F. Using these baterials as coating materials allows the building of completely biocompatible and biodegradable systems that can achieve high strains with high actuation forces [96, 97]. These actuation methods have been used

applica-tions include wearable clothes able to reconfigure to accommodate body sweat and regulate body condition [21].

In this Ph.D. thesis, systems implementing smart materials through unconventional manufacturing technologies are presented. The de-vices take inspiration from biological studies of plants to create adapt-able soft systems. In the second chapter a new extruder exploiting in-novative additive manufacturing deposition strategy is investigated with the aim to optimize growing robots functionalities. In the third chapter plant-inspired hygroscopic devices are studied and tested, such systems take advantage of structural properties and integrated hygroscopic nanofibers produced through electrospinning. In the fourth chapter some parallel activities related to control systems exploiting embedded smart sensing for soft robotics are investigated. The ap-pendix contains details of the used mathematical modeling.

2

I N V E S T I G AT I O N O F T I P E X T R U S I O N A S A N A D D I T I V E M A N U FA C T U R I N G S T R AT E G Y F O R G R O W I N G R O B O T S

2.1 i n t r o d u c t i o n

To bring autonomous robots in our everyday life, e.g. as compan-ion, assistants, or workers, we need flexible and intelligent solutions able to cope with unpredictable changes in the environment, to adapt to different task needs, and to obtain safe human-robot interactions. This way, the new applications of robotics appears to radically di-verge from the classical robotic approach, oriented to the optimiza-tion of a specific industrial process [98]. The advent of these emergent

paradigms and rules in robotics requiriere new methodologies to en-gineer continuously adaptable artificial systems. Bio-inspiration helps in rethinking robotics outside factories and in pushing towards the understanding of principles behind locomotion, adaptation or mor-phological change and aggregation of natural systems; as well as in using such natural principles as guidelines to build their artificial counterpart [99]. Nature has endowed living systems with the

abil-ity to adapt their bodies for instance to pass through small apertures with soft and squeezing bodies (e.g. worms), evolve with different shapes (e.g. metamorphosis from worm to butterfly), or adapt di-mensions and growing directions to environmental stimuli (e.g. plant roots) this suggests that a predefined and completely rigid struc-ture can limit robot functionalities in unstrucstruc-tured environments [18].

Probably the first attempt to rethink the design of robots for enounc-ing their adaptability takenounc-ing inspiration from nature was made by Fukuda with the concept of a Dynamic Reconfigurable Robotic Sys-tem [100], giving rise to the new field of cellular robotics, whose aim

is to obtain robots with a non-predefined shape. Based on this con-cept, a single robot can be composed of multiple modules, resembling simple units like cells composing a tissue, each implementing simple functionalities, letting the intelligence of the system emerge from the interaction among the modules (or cells) [101] moreover, often the

as-sembly is guided by rules extracted from organization strategies of living systems [102]. The robots developed in this field demonstrate

to be highly adaptable thanks to self-assembly and self-reconfigurable properties [102]. By the rearrangement of the modules, the same robot

modifies its shape for several purposes and to accomplish different tasks, for instance achieving different gaits of locomotion [102],

ma-nipulation [103], or reproducing several kinds of furniture [104]. In

this context, the system’s functionality is not limited by an initial de-sign; however, a limit is imposed on the possible configurations by the mechanical design and latching mechanisms. This issue was dis-cussed by Lipson and Pollack in [105], who proposed the idea of

a continuously self-designing reconfigurable robot. The authors im-plemented this concept by computationally evolving a design for the robot’s body together with its control and by printing with a commer-cial 3D-printer the components of the robot’s body, and then manu-ally assembling it. Although this approach did not allow integrating additive manufacturing technology directly as part of the robot, addi-tive manufacturing enables the fabrication of bodies otherwise poten-tially difficult to manufacture with classical techniques (e.g. molding or assembling). The potential of additive manufacturing has been ex-ploited in robotics for instance for the fabrication of the robots’ body [106] or components (e.g., soft skin [107]), to assist and enhance robot

