Having a multitude of electromechanical and robotic applications, most flexible elastomer actuators require electrically conductive electrode components as well as nonconductive substrate components to operate. Various analytical models of flexible actuators typically show that several properties of the elastomer, such as thickness, permittivity, and softness, directly influence the actuation capability. As such, the optimization of flexible actuators, particularly in dielectric elastomer actuator (DEA), has focused on improving the elastomer while electrodes are often overlooked. However, the electrodes with high modulus of elasticity, thickness, and low stretchability can reduce the amount of actuator performance. In addition, inadequate electrical conductivity increases the actuator’s power requirement and influences the viscoelastic properties of DEA materials through resistive heating. Furthermore, besides material composition of electrode, manufacturing methods also govern the actuator performances. Therefore, a thorough investigation of both electrode properties and manufacturing methods is crucial to attain high-performance DEAs. In this work, a microdispensing additive manufacturing technique was used to produce high-quality electrodes and to fabricate test coupons composed of PEDOT:PSS (conductive and transparent polymer) and Triton X-100 (surfactant plasticizer). These coupons, as well as some molded coupons, were used to investigate important mechanical, electrical, and thermal properties of DEAs. Through the testing, the electrode showed satisfactory stretchability up to 55% for a PDMS-supported sample. Although Young’s modulus of PEDOT:PSS was decreased largely by adding Triton X-100, the value was still relatively high (8.3 MPa) that needed to be lowered more to be effectively used for DEA application. The electrode maintained its conductivity above 50 S/cm when tested in the deformed state (up to 50% of strain) or at different temperatures (25-55 °C). Finally, the applicability of electrode composition was verified by electromechanical tests performed on a fully printed single layer DEA with 20 microns thick electrodes.
Surface acoustic wave (SAW) sensors offer overwhelming advantages over other competitive sensing technologies due to its small size, cost-effectiveness, fast response time, passive and wireless capabilities. Development of SAW sensors allows investigation of their potential not only for measuring less-time dependent parameters, such as pressure and temperature, but also dynamic parameters like mechanical strains. The concept behind this work is to develop a passive flexible SAW sensor with optimized materials selection that can be used in harsh environments to measure mechanical strains occurring in aerospace applications. A flat 0-3 composite thin substrate is fabricated using a hot-press, an interdigital transducer (IDT) finger deposition is made through additive manufacturing. The sensor substrate comprises polyvinylidene fluoride as a polymer matrix, lead zirconate titanate powders as well as carbon nanotubes as nanoparticle fillers, exhibiting favorable flexibility and piezoelectric properties. The electromechanical property is enhanced using a non-contact corona poling technique with high electric field. IDT fingers are printed using direct printing additive manufacturing technique of conductive paste. Design parameters of SAW IDTs are optimized using a second-order transmission matrix approach. Rayleigh waves, generated on the fabricated substrate by an RF excitation signal, travel through the substrate and can provide useful information for desired parameters. In this work the sensing mechanism is based on the radio frequency scattering parameters response of the device. Results show a correlation between the amplitude and phase frequency response of the scattering parameters, and the mechanical strain. Experimental study on SAW substrate fabrication and analysis of sensed results with phase shift in wave speed due to strains are discussed.
Surface acoustic wave (SAW) sensor has increasing demand in structural health monitoring due to its passive, reliable life-cycle, high accuracy, and small size. The ongoing demands of sensor’s adaptability with flexible substrates, which is capable of wireless monitoring, is the basis of the research. The SAW device assembly includes a piezoelectric composite substrate that aids in wave transmission and two interdigital transducers (IDTs) capable of actuating and sensing radio frequency (RF) signals. The SAW substrate is fabricated by integrating lead zirconate titanate ceramic nanoparticles into polyvinylidene fluoride polymer matrix using dimethyl sulfoxide as the solvent. Hot-pressing the mixtures produces a thin 0-3 composite substrate that exhibits flexibility and optimum dielectric properties. The substrate material properties are studied by conducting FTIR scanning. Delay-line IDTs are incorporated on the surface of the substrate by a conventional photolithographic technique. With the sensor fabricated, RF signals are passed onto the device through the input transducer generating Rayleigh waves. The transmission and reflection characteristics of the device is determined through the S-parameter reading obtained using a network analyzer. This paper discusses about the development process of a flexible piezocomposite SAW sensor.
