School of Engineering
MASTER’S
DEGREE
INBIOMEDICAL
ENGINEERING
THESIS
4D printing of conductive and non-conductive
materials for biomedical applications
Candidate
Advisors
Simone Micalizzi Eng. Carmelo De Maria
Prof. Andrés Díaz Lantada
Index
AbstractChapter 1 Introduction to 4D printing: materials and applications ... 1
1.1 State of the art: 3D printing of complex functional structures ... 1
1.2 Smart structures manufacturing ... 3
1.3 Smart Materials for 4D printing applications ... 7
1.3.1 Shape memory alloys (SMAs) ... 7
1.3.2 Shape memory polymers (SMPs) ... 8
1.3.3 Piezoelectric polymeric materials ... 10
1.3.4 Electroactive Polymers (EAPs) ... 11
1.3.5 Conductive polymers ... 13
1.3.6 Hydrogels... 15
1.4 4D Bio-printing ... 16
1.4 Activation methods of 4D printed structures ... 17
1.4.1 Water activated 4D printing ... 17
1.4.2 Heat and stress activated 4D printing ... 18
1.5 Aim of the Thesis ... 20
Chapter 2 ... 22
Materials and Methods ... 22
2.1 Materials ... 22 2.2 Methods ... 25 2.2.1 Mechanical characterization ... 25 2.2.2 Electrical characterization ... 26 2.2.3 Electromechanical characterization ... 27 2.3 Printing technologies ... 27 Chapter 3 ... 31
Results and Discussions: Conductive materials characterization ... 31
3.1 Mechanical properties ... 31
3.2 Electrical properties ... 32
3.3 Electromechanical properties ... 36
Chapter 4 ...43
Device design ...43
4.1 Shape memory programming ... 43
4.2 Actuation principle ... 45
4.3 Actuators device design ... 47
4.4 FEM analysis ... 49
4.4.1 Electro-thermal analysis ... 49
4.4.2 Shape recovering FEM Model ... 50
4.5 Sensing devices working principle and design ... 53
Chapter 5 ... 56
Results and Discussions: actuators and sensors ... 56
Fabrication of actuators ... 56
5.2 Electro-thermal analysis ... 58
5.3 Shape-memory training and actuation ... 63
5. 4 FEM model of shape recovering ... 65
5.5 Sensing devices manufacturing and testing ... 69
Conclusions ... 72
Abstract
Additive Manufacturing (AM), commonly known as three-dimensional (3D) printing or rapid prototyping, has been introduced since the late 1980s. Considerable amount of progress has been made in this field. Recently, multi-material 3D printing using “smart materials” has led to the development of a new fabrication paradigm called four-dimensional (4D) printing, where the fourth dimension is given by controlled evolution in time of 3D printed objects. Smart materials have the ability to change their shape or properties under the influence of external stimuli. 4D printing has the prospective to simplify the design and manufacturing of different products and the potential of automating the actuation of devices that react under the application of mechanical, chemical, thermal and other stimuli. Simplifying the design of the 3D printed products could help to decrease the logistic cost as the printed products can be stored as compactly as possible before activated to full volume and functionality. 4D printing can also provide an alternative solution to the fabrication of structures that are difficult to produce by conventional technologies. Products can be made more durable as well as they can be designed to adapt to environmental changes such as humidity level or moisture content, temperature, altitude and pressure. 4D printing could find applications in the biomedical field, allowing prototyping of interactive or sensing devices for patient rehabilitation: e.g. Devices that normally need electrical motors for actuation could be simplified by eliminating these components but maintaining their functionalities thanks to intrinsic motion properties.
This thesis was carried out in the context of a collaboration between the Research Centre “E. Piaggio” of the University of Pisa and the Universidad Politécnica de Madrid. The aim of this thesis was the complete engineering design of electrically controlled shape-memory actuators and sensors for biomedical applications. The design principle is based on the combination, by FDM 3D printing, of non-conductive elements (made of Polylactic Acid (PLA) or thermoplastic elastomer (TPE)) and conductive parts (made of conductive thermoplastic polyurethane rubber (cTPU) or conductive Polylactic Acid (cPLA)). The conductive materials were characterized from mechanical, electrical and electromechanical point of view. The fabrication was accomplished in a single printing
step, using a dual extrusion 3D printing mode, which minimizes post-operations and the use of additional heating elements such as resistors, Peltier cells or knitted or glued thermal patches. Geometries were designed by a Computer Aided Design (CAD) software and optimized with the support of a Finite Element Modeling (FEM) software. 4D printed actuators were based on the Joule effect experienced by the conductive thermoplastic material when voltage is applied, and on the shape-memory property of passive materials, triggered by the local heating due to conductive elements. The FEM models, the heating process of manufactured prototypes and the consequent shape recovery process were validated with the support of infrared thermography.
Pressure sensing devices were fabricated by integrating in the printing process sensing elements connected by 3D printed paths.
A “smart” keyboard that react to finger pressure, a sensorized orthopedic insole and three different actuators (a hexagon-shaped robotic claw or gripper, a finger-like actuator composed of seven phalanxes and an active “snake” toy), were manufactured and presented. These tests helped to assess and validate the proposed engineering design methodology and to put forward some current challenges when compared with alternative approaches for the development of complex shape-memory actuators and sensing devices.
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Chapter 1
Introduction to 4D printing: materials and applications
In this chapter the assembling methodologies of materials with different characteristics to create a shape-shifting structure will be explained. A library of smart materials with applications in the field of 4D printing and a research on the stimuli that could activate them will be presented.1.1 State of the art: 3D printing of complex functional
structures
Additive Manufacturing (AM), commonly known also as “3D Printing”, is a fabrication process in which solid objects are built by depositing materials layer-by-layer starting from digital models. Since the first 3D printer patent by Charles Hull in 80s, various 3D printing processes have been developed (1).
Currently, these technologies were classified according to different principles including the initial state of printing material (e.g. solid, powder, liquid) or the physical printing principles, as in the ISO 52900 (2), as shown in figure 1.1.
Figure 1.1: Different categories of additive manufacturing technologies according to the terminology proposed by ISO/ ASTM52900:2015: Binder jetting, Directed energy deposition, Material extrusion, Material jetting, Powder bed fusion, Vat polymerization, Sheet lamination (2).
2 Although the initial technology was limited to the production of prototypes, its applications span in various manufacturing areas including automotive, aerospace, and healthcare. 3D printing technology in medical devices design and fabrication unlocks unprecedented possibilities to fully customize a device to the dimensions and the needs of patients. The flexibility of 3D printing allows designers to make changes easily without the need to set up additional equipment or tools. It also enables manufacturers to create devices matched to a patient’s anatomy (patient-specific devices) or devices with very complex internal structures. Limiting the scope to biomedical field, AM has been successfully used for the fabrication of:
Anatomical models for surgery planning and training: specific patient
organ replicas can be 3D printed helping surgeons to practice on before performing complicated operations. This type of procedure was performed successfully in surgical operations ranging from a full-face transplant to spinal procedures (3).
Custom-made prosthetics: 3D printing in the medical field can be used to produce prosthetic limbs that are customized to fit the patient. 3D printing significantly speeds up the production process comparing to the conventional ones, as well as creating much cheaper products that offer patients the same functionality as traditionally manufactured prosthetics. The low price of these products makes them particularly applicable for use with children, providing the correct prosthetic limb for each moment of their growth (4).