functionalities allowing the robot to self-build its tools or grippers [108], or enabling its locomotion [109]. Specifically, in [10], taking

in-spiration by plant growth strategies, the authors implemented a root-like growing robot that directly embeds a 3D-printer based mecha-nism in its tip for the deposition of new material in the apical zone of the robot (Figure 10). Analogously, plant roots move within the soil by adding material in the meristem zone [110]: the division and

elon-gation of the cells behind the root apex allows the apex itself to pen-etrate through the soil reducing pressure [111], navigate the

environ-ment and dynamically adapt the morphology of the root apparatus [112]. In the case of [10], additive manufacturing, specifically Fused

Deposition Modeling (FDM), has enabled the robot to self-build its body while continuously pushing forward the exploratory tip, obtain-ing a tubular structure that can be used as communication channel or to sustain the robotic tip in air or anchor it in soil. Following the idea of exploiting additive manufacturing technologies to enhance robotic solutions with growing capabilities, in this chapter, we propose a sim-plified version of manufacturing approach with respect to [10] aimed

at reducing the number of components to be assembled (e.g. number of motors), while at the same time allowing the miniaturization of the system. Differently from the classical FDM, where the melted ma-terial is deposited sequentially in a layer, here, we propose a deposi-tion strategy somehow similar to the extrusion technique, preserving the employment of thermo-plastic material (specifically PLA - poly-lactic acid). The novel extruder design proposed in the present work allows the eversion of the material flux feeding the robotic tip all at once. The design phase is supported by thermal simulations extracted from a model. Thanks to the unconventional proposed geometry, the tip mimics more closely the behavior of the material flow taking place in a real natural root, in fact every single layer is plotted and cooled

2.2 materials and methods 21

all at once allowing the improvement of the theoretical speed of the robot.

Figure 10: Biological and artificial robotic systems in comparison highlight-ing similarities between characteristic zones.

2.2 m at e r i a l s a n d m e t h o d s

2.2.1 Conceptual Design of the Tip Extruder

To obtain the deposition of new material circularly all at once, a pre-cise and well-localized control of the temperature is needed. Ideally, three different thermal zones should be present in the system to man-age the transition of the material from solid, melted and solid state again. There are three main thermal regions that can be identified in the system (Figure 11 A): heating, melting and cooling zone. The thermoplastic material enters in the system through an axial channel; it passes through the heating zone, and reaches the melting tempera-ture allowing the material to change its shape in the melting region. Here, the material is radially pushed and then passes through a cool-ing region, where it cools down to reach the solid state again at the solidifying section and constitute a stable base for the next layer to be deposited. The working principle of the movement is depicted in Fig-ure 11 B-D: the push of the feeding material (FFeed) pressurizes the

material present in the melting zone, if the forces exerted by the pres-surized melted material on the solidified extruded material (FGrow)

are greater than the external forces exercised on the tip (FExt) (e.g.,

friction if moving in a medium, or gravity if moving in air), the tip will move in the same direction of the feeding material. In this per-spective and with the aim to obtain a simple assembly of the system, an accurate design of each component is fundamental and tightly connected with the distribution of the temperature within the system. To predict the behavior of the material during the heat exchange, we developed mathematical models considering PLA (poly-lactic acid) as feeding material (melting temperature ∼ 180 ◦C) and considering the heat exchange of the system with the environment at 25◦C. The

PLA has been chosen because of its cheapness, availability and well known thermal properties since widely used as feeding material in commercial 3D printers. Firstly, an analytical analysis was carried out to preliminarily understand the general requirements of the design. To verify the temperature distribution and the behavior of an approx-imate geometry of the system, thermo-fluid-dynamics FEM simula-tions were carried out using COMSOL software (v5.0), considering as structural materials Aluminum (used for its conductivity) and PTFE (Polytetrafluoroethylene) (adopted for its resistance to high temper-atures) as discussed in Section 2.2. In particular, these simulations allowed an approximate estimation of thermal source temperature, mass flow rate and power thermal consumption (Figure 11C).