Dielectric elastomer actuator (DEA) is one of the most promising group of electroactive polymers (EAP) that can find its applications in scientific, medical, industrial and other fields. By geometrically modifying DEA structure, helical dielectric elastomer actuator (HDEA) possesses several advantages due to the continuities of its electrodes and elastomers. The actuator is well known for its competitive electro-mechanical properties and capability to deform significantly in the first place. However, some applications of EAP require relatively high actuation force along with moderate to high deformation capability for a minimum voltage applied. For this purpose, an optimization on the actuator is carried out to maximize the actuation force while keeping the deformation capability on an appropriate level for a particular application. Numerical simulation on a single HDEA actuator is performed to validate the analytical model used in the optimization and to evaluate the performance of the actuator. In both analytical and numerical analyzes, elastomer and electrode layers of HDEA are modeled using a hyperelastic model with the material suitable for 3D printing manufacturing technology. The results of the simulation and analytical solution are compared and discussed. The necessary changes to the hyperelastic model are discussed. In addition, an adaptive soft active composite (SAC) trailing edge of a wing is chosen as a target application in the optimization procedure. Thus, actuator parameters are optimized not only for the single actuator, but also for the adaptive SAC trailing edge with its own dimensional constraints, certain actuation force, deformation, and voltage requirements. The obtained designs will be used in further studies on HDEA-based SAC adaptive structures.
Folding sheet materials into cylindrical structures using an origami-based approach allows the sheet materials to be densely packed within a confined space that can be deployed when needed. Kresling pattern, which is a cylindrical origami pattern consisting of identical triangular panels with cyclic symmetry, functions under the spontaneous buckling of a thin cylindrical shell under torsional loading. The incorporation of smart materials, such as electroactive polymers, in origami structures can allow them to actively fold using electrical stimuli. In this study, finite element analysis (FEA) is performed in a single cell of Kresling pattern as well as the continuous Kresling pattern-based origami structure. Furthermore, different placements of dielectric elastomer actuators (DEAs) implemented within the origami structure are studied to identify the performance. The objective of this study is to validate the effectiveness of DEAs as a method to actively fold the origami structure, to deform and return to its initial state, and to investigate the geometric parameters on the folding structure incorporated with DEAs. Equivalent mechanical pressure and stress are used as loads in the FEA to simulate the electric actuation performed by the DEAs. By thorough FEA investigation, the impact of geometric parameters, material properties, and placement of DEAs on the origami structure for optimal performance is studied to avoid trial and error iterations for experimental studies.
Advances in soft robotic systems enable to create devices that can elegantly deal with complex environments and gently interface with humans. However, much progress in actuator technologies is required for adoption in practical and commercial scale-up implementations. An helical dielectric elastomer actuator (HDEA) can be a promising solution that fits in these applications. Nevertheless, in order to move forward from theory to practice, many aspects still need to be developed and advanced. For instance, current works may be insufficient to advance the topics in control systems applied to actuator geometry, in relation to relevant segments such as material synthesis and design for manufacturing. It is apparent that absence of a more complete and generalized dynamics model of an HDEA limits rapid engineering progress in this field. In some previous research, important contributions of electromechanical model were proposed for linear and nonlinear hyperelastic materials. However, other effects such as viscoelasticity and hysteresis in the strain-voltage relation were often neglected. This paper presents the dynamical model derivation of an HDEA using lumped parameters to model the electrical and mechanical behavior of the actuator. Furthermore, it covers the most imperative effects embedded in the dynamics of the actuator. In this work, the dielectric elastomeric transducer is modeled with VHB 4910 acrylic due to its well-documented material parameters needed in the non-linear strain energy functions.
Fabrication of dielectric elastomer actuator (DEA) using additive manufacturing techniques can provide an alternative solution for current manufacturing processes of DEAs that are generally inconsistent and time consuming. In addition, additive manufacturing can allow DEAs with complex geometric configurations to be realized. This study investigates analytical approaches to optimize the performance of helical dielectric elastomer actuator (HDEA) based on additive manufacturing technologies. Optimized geometric configurations tailored to additive manufacturing and proper material selection for elastomer and electrode can improve the overall performance of HDEA. Due to the absence of pre-stretch in the elastomer membranes with additive manufacturing, associated drawbacks, such as electromechanical instability, high external voltage requirement, and their alternate solutions are analyzed and discussed. The performance of HDEA are evaluated by displacement, block force, and weight-to-force ratio by varying multiple geometric parameters including membrane thickness, pitch angle, inner-toouter electrode ratio, and actuation voltage. Since the selection of materials is as important as the geometric parameters of the actuator, printable elastomer and electrode materials with dielectric and mechanical properties for HDEA are evaluated. By optimizing geometric parameters and selecting appropriate materials based on its properties, appropriate manufacturing techniques are discussed to print both dielectric elastomer and electrode layers.