Surgical instruments: sterile surgical instruments, such as forceps, hemostats, scalpel handles and clamps, can be produced using 3D printers (5). Bio-printing tissues and organs: a computer-guided pipette and inkjet print head were used to layer living cells, referred to as bio-ink, on top of one another to create artificial living tissue in a laboratory. These tissue constructs can be used for medical research thanks to their ability to mimic organs on a miniature scale (in vitro model) (4).
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1.2 Smart structures manufacturing
Some 3D printing technologies, such as “material extrusion” (e.g. Fused Deposition Modelling, FDM) and “material jetting” (e.g. PolyJet), enable the fabrication of components with fine details by accurately placing multiple materials at any position within a design domain. By printing smart materials it is possible to fabricate shape-shifting structures that can take multiple shapes, depending on the environmental conditions, exploiting different physical principles such as capillary forces, residual stresses, energy released from active materials (6), whose effects can be enhanced by the geometry and the printing process. A multi material 3D printer provides two main advantages for the smart structure manufacturing:
mixing of materials with different ratios to achieve the desired physical/chemical properties;
programming the structure movement, by pattering the printing materials. The strategy to trigger the shape-shifting of a smart structure is exploiting the stress at the interface that occurs when two different materials are in contact. Appling a stress gradient, that could be mechanical or thermal, leads structure to bend.
A strategy used for generating the bending moments is to combine two layers of different materials into a bilayer construct. Dependent on the layer thicknesses and the induced strain, surface bending may be programmed (6). Upon activation, the deformation of the main straining layer is constrained by the second layer and therefor one layer is loaded in tension while compressive stresses are observed in the other layer. The generated bending moment results in global bending of the constructs as shown in figure 1.2.
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Figure 1.2 Shape-shifting structures where a bi-layer of a SMP and a hyperelastic polymer transforms into a (a) rolled, (b) spiral, (c) wrinkled and rolled, or (d) wave-like structure (7).
Figures 1.3 and 1.4 show, instead, how different infill pattern and temperature characteristics of 3D printed active composite (PAC) flat structures, fabricated using the FDM technology, lead to a different shape-shifting (8).
Figure 2.3 Active helix shape. (a) The details of the design. (b–e) The deformation behavior of the active helix in the recovery process. (8)
Figure 1.4 Active “wave” shape. (a) The design of the structure. The positions of fibers are exchanged in two segments. The figure is used for describing the design method, not to scale. (b–e) The deformation behavior of the active “wave” shape when heated to a temperature of 70 °C (8).
The matrix structure is built with a shape memory polymer, TangoBlack+, which has the lowest glass transition temperature (Tg~2°C) of the three materials. Two families of digital Shape Memory Polymer (SMP) fibers with different Tg are embedded in the
5 two layers, respectively, with prescribed volume fractions (fiber 1: DM8530, Tg~57 °C; fiber 2: DM9895, Tg~38°C) (8). An active helix, as shown in figure 1.4, presents different orientation of the fibers. After the programming step, the printed flat strip bends as shown in Fig. 1.4a. When an increase in temperature is provided, the sample bends and curls significantly and forms a helix shape and finally recovers to the flat permanent shape (Fig. 1.4b–e) due to the bend-twist coupling of the asymmetric composite laminate. By alternating the position of fibers in the top and bottom layer, which is difficult to do with traditional composite laminates but straightforward with 3D printing, is possible to create different shape shifting (Figure 1.5) (8).
Instead of stacking multiple layers which exhibit different dimensional changes in response to certain stimuli, the different materials could be tessellated in a single layer construct as well, as shown in figure 1.6. One simple example of a swellable material tessellation in single layer constructs is the parallel alignment of two hydrogel strips with high and low swelling ratios (volumetric expansion of 4.3 and 2.2, respectively) (6).
Figure 1.6: Multiple material tessellations resulting in different shape-shifting materials. Parallel alignment of multiple strips could be used to program self-helixing dependent on the orientation angle (a-b). Programming of multiple shape transformations using different responsive materials (c). Axisymmetric material tessellations used for programming of a negative Gaussian curvature (d-e).
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Incorporation of cuts in the material tessellation allows for programming more complex shapes including the Sydney opera house (f). (6)
A method for production of material tessellations is the photo-polymerization of swellable hydrogels. Controlled by the amount of light exposure, the conversion of monomers and the degree of crosslinking are enhanced, thereby increasing the material stiffness. When submerged in an aqueous medium, the swelling ratio of the hydrogel is dependent on the crosslinking density. By controlling the pattern and amount of light exposure, regions with different swelling ratios could be created to generate in-plane compressive stresses upon activation.
Drawbacks of these bi-layer structures lying on the interface between the two materials. When a high shear stresses at the interface is applied the risk of delamination and/or interfacial sliding increase.
Another approach to manufacture actuating structures consists in create a gradient in the material properties of a monolayer construct. This irregularity could be used to generate a stress gradient along the thickness of the material.
Following this principle, it is possible to polymerize a photocurable polymer resin for production of self-rolling structures (25). During the polymerization process, the light is exposed from one side while being partially absorbed by the polymer, which results in a gradient in light intensity along the material thickness. This approach creates a gradient in the crosslinking density of a layer of swellable material. The stiffness of the material increases with higher crosslinking density, thereby reducing the swelling ratio of the material. Dipping in water therefore results in curving.
Different patterns of light exposure could be performed as function of the desired motion (Figure 1.7).
7 Electrophoresis during the photo-polymerization process could be used to create a gradient of particles along the thickness of an active material in order to locally reduce the swelling/shrinkage ratio (figure 1.7b).
1.3 Smart Materials for 4D printing applications
Smart materials are widely researched and used in practice, but there is disagreement on the definition of what they are(1). One of the definitions is that smart materials can demonstrate coupling or conversion of energy between various physical domains such as the conversion of thermal energy into mechanical work (9). Others define smart materials as able to sense fluctuations in their external environment and generate a useful response by either changing their material properties or geometries (10). In this paragraph examples of smart materials, with their relative 4D printing applications, will be presented.
1.3.1 Shape memory alloys (SMAs)
The SMAs can directly convert thermal energy into mechanical work, thanks to their shape memory characteristic. The shape memory effect is a result of the transformation between two different crystalline phases: martensitic phase (low temperature) and austenitic phase (high temperature). These two phases allow SMAs to alter shape and return to its original shape when exposed to high temperatures (11). The particularity of a SMA lies in the fact that after annealing, it can be deformed into a temporary custom shape and, if it is heated above the transition temperature it returns to its permanent shape. The nickel-titanium (NiTi) (12), also known as Nitinol, is an example of SMA. It was developed at the Naval Ordinance Lab, which has found popularity in the automotive, aerospace, biomedical, robotics, and soft actuation industries (11). NiTi exhibits the best shape memory behavior such as high percentage of shape recovery among all the different types of SMAs (13). However, NiTi SMAs are not easy to process by conventional methods (14). The NiTi alloys are extremely compositional sensitive. Impurity elements can be easily picked up during high temperature processing and cause problems such as oxidation and microstructural defects. These problems can then lead to a shift in the transformation temperatures.