Figure 11: Expected thermal distribution and conceptual design. In (A) it is depicted the conceptual design with the different desired thermal zones. In (B) the forces acting on the growing system and the heat fluxes acting on the control volumes are shown, in (C) the isothermal contours dividing the estimated thermal zones using the thermo-fluid-dynamics model are shown. While in (D) the growing principle and direction of movement is depicted.

Analytically, we modeled the heating phase of the material consid-ering an energy balance in the heating and melting zone (V1) of the

system.Assuming, the heat exchange and a mass balance with con-stant density, the energy balance can be described by:

˙

min =m˙ out=m˙ (1)

˙

Q =m(C˙ _p∆T + ∆H_m) (2)

where ˙m_in and ˙m_out are the mass flow rates respectively of the entering and exiting material from the control volume, ∆Hm (∼ 45

2.2 materials and methods 23

J/g) is the specific heat necessary for the phase change (melting), ∆T is the difference between source and room temperature, Cp is the

specific heat of the polymer averaged on the temperature interval and ˙

Qinis the heat entering in the heating and melting zone. Considering

to start from a room temperature of 25 ◦C up to an internal source temperature of 230◦C, and assuming a mass rate of 0.04 g/s for the material, it is possible to estimate the minimal power consumption needed for the melting of the material as 12 W. To design the cooling zone, which should guarantee the solidification of the material, we used Equation 1 and Equation 2 applied on the cooling zone (V2)

considering ∆Hs = −∆Hm for the phase change (solidification) and

∆T = −90◦C to account for the cooling of the material. Preserving the same mass rate ˙m, the power to dissipate is ˙Qout ∼ 9 W. Assuming to

dissipate the heat by natural convection, the thermal exchange area can be defined as:

A = Q˙out h(Tc− Tamb)

(3) where h is the value of convective exchange parameter in free air (h = 15 W/m2_{), ˙Q}

out is the dissipated power, Tc is the average

temper-ature of the cooler component and Tamb is the environment

temper-ature. In order to be able to solidify the fused material, the exchange area can be estimated as A = 6.04·10−3 _mm2_.

For this preliminary evaluation, we built an approximated axis symmetrical geometry in order to decrease the computational costs. The temperature of the source was kept at 230◦C and the speed of the feeding material at the entrance of the heater was imposed at 13 mm/s to allow obtaining the thermal distribution shown inFigure 11

C.

2.2.2 Design and Prototyping

The final design includes four principal components: base, heater, hat and cooler (Figure 12 A). We prototyped the proposed design us-ing classical fabrication techniques and assemblus-ing it with six screws (Figure 12 B). The base component is used to receive the feeding ma-terial and to support the whole assembly. It should resist to compres-sion at high temperature and it needs to have a good thermal insula-tion. For these reasons, PTFE (Polytetrafluoroethylene) was chosen as constituent material for the base. The heating system was composed of a resistance (2.5 Ω) made of NiCr alloy which was heated using Joule effect. The heating power was controlled through Pulse Width Modulation (PWM) managing the current of a 10 V power source. A temperature sensor (Negative Thermal Coefficient Thermistor) is integrated in the base to close the temperature control loop. An Ar-duinoTM MEGA 2560 was used to control the heating mechanism

and the temperature sensor value was used as feedback for the im-plemented PI temperature control. The parameters of the control sys-tem were tuned approximating the step response of the syssys-tem with a first-order dynamic system (τ = 500 s) and choosing the parameter to achieve the desired response time. To define the geometry of the base, we considered the friction between the deposited material and the structural material. In fact, by preliminary experimental proto-types, we observed the shrinking of the solidifying material, inducing the deposited material and the base to get in contact increasing the friction. To minimize this source of dissipation, we used low friction material and geometrical precautions. In particular, the external part of the base was designed with a variable section in order to avoid this increment of friction. The dimension of the sections was chosen con-sidering an approximated friction coefficient between the base com-ponent and the moving plastic material. Specifically, we defined the inclination of the lateral walls to exploit the shrinkage of the mate-rial and to obtain a sliding movement of the matemate-rial itself. To obtain this sliding, the angle of inclination of the lateral wall θ (Figure 12B) needs to be greater than the angle of the friction cone of the base:

θ > tan−1(µ) (4)

The friction coefficient µ was evaluated considering the contact of PTFE and the sliding material approximated as static friction; thus, assuming a value of µ ' 0.14, it results θ > 8 deg. A central hollow channel in the base is used to pass the raw material to the heater. The heater is built with a thermally conductive material (aluminum) and is activated to reach the PLA characteristic melting temperature (∼ 180◦C), using a resistance located between the base and the heater itself. Being pushed, the melted material moves, increases its tem-perature and reaches the melting chamber of the system, placed be-tween the heater and the hat. The hat is used as a wall, to invert the movement of the melted material and to push it through the cooling zone where the material can finally cool down and solidify. The hat needs to be thermally insulating and characterized by low friction, so again PTFE (Polytetrafluoroethylene) was selected. While the cooler is made of conductive material (aluminum) and endowed with fins increasing the thermal exchanging area.

2.2.3 Thermal Behavior Validation

We performed FEM simulations using COMSOL software to verify the temperature distribution that can be obtained with the chosen de-sign. The simulation results were compared with temperature mea-surements acquired experimentally on the prototype while heating without the passage of PLA. A pure thermal 3D model was built up to

2.2 materials and methods 25

Figure 12: Design of the system. In (A) a view of the assembled is shown; while in (B) the assembled design is presented highlighting the different thermal zones; the numbered white dots localize the position of the sensors used to validate the thermal model.

accurately simulate the presence of the conductive connectors in the assembly. The accurate geometry of the proposed design (Figure 12) was taken into account and the thermal and mechanical properties of the materials used for the fabrication were considered. The external boundary conditions were chosen considering the thermal environ-ment in which the prototype is placed. So, in the external limits of the system we considered an outgoing thermal flux by natural convec-tion of air at room temperature. Finally, the thermal source was sim-ulated as heat power source generated in the dominion occupied by the component called heater, allowing in this way a time-dependent study. On the prototype, the temperature was measured at different points: one along the external surface and three in the internal struc-ture, one for each thermal zone, as depicted in Figure 12 B. Three experiments (each lasting Texp ∼ 3500 s) have been carried out and

the temperature acquired every 0.5 s (∆t). For each sensor position, the experimental temperature, averaged over the repetitions, was cal-culated for each interval of time (Ts(t)). These values of Ts(t) were

then compared with the expected temperatures Te(t)in the system as

obtained by the model to calculate the errors:

ε =kTs(t) − Te(t)k (5)

For each sensor position, the final temperature error was averaged on the steady- state condition of the system reached approximately after 2500 s.

2.3 r e s u lt s

As introduced inSection 2.2.3, the analysis of the thermal distribution was predicted using a time-dependent simulation, considering a heat-ing flux as source (6 W) (Figure 13). After reaching the steady-state condition, we can observe a good agreement between the thermal distribution predicted by the FEM simulation (Figure 13A) and the expected one (Figure 11). The thermal behavior along time predicted by the model is instead plotted in Figure 13 B (cross markers) for each of the four positions. Simulated results are then compared with experimental data (dashed lines), obtained on the assembled system (all the fabricated components are depicted in Figure 13 A and the assembled system in Figure 13B). As observable in the plot, the tem-perature of the heating and cooling zone is lower than the melting temperature of the material (∼ 180◦_{C), while higher temperature is}

found in the melting zone, and for each location we obtained a good agreement between simulated and experimental results, with a max-imal error of ∼ 8◦C. The errors between expected and experimental temperatures are presented in Table:

Sensor 1 2 3 4

Averaged Error εf(◦C 8.3 5.6 2.4 3.2

Finally, we tested the assembled system with the PLA filament to verify the thermal distribution and usability of the system. The sys-tem with an extrusion of∼4.5 cm is presented inFigure 14.Figure 14

A depicts the real disassembled system used for the experimental tests. Figure 14B shows the complete system assembled with part of the obtained extrusion coming out from the cooling zone surrounded by the cooler.Figure 14C presents instead the same extrusion without the external cooler. The feeding material has melted and has reached the cooling zone becoming solid in the predicted part of the system. 2.4 d i s c u s s i o n

As already assessed inSection 2.3, the results predicted by the devel-oped models are confirmed by the experimental results. Thus, demon-strating the effectiveness of embedding additive manufacturing tech-niques in artificial system and use them as a strategy for self-growing robot. In particular, the results demonstrate the feasibility of a system that allows the simultaneous deposition of a thermoplastic material on the whole section. The proposed design and implementation cer-tainly present some issues. Firstly, to choose the direction of growth, and perform a bending, an accurate control on the viscosity of the extruded material should be achieved. A possible way to achieve this could be a differential control of the temperature distribution in the

2.4 discussion 27

Figure 13: Thermal simulation considering a 3D model of the real system. The screw connectors are considered in these simulations. In (A) the results of the thermal prediction at steady state; in (B) the thermal simulation and experimental data obtained along time in four different positions of the system.

A

B

C

Figure 14: The realized final design. In (A) the disassembled systems is shown. In (B) the complete assembled systems is shown after the experimental test. In (C) the complete extruded structure is presented without the cooling component of the system. The yel-low material was extruded and solidified moving away from the cooler.

cooling zone, achieved by using for instance, three different cooling chambers. Secondly, the energy efficiency still remains an issue, in fact the high temperatures used to extrude the thermoplastic mate-rial needs to be preserved constant during the whole operation. A possible solution reducing the impact of energy consumption could be the employment of a polymer with a lower melting temperature. The use of a thermal field to induce the change of phase of the mate-rial turns out to be the most intuitive choice (also in view of the ease of availability of 3D printers), but certainly imposes important diffi-culties in terms of feasibility, controllability and energy efficiency of the system. However, we demonstrate in this chapter, the feasibility of the conceptual design opening the way also for a different mechanism enabling the stiffness variation. In fact, the same strategy of deposit-ing a sdeposit-ingle layer of material all at once could be used by exploitdeposit-ing other kinds of phenomenon, different from the thermal (e.g. chem-ical reaction), to induce the phase change of the material. It would be enough to construct a model describing the selected phenomenon and the movement of the feeding material, but the concept of evert-ing or depositevert-ing some kind of raw material at the tip level would remain unchanged. Nevertheless, the use of a different physical field to induce phase change should certainly be assessed against the con-trollability, effectiveness and energy efficiency of the transformation itself. Moreover, our design can potentially enable a faster growth speed, with respect for instance to the approach adopted in [10]. In

fact, considering having the speed of the feeding material at the en-trance of the heater imposed at 13 mm/s the growing speed could be chosen in the range 0.15 - 0.30 mm/s, having the approximation of a constant density of the feeding material and the control of the thermal outflow. In this perspective, the system would result∼ 4.5 times faster with respect to the FDM technique used in [10] (from 4 mm/min to

18 mm/min). Another additional advantage of the proposed design is the simplicity. In fact, it accounts of only four components and a single motor for pulling the filament is envisaged. Also, this simple design allows for miniaturization and fast assembling of the complete system.

2.5 c o n c l u s i o n

This chapter investigates material extrusion as an additive manufac-turing strategy for enabling the growth from the tip of artificial sys-tems. The proposed novel design of the extruder allows the deposi-tion of the material all at once on the whole secdeposi-tion of deposideposi-tion. This strategy can potentially enable a fast growth speed (up to 18 mm/min), while the simple design guarantees a fast assembling of the system. The results obtained in such investigation are encourag-ing from the speed improvement performance, but from the energetic