Nanocomposites exhibit remarkable electromechanical properties and have potential applications in sensing and actuation. In this work, carbon nanotubes (CNTs) - epoxy nanocomposites are fabricated with the addition of graphite nanoplatelets (GNPs). An improvement in piezoresistivity is observed with the combination of CNTs and GNPs, compared to the use of only CNTs, which indicates the formation of an efficient hybrid conductive networks for strain and electrical transfer in the materials. We investigate the effect of static mechanical loading on the electrical sensing performance of the nanocomposites. The inter-particle distances between the fillers change in the event of applied loading, which leads to a modification of the CNT-GNP hybrid percolated network and hence results in a change of the electrical conductivity. This phenomenon is exploited to use the hybrid composites as strain sensors. Specifically, different matrix materials are tested to investigate their effects on the mechanical and sensing performance of the nanocomposites. In addition, numerical simulations are performed to model the strain sensing performance of the nanocomposites. The effect of the type of matrix on the sensing performance of the nanocomposites is predicted and compared with the experimental results.
Inflatable structures for space habitat are highly prone to damage caused by micrometeoroid and orbital debris impacts. Although the structures are effectively shielded against these impacts through multiple layers of impact resistant materials, there is a necessity for a health monitoring system to monitor the structural integrity and damage state within the structures. Assessment of damage is critical for the safety of personnel in the space habitat, as well as predicting the repair needs and the remaining useful life of the habitat. In this paper, we propose a unique impact detection and health monitoring system based on hybrid nanocomposite sensors. The sensors are composed of two fillers, carbon nanotubes and coarse graphene platelets with an epoxy matrix material. The electrical conductivity of these flexible nanocomposite sensors is highly sensitive to strains as well as presence of any holes and damage in the structure. The sensitivity of the sensors to the presence of 3mm holes due to an event of impact is evaluated using four point probe electrical resistivity measurements. An array of these sensors when sandwiched between soft good layers in a space habitat can act as a damage detection layer for inflatable structures. An algorithm is developed to determine the event of impact, its severity and location on the sensing layer for active health monitoring.
Dielectric elastomer actuators (DEA) are known for its capability of experiencing extreme strains, as it can expand and contract based on specific actuation voltage applied. On contrary, helical DEA (HDEA) with its unique configuration does not only provide the contractile and extendable capabilities, but also can aid in attaining results for bending and torsion. The concept of HDEA embraces many new techniques and can be applied in multiple disciplines. Thus, this paper focuses on the simulation of HDEA with helical compliant electrodes that is a major factor prior to its application. The attributes of the material used to build the structure plays a vital role in the behavior of the system. For numerical analysis of HDEA, the material characteristics are input into a commercial grade software, and then the appropriate analysis is performed to retrieve its outcome. Applying the material characteristics into numerical analysis modeling, the functionality of HDEA for various activations can be achieved, which is used to test and comply with the fabricated final product.
Hybrid nanocomposites with carbon nanotubes and graphitic platelets as fillers are known to exhibit remarkable electrical and mechanical properties with many potential strain and damage sensing applications. In this work, we fabricate hybrid nanocomposites with carbon nanotube sheet and coarse graphite platelets as fillers with epoxy matrix. We then examine the electromechanical behavior of these nanocomposites under dynamic loading. The electrical resistivity responses of the nanocomposites are measured in frequency range of 1 Hz to 50 Hz with different levels of induced strains. Axial cycling loading is applied using a uniaxial electrodynamic shaker, and transverse loading is applied on end-clamped specimen using modified speakers. In addition, a dynamic mechanical analysis of nanocomposite specimen is performed to characterize the thermal and dynamic behavior of the nanocomposite. Our results indicate that these hybrid nanocomposites exhibit a distinct piezoresistive response under a wide range of dynamic loading conditions, which can be beneficial for potential sensing applications.
A solid mechanical spring generally exhibits uniform stiffness. This paper studies a mechanical spring filled with
magnetorheological (MR) fluid to achieve controllable stiffness. The hollow spring filled with MR fluid is subjected to a
controlled magnetic field in order to change the viscosity of the MR fluid and thereby to change the overall stiffness of the
spring. MR fluid is considered as a Bingham viscoplastic linear material in the mathematical model. The goal of this
research is to study the feasibility of such spring system by analytically computing the effects of MR fluid on overall
spring stiffness. For this purpose, spring mechanics and MR fluid behavior are studied to increase the accuracy of the
analysis. Numerical simulations are also performed to generate some assumptions, which simplify calculations in the
analytical part. The accuracy of the present approach is validated by comparing the analytical results to previously known
experimental results. Overall stiffness variations of the spring are also discussed for different spring designs.