8 One potential solution is to fabricate NiTi parts by additive manufacturing processes such as selective laser melting (SLM). The shape memory in a 3D printed SMA is showed in figure 1.8.
Figure 1.8 SLM NiTi cantilever beam bending upwards in the low temperature martensitic phase on the left and transforms to a straight state in the high temperature austenite phase on the right (11)
The process involves lowering the temperature of the 3D printed SMA cantilever below its martensite phase and deforming the cantilever. Next, the cantilever is heated above its austenite phase and deformed. The annealing process is repeated until the shape-memory effect is observable in the cantilever. The cantilever will deform into its martensite shape when the temperature falls below the martensite temperature and transform into a different shape when the temperature is raised above its austenite temperature (15).
1.3.2 Shape memory polymers (SMPs)
The SMPs possess the ability to remember a permanent shape and transform to a temporary shape when exposed to a number of external stimuli such as temperature, pressure, water, pH-levels, magnetism, or light. Thanks to this characteristic it is possible to create products that react to their environment automatically without the need for complex, heavy, and expensive electronic actuation systems. In comparison with SMAs, this cycle of programming and recovery can take place in a much shorter time interval and polymers allow a much higher deformation rate between deformed and permanent shape (16).
Nevertheless, SMPs have their drawbacks compared to SMAs, as they possess low strength and low operating temperature (1). However, they are cheaper, possess high strain recovery, low density, biocompatibility, and biodegradability when compared to
9 SMAs (1). An example that explain how to provide a thermally induced shape memory in polymers is shown in figure 1.9.
Figure 1.9 Schematic representation of the shape memory effect with four steps: (1) memorized shape after molding and cooling; (2) free deformation due to the rubber elasticity of the amorphous portion by heating over Tg under an applied force; (3) shape fixity by cooling below Tg; and (4) shape recovery by heating over Tg under free load condition (17)
For heat-induced SMPs, the temporary shape is obtained by deforming the material above its glass transition temperature (Tg). Cooling it below this temperature is possible to maintain the deformed shape. A further heating above the Tg leads to the relaxation of the stressed polymer that recover its permanent shape.
Figure 1.10 shows a SMP 3D printed with a PolyJet with 25 𝜇𝑚 resolution in X and Y direction and 16 𝜇𝑚 in Z direction. Shape changing is activated by temperature. Recovery rate of printed components can be pre-designed and sequentially controlled by graded heating in water (18).
Figure 1.10 Printed sample of ‘NTU’ before heating (a) and after heating (b) (1)
This SMP structure consists of three connected letters ‘NTU’ in the printed form. It was then heated to above its Tg temperature and straightened at high temperature and cooled to room temperature while maintaining the pulling force. Once the sample has reached room temperature, the pulling force was removed and it took the shape in
10 Figure 1.10(a). When being heated to above the Tg, the sample returned to the printed form shown in Figure 1.10(b), demonstrating a full shape recovery.
1.3.3 Piezoelectric polymeric materials
These materials are able to produce electrical charge or voltage when experiencing an externally applied stress and vice versa. The applications of piezoelectric material can be found in loud speakers, acoustic imaging, and energy harvesting, actuators, transducers and tissue regeneration. Piezoelectric polymeric materials have some unique characteristics as compared to other piezoelectric materials. These materials are suitable for systems that require mechanical flexibility, small active elements, biocompatibility and solution-based processability (1). Classification of piezoelectric polymers is based on the topology and dipole moment. They are divided into bulk polymers, polymer composites and voided charged polymers. In particular, ceramic/polymer composites can be defined as a material in which the ceramic phase is dispersed in a polymer matrix (19). Mechanical properties of polymers allow their use where traditional piezoelectric ceramic crystals are not effective such as flexible and wearable electronics. A polymer may or may not exhibit piezoelectricity. Materials such as Polydimethylsiloxane (PDMS), and Polyvinyl chloride (PVC), do not have any inherent piezoelectricity, but, because of their low stiffness coefficient, low acoustic impedance, thermal and chemical stability, they can be combined with piezoelectric materials like Barium Titanate (BaTiO3) and Lead zirconate titanate (PZT), which have high dielectric permittivity. Another type of composites can be made by mixing piezoelectric polymers such as Polyvinylidene fluoride (PVDF) with piezoelectric ceramics like BaTiO3, PZT, and Zinc oxide (ZnO). Different techniques are available for microfabrication and nanofabrication of piezoelectric materials, but they are not easy to adopt (20). Recently a new nanofabrication method that produces 2D and 3D piezoelectric nanoparticle polymer composite structures has been developed by using Digital Projection Printing (DPP). The main advantage of DPP is that its resolution can reach 1 μm and it can be carried out over a large area with high reproducibility and precision (20). Moreover, the equipment is relatively less complex and the fabrication time is shorter compared to some other techniques (21). Figure 1.11 shows one of the 3D samples fabricated with the DPP technique.
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Figure 1.11 Piezoelectric polymeroptically printed into three-dimensional (3D). Schematic (a) of the DPP setup that projects dynamic digital masks on the photoliable piezoelectric nanoparticle, honeycomb array (b) printed using the digital projection printing. Microtubule structure (c) formed by releasing a honeycomb array from the substrate. The film rolls up after release due to slight stress gradients in the film. (20)
Manufacturing process of the microtubule structure consists in the projecting of a honeycomb image onto the nanocomposite solution. Once the solution is polymerized, the 2D honeycomb array is removed rolling up into the tubule shape automatically (fig. 1.11c). The diameter of the tube and the extent of the rolling can be controlled by printing the layers with different thermal expansion coefficients, densities or lattice parameters (1). Minimize the DPP fabrication time by tuning the irradiation power, photo-initiator concentration, monomer concentration, nanoparticle loading and the addition of a quencher and the resolution limits of both 2D and 3D samples fabricated are the challenges for this technique (20).
1.3.4 Electroactive Polymers (EAPs)
Dielectric Elastomer Actuators (DEAs) (22) are made with an incompressible and highly deformable dielectric medium made of EAPs, included between the two parallel (soft) plates (creating, in practice, a capacitor). When an electric field is applied across the parallel plates, coulomb forces between the charges generate a stress, called the Maxwell stress, causing the electrodes to move closer. This movement squeezes the
12 elastomer, causing an expansion in the lateral direction (23). Dielectric elastomers show efficient coupling between electrical energy input and mechanical energy output (24). EAPs are classified depending on the mechanism responsible for actuation as electronic EAPs (which are driven by electric field or coulomb forces) or ionic EAPs (which change shape by mobility or diffusion of ions and their conjugated substances) (25). The electronic EAPs such as electrostrictive, electrostatic, piezoelectric, and ferroelectric generally require high activation fields (>150V/m) which are close to the breakdown level of the material. The electronic EAPs also have high energy density as well as a rapid response time in the range of milliseconds. In general, these materials have a glass transition temperature inadequate for low temperature actuation applications. In contrast, ionic EAP materials such as gels, ionic polymer-metal composites, conducting polymers, and carbon nanotubes require low driving voltages, nearly equal to 1–5 V. One of the constraints of these materials is that they must be operated in a wet state or in solid electrolytes.
Bending and gripping are simple actuation mechanisms that can be mimicked by dielectric elastomers (26) as shown in figure 1.12.
Figure 1.12: Actuator gripping an egg (26).
The elastomer, 3D printed using FDM technique, is coated with compliant electrodes to form a deformable electrostatic capacitor. When a voltage of 3kV is applied to the elastomer, the thickness of the elastomer reduces, and the area expands, resulting in an opening (Figure 1.12 on the left). The final equilibrium state without applied voltage
13 resembles the form of a gripping element (Figure 1.12 in the middle). Finally, by removing the voltage the elastomer grasps the object (Figure 1.12 on the right) (26).
1.3.5 Conductive polymers
These materials present a polymeric matrix filled with conductive particles such as carbon black (CB), carbon nanotubes (CNTs), or graphene. CB is one of the most used fillers for the enhancement of electrical conductivity, despite of the fact that its own conductivity is much lower than the electrical conductivity of metals or graphene. Electrical conductivity of dry compressed CB is of the order of 102 S/m (27). Two main factors influence the electrical conductivity of CB filled:
Content of CB in the polymer matrix.
Spatial distribution of the CB particles; in fact, in a highly branched CB structure, obtained by controlled processing or design (27), the increase in electrical conductivity occurs at lower CB concentration in comparison with more random distribution.
According to the percolation theory, there is a critical threshold of conductive content over which the non-conductive matrix suddenly becomes an excellent conductor, since at least one continuous path allows the current passing through the conductive particles as shown in figure 1.13.
Figure 1.13: Percolation threshold and saturation for particles of variable diameter
Larger cluster form complete paths without interruption from one side of the board to the other providing a transition from non-conductive path to a conductive one. Generally the electro-conductive compounds based on the percolation phenomenon
14 have a conductivity that changes with mechanical stress. In fact, for a given sample, the number of conductive particles, and consequently of conductive paths, increases compressing the material and therefore the relative concentration of particles. In the case of a high traction, the particles are displaced and the conductivity decreases. High stretching increases the rate of breakage of conducting networks and the distance between them becomes much more predominant with consequently resistance increase (28). A representation of what said above is shown in figure 1.14.
Figure 1.14 Conceptual model for structural change of the fiber-filled composite during low and high stretching
The electrical resistance R of these materials varies in a fairly narrow range, typically 102÷ 103[Ω] for samples of a few 𝑚𝑚2.
FDM-based 3D printing of conductive polymers finds applications in the electronic field for the development of 2D and 3D circuits as shown in figure 1.15.
Figure 1.15 2D and 3D circuits manufactured using FDM-based 3D printing of conductive (black) and non-conductive (white) materials (29)
15 The 2D circuit (figure 1.15 on the left) is 3D printed using thermoplastic conductive composite (29.8 wt% CB in polypropylene (PP) matrix) as conductive track (black) on an ABS substrate (white) (29).
1.3.6 Hydrogels
Smart hydrogels are matrices with high water content that possess the ability to respond to external stimuli such as electric, ionic strength, light, magnetic field, pH and temperature. They have unique features such as shape memory, self-healing and controllable sol–gel transition. Some hydrogels can drastically, and reversibly, alter their volume in response to changes within their environment. One example of hydrogel are the Ionic Covalent Entanglement (ICE) hydrogels that can be 3D printed (30) and demonstrate high toughness. ICE gels are a type of an interpenetrating polymer network hydrogel that is made up of an entanglement of a polymer network cross-linked with metal cations and a second polymer network cross-cross-linked with covalent bonds (31) (32). The Poly(N-isopropylacrylamide) (PNIPAAm) is a widely studied temperature-sensitive hydrogel that exhibits a large reversible volume transition at a critical temperature, Tc in the range 32–35 °C (33). Structures made with a composite of Alginate/PNIPAAm ICE gel were 3D printed and thermally actuated as shown in figure 1.16.
Figure 1.16 Alginate/PNIPAAm ICE hydrogel tensile specimen swollen in water at a) 20 °C and b) 60 °C. (34)
The activation temperature is set above the PNIPAAm thermal transition so as to rapidly heat the gel and generate fast actuation. The alginate portion of the ICE gel resists the contraction of the thermally responsive PNIPAAm phase, so the contraction ratio
16 decreased as the alginate fraction increases. The gels dramatically changed in size when heated (figure 1.16). The volumetric contraction ratio on heating from 20 to 60 °C increases from 6.1 to 16.7. The reversible nature of the thermally induced actuation is observed when the alginate/PNIPAAm gels swelled to their initial equilibrium conditions when cooled from 60 to 20 °C (34).
1.4 4D Bio-printing
Bio-printing can be defined as the use of material transfer processes for patterning and assembling biologically relevant materials – molecules, cells, tissues, and biodegradable biomaterials – with a prescribed organization to accomplish one or more biological functions. The main advantages of bio-printing include the ability to mass produce tissue engineered products, high accuracy in positioning the different types of cells and capability to fabricate high cell density tissue (35). The shape-shifting ability of 3D bioprinted structure (4D bio-printing) was exploited in different approaches, summarized in figure 1.17.
Figure 1.17: Three approaches in 4D Bio-printing (36)
In the first approach (shape change), a substrate material (e.g., smart biopolymer or responsive hydrogel), upon stimulus, folds into a pre-defined 3D configuration, and the printed cells or tissue materials simply follow the folding of the substrate and form into a desired shape (36). By utilizing the potential of 3D printing to fabricate structures made of smart hydrogels, the fabricated 4D bio-printed structures or bio-origami
17 hydrogel scaffolds can have the capability to self-fold or self-unfold in response to external stimulus. Cells could be seeded into more complex scaffold, providing better strategies for drug delivery systems, and improvement of minimally invasive medical devices. For example, hygroscopic 4D printing shows how objects can react intelligently to their environment without the need for human interaction. Intelligent sensors or structures could be deployed in extreme environments that prove difficult for human exploration such as deep sea exploration. The hygroscopic polymers can be made of soft materials that may prove useful when handling biological substance such as tissue, organs, or live flora and fauna. The second and the third approach can be seen as the 4D printing interpretation of the scaffold-based tissue engineering and the bioassebly principles, respectively. In fact, in the second approach (size change) a 3D printed polymer scaffold is implanted first and then accommodates the growth of tissue or organ over the postsurgical period (37). When the tissue or organ becomes stronger and stronger, the scaffold gradually breaks and is absorbed by the body. In this approach, the growth of the tissue could be seen as the stimulation. In the third approach (pattern change), micro-droplets of cells are precisely deposited into a certain pattern, and then the pattern changes over time due to cell communication and self-organization (38). In this approach, self-assembly is stimulated to occur, but what could stimulate the pattern to change is not clear yet (39).
1.4 Activation methods of 4D printed structures
Current 4D printed examples utilize pressured air, water absorption, and thermal shape memory or light and chemical activation for shape changing. This section regroups the aforementioned structures and materials according to their activation mechanism.
1.4.1 Water activated 4D printing
Activation in these kind of structures is made controlling the bending angle and direction of the expanding material by placing rigid materials in selected areas that prevent the hygroscopic material from expanding in unintended directions. In the example of figure 1.18, the active material is a hydrophilic polymer that can expand
18 150% when exposed to water (11). The multi-material printing uses a static rigid material as the framework of the object and the smart material as the programming of the object. When the entire part is dipped in water, only the areas with hygroscopic material will actuate. These complex components have demonstrated 1D folding, 2D folding and 2D folding with stretching (figure 1.18).