This study takes the conceptual idea of the helical dielectric elastomer actuator (HDEA) and explores mathematical concepts for the actuator capabilities to provide a reasonable function in an industrial role. In the past, other researchers have demonstrated the contractile capabilities of the HDEA through experimental work and analytical modeling. The researchers in those work fabricated helical dielectric elastomer actuators with helical compliant electrodes interposed in an elastomeric insulator. Although an analytical electromechanical model was described, it was applicable only for relatively small strains and the elastomer was designed as a linearly elastic body. It is desirable to consider large strains for better prediction of the performance of the actuator. We begin our analysis by considering the 3D actuator as a purely hyperelastic model with no viscoelastic effects. The constructed mathematical concepts show the displacement, model capabilities, and also overall functionality of the HDEA. The electromechanical coupling of the actuator is modeled to produce a history of the actuator’s strain response for specific activation voltages. The performance of the HDEA is also numerically compared using a commercial grade software.
The conventional feel system in aircraft occupies large space in the cockpit and has complicated designs. The primary objective of this research is to develop an artificial feel force system that can overcome some drawbacks of the current system. A novel feel system using magneto-rheological (MR) fluid is constructed to precisely control the shear stress under the magnetic field. To validate the functionality of the MR artificial feel system, the final system is fabricated and multiple tests are performed to acquire force-velocity characteristics that are compared to the mathematical model derived. In addition, the PID closed loop control algorithm is developed to simulate the dynamic system model. Both experimental and simulation results are compared to validate the derived system model. The system response time and sampling rates are evaluated and compared to the conventional system at the end. It is concluded that the developed artificial feel system can precisely control and acts as a fail proof system when incorporated with a modern fly-by-wire aircraft system.
Smart materials offer several potential advantages for UAV flight control applications compared to traditional servo actuators. One important benefit is that smart materials are lightweight and can be embedded directly into the structure of a wing or control surface. Therefore, they can reduce the overall weight of the vehicle and eliminate the need for mechanical appendages that may compromise the form factor of the wing, benefits that become more significant as the size of the vehicle decreases. In addition, smart materials can be used to realize continuous camber change of aerodynamic surfaces. Such designs offer improved aerodynamic efficiency compared to the discontinuous deflections of traditional hinged control surfaces driven by servo actuators. In the research discussed in this paper, macro-fiber composite (MFC) aileron actuators are designed for implementation on a medium-scale, fixed-wing UAV in order to achieve roll control. Macro-fiber composites, which consist of piezoceramic fibers and electrodes embedded in an epoxy matrix, are an attractive choice for UAV actuation because they are manufactured as lightweight, thin sheets and, when implemented as bending actuators, can provide both large structural deflections and high bandwidth. In this study, several MFC aileron actuator designs were evaluated through a combination of theoretical and experimental analysis. The current design consists of glass fiber composite ailerons with two unimorph MFC actuators embedded in each aileron to produce upward deflection. Wind tunnel test results are presented to assess the changes in lift and drag coefficients for different levels of MFC aileron actuation. Preparations for open-loop flight testing using a Skywalker UAV with MFC ailerons are also discussed. In addition, the development of a closed-loop, autonomous flight control system for the Skywalker is overviewed in preparation for conducting simulations and flight testing of an autonomous Skywalker with MFC aileron actuators.
With smart materials and adaptive structures being nudged into mainstream technology progressively, the smart composites are donning a predominant role as indispensable structures. Among these, the Ionic Polymer Metal Composites (IPMC), with their large bending deformation and relaxation characteristics at very low voltages are attractive as transducers in many areas of application. The actuation and sensing properties of IPMC have been sought after for various engineering functions. The paper focuses on combining the ionic polymer with multi-walled carbon nanotube Bucky paper electrodes to create an enhanced IPMC, and comparatively analyzes the different methodologies briefly discussing the electrode morphology and also compares the uniformity of the electrode plating obtained from the different processes. This paper also concentrates on making use of different ionic solutions for comparison such as to determine the most suited ion content within the solid electrolyte for effective IPMC actuation. This new functionally graded material is tested for its bending deformation, blocking force and the current consumption to prove the electromechanical efficiency of the Bucky paper IPMC. By studying the electromechanical properties of this smart composite actuator based on its actuation under different electric excitations, we can draw conclusions subsequently from the results of the comparison.
In metallic structures, the first and second most frequent damages incurred are generally cracks and corrosions. Correct
damage classification for these two damages is important since their phases can be developed with dissimilar patterns.
In this research, damage classification using the Adaboost machine learning algorithm is investigated. To accomplish
this, the physical differences of the two types of damages are defined and the most appropriate excitation signal is also
determined. Various time-frequency methods are examined with the sensed damage signals to obtain a suitable signal
processing method for damage classification. Among the methods examined, the spectrogram is chosen since it provides
reliable results for these types of damages. With these results, the damage classification is performed through the
Adaboost machine learning algorithm. The training samples for the algorithm are obtained from a finite element tool
and experiments are also performed to get the testing samples. The analysis results show that correct damage
classification is feasible using time-frequency representations and the Adaboost machine learning algorithm.