Figure 1.18: Complex 2D multi-material component exhibiting stretching and folding from left to right, and top to bottom (1)
This component deforms into convex and concave surfaces via the folding deformation and ring stretching deformation. Nevertheless, according to the results obtained, mechanical degradation was encountered when the components undergo the transformation cycle of folding and unfolding (wetting/drying) repeatedly (40). Furthermore, the transformation between the different shapes is not permanent. Thus, depending on the applications, this reversible transformation can either be an advantage or disadvantage.
1.4.2 Heat and stress activated 4D printing
An advantageous activation method for 4D printing is the use of high temperatures to trigger the smart material for shape change purposes and the concept of self-assembling origami. Heat and stress are used to activate a shape memory composite to bend at different rates and directions depending on the design of the hinges. Glassy polymers in the form of fibers exhibit shape memory effects when heated above their Tg and are 3D printed within an elastomeric matrix. This combination of elastomer and glassy polymer fibers creates a soft composite, which was named printed active composites (PACs) (41). Volume and orientation of the glassy polymer fibers inside the polymer matrix can be specified using CAD software. These characteristics are
19 important design parameters for PACs since they can determine bending angle, speed, storage modulus, strain, and fixity. Another example of heat activated structures are those made with light activated polymers. Light is an effective activation technique because it is an abundant, wireless and controllable source of energy that can be used to activate materials as SMPs (1). Light-activated SMPs have been used in areas of self-assembly structures, complex folding methods, transformative surface deformations (42), UV sensor and filters (20), and soft robotics. Some 4D printing researches that uses light-activated materials actually use the heat from the light source to activate the shape change properties (1). Figure 1.13 shows two 3D printed box. The first one (figure 1.19 (1)) made with PACs soft composites, activated by an environmental increasing in temperature and the second one (figure 1.19 (2)), infrared light triggered, made with polystyrene films and black ink printed on areas of the sheet designated as hinges.
Figure 1.19: 3D printed self-evolving box. PAC box (1) triggered by the environmental increasing temperature and light activated box (2)(7).
Both structures, shown in figure 1.19, were activated heating above its glass transition temperature (Tg). The PAC hinges (figure 1.19 (1)) are pre-programmed with definitive bending angles using the relation of PAC hinge length and applied strain to determine the bending angle. The walls of the box should bend at a 90°, so the hinges required a strain of 20% to be applied while the material.
The black ink hinges (figure 1.19 (2)) heat up quicker and bend faster than the polystyrene structure. Ink printed part on top of the sheets bends towards the light
20 direction and ink printed on the bottom of a transparent sheet bends away from the light. Altering the pattern geometry, width produces different bending angles, bending times, and light intensity requirements (11).
1.5 Aim of the Thesis
The objective of this thesis is the complete engineering design of geometrically complex shape-memory actuators and sensing devices, for biomedical applications such as custom-made prosthetics and surgical instruments. The developed devices help to assess and validate the employed 4D printing technique and to put forward some current challenges when compared with alternative approaches for the development of medical devices. Devices were fabricated, in a single printing process, by dual extrusion 3D printing, using both conductive composite filaments and nonconductive thermoplastic filaments. For what to concern the actuators, after fabrication, they will respond in a controllable manner to an external electrical stimulus, resulting in a change in shape or physical properties over time. Such final testing sensorized devices that incorporate pressure sensors, were developed and manufactured. The sensor was added during the manufacturing process and placed between two conductive materials blocks. In this case, the conductive material coating will preserve the integrity of the sensor during the wiring process providing the information to the measurement tool.
The next chapters are organized as follows:
Chapter 1 provides an overview of the used materials, and of methods to provide their mechanical, electrical and electromechanical characterization. Comparison between two different 3D dual printing technologies, is presented.
In Chapter 2, results from characterization of conductive materials are discussed. For both conductive PLA and conductive TPU a lumped electrical model that describes their behavior in function of the frequency is provided.
In Chapter 3, a library of prototypes manufactured using two different 3D printing technologies is provided. Printing setting to obtain the best result are discussed. In Chapter 4, fabrication process of actuators and sensing devices is explained. The shape-memory training and actuating process are studied. A Finite Element Method (FEM) analysis, is performed.
21 Finally, in chapter 5, the functionality of a “smart” keyboard, a sensorized orthopedic insole and three different actuators (a gripper, an actuating chain actuator and a sort of active “snake” toy), is described.
22
Chapter 2
Materials and Methods
In this Chapter the materials used will be presented and their main characteristics analyzed. The methods used to characterize them, from the mechanical, electrical and electromechanical point of view, will be explained. Considerations about pros and cons of the available 3D printing technologies will also provide.2.1 Materials
In this thesis the differences between conductive and non-conductive and soft and rigid materials, will be investigated. In table 2.1 different materials used are listed.
Table 2.1 Materials used classification.
The conventional thermoplastic Polylactic-Acid (PLA) (commercial name: PLA INGEO 3D850 filament by Sakata 3D Filaments) and the thermoplastic elastomer (TPE) (commercial name: FilaFlex Original 82A filament by Recreus) filaments, were used as passive materials. The conductive Polylactic-Acid (cPLA) (commercial name: Protopasta CDP12805 by ProtoPlant) and the conductive thermoplastic polyurethan (cTPU) rubber (commercial name: PI-ETPU 95A carbon black by Filiprint) filaments, were employed as electroactive materials. The filament presented two different cross section values: 2.85 mm for the PLA, TPE and cPLA and 1.75 mm for the cTPU rubber. Technical characteristics of materials, from datasheets, are listed in the tables 2.2 and 2.3.
Material Soft Rigid
Conductive Thermoplastic Polyurethan (cTPU)
Conductive Polylactic Acid (cPLA)
Non-conductive Thermoplastic Elastomer (cTPE)
Polylactic Acid (PLA)
23 Characteristic Value Unit PLA TPE Glass transition temperature (𝑻𝒈) 60-65 50-65 °C Melting temperature (𝑻𝒎) 150-160 210-230 °C Specific heat capacity 1800 2000 J/(kg·K) Thermal conductivity 0.13 0.2 W/(m·K) Density 1300 1140 kg/m3 Elastic (Young’s
tensile) modulus 3.5 0.4 GPa
Elongation at break 6 665 %
Flexural modulus 4 NA GPa
Shear modulus 2.4 NA GPa
Printing
temperature 190-210 225-235 °C
Printing speed 30-70 20-40 mm/s
Hot-bed
temperature 25 25 °C
Table 2.2: Comparison of technical characteristics between non-conductive materials, according to their commercial datasheets (NA = not available)
24 Characteristic Value Unit cTPU cPLA Glass transition temperature (𝑻𝒈) 50-65 60-65 °C Melting temperature (𝑻𝒎) 210-220 150-160 °C Specific heat capacity 2000 1800 J/(kg·K) Thermal conductivity 0.3 0.13 W/(m·K) Density 1500 1150 kg/m3 Elastic (Young’s
tensile) modulus 0.1 3 GPa
Elongation at break 400 6 % Hardeness 95° 97° Shore Printing temperature 230-240 200-220 °C Printing speed 15-30 30-55 mm/s Hot-bed temperature 60 25 °C Surface resistivity < 102 NA Ωcm Resistance of a 10 cm sample NA 2-3 kΩ
Table 2.3: Comparison of technical characteristics between conductive materials, according to their
commercial datasheets (NA = not available). Surface resistivity, Elastic (Young’s tensile) modulus,
printing temperature, printing speed and hot-bed temperature were estimated during this thesis work.
25
2.2 Methods
In this paragraph, the tests on the above mentioned materials, for the evaluation of their mechanical, electrical and electromechanical parameters not provided in the datasheets are described. In particular conductive filament samples with different length and diameter were tested to evaluate Young’s Modulus [Pa], resistance [Ω] and resistivity [Ω·cm].
2.2.1 Mechanical characterization
Mechanical characterization was conducted only for the cTPU rubber. A uni-axial tensile test, using a uniaxial tensile testing machine (ZwickRoell Pro Line z005) with a 100 N load cell, imposing a maximum strain of 30% of the initial sample length, was performed. Figure 2.1 shows the testing setup.
Figure 2.1: Tensile test setup for a cTPU rubber filament sample of 71 mm length and 3 mm diameter
Filament samples (3 mm diameter) were tested, in triplicate, at two different strain rates of 1%/s and 1%/min of the initial sample length in triplicate. The average length of the samples was of 72 ± 3 mm.
26 Data were processed to investigate the viscoelastic behavior calculating the residual deformation. From stress-strain curves the Young’s modulus was calculated considering the slope of the first linear part.
2.2.2 Electrical characterization
Electrical characterization for both cTPU rubber and cPLA was conducted. cTPU rubber filaments and cPLA were used. The impedance behavior in the frequency range from 20 Hz to 2 MHz was evaluated by using an Agilent E4980A LCR impedance meter. Measurements were conducted on spool and extruded filament. Extrusion was performed at 235 °C using a 0.4 mm diameter nozzle. Lengths of the samples were 4, 6 and 8 cm. The diameter of the extruded filament was 0.45 mm,while the diameters of the spool filaments were 3 mm and 1.75 mm for cTPU, and 1.75 mm for the cPLA. For each length, spool and extruded filament samples were prepared in triplicate. Figure 2.2 shows the testing setup.
Figure 2.2 Impedance evaluation. Setup for a spool (left) and extruded (right) cTPU rubber filament sample of 8 cm length.
Impedance modulus and phase were acquired and then processed. A fitting transfer function that describes the system behavior in frequency domain was evaluated using Matlab® System identification toolbox. A circuit model and its parameters were then estimated from the transfer function.
For each sample length a mean resistance value was calculated and compared with values obtained from multimeter measurements, which works in DC mode. Materials resistivity was calculated performing a polynomial fitting in Matlab®.
27
2.2.3 Electromechanical characterization
Electromechanical characterization was conducted only for the cTPU rubber. A uni-axial tensile test, using a 100 N load cell, at different deformation (respectively 10%, 20%, and 30%) and strain rate of 1%/s of the initial sample length was performed. Measurements were conducted on spool filament samples with length of 4, 6 and 8 cm and 1.75 mm diameter. For each length, three different samples were used to study the impedance behavior, in frequency domain, at different strain percentage. Testing setup is shown in figure 2.3.
Figure 2.3: Electromechanical testing setup for a cTPU rubber filament sample of 8 cm length and 1.75 mm diameter.
Data were processed using same procedure explained in the paragraph 2.2.2. Impedance behavior and material resistivity dependence on strain, were investigated.
2.3 Printing technologies
In this thesis, the FDM technology was selected for 4D printing complex structures and sensors. In this technology, the configurations that allow the 3D printing of two materials are the single and double extruder. In this thesis work both configurations were used. Pros and cons during printing experience were evaluated.
28 The single nozzle extruder used in this thesis is commercialized by E3D Company with the name of “Cyclops”, and consists in an aluminum jacket with two inlet for the filaments converge into the only hot-end, as shown in figure 2.4.
Figure 3.4: Cyclops extruder configuration
The cyclops extruder was mounted on a standard Prusa i3 rework FDM 3D printer. Figure 2.5 shows the 3D printer before and after modifications.
29 For this single nozzle configuration it is possible to note that:
Pros: presenting a single hot-end, the process of nozzle level calibration is not requested. Cyclops extruder allows to print both separated and mixed materials in a single printing process. The mixing allows to create different materials in terms of mechanical characteristics. By varying the two material percentage it is possible to manufacture smart structures presenting different behaviors as presented in paragraph 1.2.
Cons: the single hot-end configuration needs the cleaning of the nozzle if an object with a clean interface between materials is requested. During material changing, a purge procedure is needed to clean the nozzle, resulting in material waste and in an increase of printing time. Nevertheless, a clean interface, if materials present large differences in terms printing temperature, is difficult to obtain. Using materials with high differences in terms of printing temperature, increases the probability of nozzle clogging.
The manufacturing process with two separated hot-end was performed using the BCN3D Sigma 3D printer as shown in figure 2.6.
30 The machine presented two nozzle at the same level. Every material change, the nozzle pass through a cleaning system located to the side of the printing plate.
The double nozzle configuration have the following vantages/disadvantages:
Pros: using two separated nozzles overcome limits of material wasting and printing time. This configuration allows to manufacture objects that present a clean materials interface using different materials too.
Cons: the nozzles level calibration could become a difficult practice if the 3D printer does not present an auto-calibration system. The material mixing is not allowed.
Main printing parameters, used for extruding the filaments tested as described in this chapter and also for fabricating the various prototypes described in this thesis, are listed in table 2.3.
Printing parameters PLA TPE cTPU cPLA
Layer height (mm) 0.2 0.2 0.2 0.2 Temperature °C Extruder 200 230 235 215 Bed 40 60 70 40 Speed (mm/s) Perimeters 35 20 20 30 Infill 30 20 20 30 Retraction 2 / / 1.5 Retraction (mm) 2 / / 1.5 Extrusion multiplier 1 1. 1.2 1
31
Chapter 3
Results and Discussions: Conductive materials
characterization
In this chapter results from characterization will be discussed pointing the attention on mechanical and electrical properties of conductive materials.3.1 Mechanical properties
A uni-axial tensile test was conducted on the cTPU rubber and his behavior is shown in Figure 3.1.
Figure 3.1: Evaluation of the viscoelastic behavior of the rubber filament under different strain rates: 1%/s red and 1%/min blue
cTPU rubber filament presents a viscoelastic behavior. A residual displacement of 9.2% for samples deformed at a velocity of 1%/s and 10.55% for samples deformed at a velocity of 1%/min, were measured.
cTPU rubber showed an elastic modulus that change in function of the deformation velocity. The Young’s modulus measured for a 1%/min deformation velocity was 68 [MPa], unlike a value of 100[MPa] for a 1%/s deformation velocity. For this thesis work, the elastic modulus of 68[MPa] was considered for the cTPU. The Young’s moduli for the PLA (43), TPE (44) and cPLA (45) were provided by the seller companies and listed in table 3.1.
32 Young’s Modulus (GPa)
PLA cPLA TPE
3.5 ± 0.59 2* 0.42*
Table 3.1: Materials elastic modulus comparison provided by the datasheets. Values provided without the standard deviation (*).
Rigid materials present a Young’s modulus one order of magnitude higher than soft/flexible materials.
3.2 Electrical properties
In this paragraph electrical properties of the conductive materials will be discussed. Comparison between impedance modulus and phase obtained processing data from impedance meter measurements and the Matlab® system identification toolbox fitting are shown in figure 3.3.
Figure 3.3: Figure 21: Impedance modulus and phase for a cPLA sample. Length 8[cm] with a diameter of 1.75[mm]. Data from measurements (blue), and fitting data (red).
According to the impedance measurements, the system presents a pole at high frequency (around 100 kHz) and no zeros. This characteristic was encountered both for cTPU rubber and cPLA spool and extruded samples.
33 Analyzing data from Matlab® system identification toolbox, a transfer function that fits the impedance trend was estimated. Transfer function has the following form:
TF =
AS+B
(eq 3.1)
where, A and B are the estimated parameters.
A circuital model was associated to the transfer function. The parallel between a resistor and capacitor well replicates the material behavior in the frequency domain, whose transfer function is:
TF =
1𝐶
∙
1𝑆+1 𝑅𝐶⁄ (eq 3.2)
Where, by making a comparison with equation 1, we can write the following system,
{
𝐴 =
1 𝐶𝐵 =
1 𝑅𝐶⇒ {
𝐶 =
1 𝐴𝑅 =
𝐴 𝐵 (eq 3.3)Fit to estimation data for the evaluation of A and B provided a value always bigger than 88% for the cTPU filaments samples and 92% for the cPLA. Mean values of resistance and capacitance, for all the cTPU and cPLA samples, measured with multimeter and estimated with the Matlab® model are listed in tables 3.2 an 3.3.
34 Diameter [mm]
Spool 3 mm Spool 1.75 mm Extruded 0.45 mm
Length [cm] 4 6 8 4 6 8 4 6 8 Resistance [kΩ] Measured with multimeter 3.5 4 6 5.5 8 9 28 50 74 Modeled 2.5 ± 0.06 3 ± 0.1 4 ± 0.18 5 ± 0.23 6.5 ± 0.5 7.5 ± 0.18 25 ± 2 48 ± 3 82 ± 7 Capacitance [pF] Modeled 0.3 ± 0.1 0.3 ± 0.06 0.3 ± 0.1 0.3 ± 0.15 0.15 ± 0.06 0.2 ± 0.13 0.05 ± 0.02 0.04 ± 0.004 0.04 ± 0.002
Table 3.2: Mean values of resistance and capacitance for all the spool and extruded cTPU filament samples. Measured values, with a digital multimeter LCD XL830L, have a constant error of 0.3 [kΩ] for the spool and 1.5 [kΩ] for the extruded samples. Modeled values are listed in the form of mean ± standard deviation.
Diameter [mm] Spool 1.75 mm Extruded 0.45 mm Length [cm] 4 6 8 4 6 8 Resistance [kΩ] Measured 0.85 1.4 1.75 8 18 18 Modeled 0.8 ± 0.018 1.2 ± 0.07 1.7 ± 0.046 8.5 ± 0.24 11.5 ± 0.53 18 ± 0.53 Capacitance [pF] Modeled 0.32 ± 0.084 0.24 ± 0.063 0.15 ± 0.032 0.35 ± 0.095 0.13 ± 0.02 0.13 ± 0.01
Table 3.3: Mean values of resistance and capacitance for all the spool and extruded cPLA filament samples. Measured values, with a digital multimeter LCD XL830L, have a constant error of 0.08 [kΩ] for the spool and 0.9 [kΩ] for the extruded samples. Modeled values are listed in the form of mean ± standard deviation.
By plotting the average value of the resistance as function of the samples geometrical properties, the materials resistivity was evaluated as the slope of the linear regression calculated using Matlab® polynomial fitting tool as shown in figure 3.4.
35
Figure 3.4: Polynomial fitting of resistance values in function of the geometrical properties of cPLA filament. (lengths 4-6-8 [cm] and diameter 1.75[mm]). The linear regression weighted on the inverse of the variance.
On the x-axis the length of the sample to the section L/A [cm-1] is reported, while the y-axis represents the mean resistance [Ω] of samples analyzed. The polynomial equation used was (eq. 3.4):
𝑓(𝐿 𝐴⁄ ) = 𝜌 ∙ (𝐿 𝐴⁄ ) + 𝑞 (eq. 3.4)
where 𝜌 [Ω𝑐𝑚] is the modeled resistivity, and 𝑞 is the y-intercept of the line. Same fitting process was conducted for the cTPU rubber and cPLA spool and extruded samples. The goodness of fitting presents the R2 values always bigger than R2= 0.97. Resistivity values, measured with multimeter and derived from the mathematical models (eq. 3.4), are reported in table 3.4.
Resistivity
Spool filament Extruded filament
Resistivity
𝜌[Ωcm] Measured Modeled Measured Modeled
cTPU 26±1.2 25.41±4.5 18.3±1.05 18.82±5.1
cPLA 5.5±0.25 5.1±1.5 4±0.3 3.8±2.3
Table 3.4 resistivity values for spool and extruded samples of conductive materials. Measured values, with a digital multimeter LCD XL830L, have a constant error of 1.1 [Ω·cm] for the spool and 4.5 [Ω·cm] for the extruded cTPU samples and 0.3 [Ω·cm] for both spool and extruded cPLA samples. Modeled values are listed in the form of mean ± standard deviation.
36 Studies conducted on electrical properties of the spool and extruded filaments highlighted a lower resistivity value for the cPLA compared to the rubber (table 3.3).
3.3 Electromechanical properties
Electromechanical characterization was conducted only for the cTPU rubber, given the low failure deformation of cPLA. Impedance was evaluated during a uniaxial tensile strain. Following the same procedure explained in paragraph 3.2, it resulted that samples can be described as a one pole and no zero system. In figure 3.5 trends of modulus and phase measured with the impedance meter, for the cTPU rubber samples after 10% strain deformation, are shown.
Figure 3.5: Modulus and phase trends for the cTPU samples after 10% strain deformation
Figure 3.6 shows trends of measured and fitted modulus and phase data for the cTPU rubber samples after 10% strain deformation.
37 Figure 3.6: Impedance modulus and phase for deformed filament sample with initial length of 8cm and
a 10% strain. Data from measurements (blue), and fitted data (red).
A circuital model that describes the behavior of this system is represented by the parallel between a resistor and a capacitor. Fit to estimation data for the evaluation of A and B provided a value always bigger than the 89%. Mean values of resistance and capacitance measured with multimeter and with the Matlab® model are listed in the table 3.5.
Length [cm] 4 6 8 Deformation [%] 10 20 30 10 20 30 10 20 30 Resistance [kΩ] Measured 3 3 3 4 4 4 6 5 4.5 Modeled 2 ± 0.1 2 ± 0.2 2 ± 0.1 4 ± 0.8 3 ± 0.7 3 ± 0.2 5 ± 0.3 6 ± 0.6 2.5 ± 0.5 Capacitance [pF] Modeled 0.7 ± 0.05 0.25 ± 0.06 0.25 ± 0.05 0.3 ± 0.02 0.3 ± 0.1 0.2 ± 0.07 0.4 ± 0.04 0.3 ± 0.04 0.3 ± 0.1
Table 3.5: Mean resistance and capacitance values of samples in function of an imposed strain of 10-20-30%. Measured values, with a digital multimeter LCD XL830L, have a constant error of 0.5 [kΩ]. Modeled values are listed in the form of mean ± standard deviation.
38 By plotting the average value of the resistance as function of the samples geometrical properties, the materials resistivity was evaluated as the slope of the linear regression calculated using Matlab® polynomial fitting tool. In figure 3.7 the fitted curves, for the cTPU samples at different strain deformations, are shown.
Figure 3.7 Polynomial fitting of resistance values in function of an applied strain for cTPU filament sample of 8 cm.
In particular, on the x-axis sample ratio of the sample final length over the cross-section area (𝐿0+ ∆𝐿)⁄ [𝑐𝑚𝐴 −1] is reported. On the y-axis the average resistance values [Ω] of 8 cm samples length evaluated at different strain percentage are reported.
The line calculated using a polynomial fitting has following form (eq. 3.7):
𝑓 ((𝐿0+ ∆𝐿) 𝐴
⁄ ) = 𝜌 ∙ ((𝐿0+ ∆𝐿) 𝐴
⁄ ) + 𝑞 (eq. 3.5)
Were 𝜌 [Ω𝑐𝑚] is the modeled resistivity, and 𝑞 is the y-intercept of the line. Same fitting process was conducted for all the cTPU deformed samples and the goodness of fitting presents a R2 value always bigger than R2 = 0.74.
Results from the data analysis (fig. 3.7) showed that, for the range of deformation considered in this thesis work, the resistivity was not change in function of the strain.
39 Resistivity values, measured with multimeter and with the Matlab® model are reported in the table 3.6.
Resistivity in function of strain (%)
10 20 30
Resistivity
𝝆[Ωcm] Measured Modeled Measured Modeled Measured Modeled
cTPU 8 9.11±3.2 7.7 7.8±3 6.4 7.3±3
Table 3.6: Resistivity values, measured and modeled, for all the cTPU sample in function of the strain. Measured values, with a digital multimeter LCD XL830L, have a constant error of 0.25 [Ω·cm] for all the deformation percentages.
The Gauge Factor (GF) is defined as the ratio of relative change in electrical resistance R to the mechanical strain ε (eq. 3.6):
𝐺𝐹 =
∆𝑅⁄𝑅𝜀 (eq. 3.6)
Figure 3.8 shows GF values of different samples.
Figure 3.8: Average values of the Gauge Factor for samples with different lengths under increasing strain percentage.
An ordinary one way ANOVA proved that data are not statistically different. Gauge Factor could be considered constant in cTPU.
40
3.4. 3D printing of smart materials
Using the Prusa i3 printer with the cyclope single hot-end extruder, different 3D structures were printed using conventional PLA and cTPU.
The need to clean the nozzle during printing process at each switch of material, was explained in the paragraph 2.3. Most of available slicing software allow to print a tower aside the piece to clean up the nozzle. In this thesis, Slic3r software was used, to perform the slicing procedure (https://slic3r.org). Slic3r does not natively contain this feature, so a modification of the Gcode was necessary, before printing. Gcode is the output of the slicing software that contains all of the instruction that the printer will follow to fabricate the desired piece.
One of the most important aspect to take into account, during printing setting, is the materials extrusion temperature. The cTPU rubber has an extrusion temperature in the range of 235÷245 °C, significantly different compared with the extrusion temperature of the PLA (about 200÷220 °C). This difference could cause the nozzle clogging. A modification of the Gcode was necessary to overcome this problem.
The Gcode section to manage the cleaning nozzle is reported below:
1 T[next_extruder] // material choosing instruction // 2 {if[next_extruder] == 0} G0 X0 Y0 // reaching home position //
3 {if [next_extruder] == 0} G92 E0 // set to 0 the extruder motor of the material 1 // 4 {if [next_extruder] == 0} G4 S5 // wait 5 seconds //
5 {if[next_extruder] == 0} M109 S210 // set PLA extrusion temperature of 210°C and wait until reached //
6 {if[next_extruder] == 0} G4 S5
7 {if[next_extruder] == 0} G1 E200 F100 // extrude 200mm at the speed of 100 mm/min // 8 {if[next_extruder] == 0} G92 E0
9 {if[next_extruder] == 1} G0 X0 Y0
10 {if[next_extruder] == 1} G92 E0 // set to 0 the extruder motor of the material 2 // 11 {if[next_extruder] == 1} G4 S5
12 {if[next_extruder] == 1} M109 S240 // set cTPU rubber extrusion temperature 240°C and wait until reached //
13 {if[next_extruder] == 1} G4 S5
14 {if[next_extruder] == 1} G1 E200 F100 // extrude 200mm at the speed of 100 mm/min // 15 {if[next_extruder] == 1} G92 E0
41 The extruder is set at 0 to print the PLA and at 1 to print the cTPU rubber. The “if statement” ensures, at each material switching, the correct temperature setting. In the slicing software a different printing speed for each material: 35 mm/s for PLA and 15 mm/s for cTPU rubber, respectively, was set.
Figure 3.9 shows the CAD model of a finger-like structure, its slicing procedure with the software Slic3r and the printed part manufactured with the Prusa i3.
Figure 3.9 CAD design (up), slicing (middle) and 3D printed object (down) of a finger-like structure. Materials used: PLA (green) and cTPU rubber (black).
Figure 3.9 (bottom part) shows how, although the cleaning nozzle procedure was performed, a certain mixing was present in the PLA matrix (green part). This phenomenon is related to the mechanical properties of the two materials. The rubbery nature of the cTPU causes the presence of deposited material in the hot-end, in larger quantities than the PLA, during the extrusion. Figure 3.10 shows how varying commands of nozzle cleaning was possible to manufacture a clamp presenting a cleaner interface between materials over the entire structure.
42
Figure 3.10 CAD design (left), slicing (middle) and 3D printed object (right) of a clamp structure. Materials used: PLA (green) and cTPU rubber (black). One block on the slicing image (middle) was one
centimeter long.
For the clamp manufacturing, lines 7 and 14 of the Gcode, showed above, were modified as following:
7 {if[next_extruder] == 0} G1 E4000 F100 // extrude 400mm at the speed of 100 mm/min // 14 {if[next_extruder] == 1} G1 E50 F100 // extrude 50mm at the speed of 100 mm/min //
The amount of cTPU rubber extruded to purge the nozzle from the PLA was decreased, while the amount of extruded PLA was increased. At the end of the process the amount of material wasted was considerable for a structure of 30 layers.
Different 3D structures were printed using the BCN3D Sigma 3D printer with two separated extruder. Conventional PLA and cPLA were used. Figure 3.11 shows the design and prototyping of mesh structures with a grid and a web shape.
Figure 3.11 CAD design (left), slicing (middle) and 3D printed objects (left) for a web (a) and a grid (b) mesh. PLA (red) and cPLA (black).
In this case separation between materials is clear. Gcode modification was unnecessary because the slicing program has the routine to manage the dual extrusion.
