Many cases have evinced the importance of having structural health monitoring (SHM) strategies that can allow the detection of the structural health of infrastructures or buildings, in order to prevent the potential economic or human losses. Nanocomposite material like the Carbon nanofiller-modified composites have great potential for SHM because these materials are piezoresistive. So, it is possible to determine the damage status of the material by studying the conductivity change distribution, and this is essential for detecting the damage on the position that can-not be observed by eye, for example, the inner layer in the aerofoil. By now, many researchers have studied how damage influences the conductivity of nanocomposite material and the electrical impedance tomography (EIT) method has been applied widely to detect the damage-induced conductivity changes. However, only knowing how to calculate the conductivity change from damage is not enough to SHM, it is more valuable to SHM to know how to determine the mechanical damage that results in the observed conductivity changes. In this article, we apply the machine learning methods to determine the damage status, more specifically, the number, radius and the center position of broken holes on the material specimens by studying the conductivity change data generated by the EIT method. Our results demonstrate that the machine learning methods can accurately and efficiently detect the damage on material specimens by analysing the conductivity change data, this conclusion is important to the field of the SHM and will speed up the damage detection process for industries like the aviation industry and mechanical engineering.

Proportional valves are widely used in fluid systems for controlling the volume flow rate or fluid pressure. The actuation of this valves is done by PWM-driven proportional solenoids, which enable self-sensing position determination abilities due to air gap-dependent electrical behaviour, e. g. for condition monitoring or position controlling tasks. However, the sensor properties of conventional proportional solenoids are poor due to ambiguities caused by hysteresis effects (magnetic hysteresis, eddy currents) and saturation effects. Thus, a sensor-designed actuator was developed with very low hysteresis effects and unique position determination by using electrical sheet and a particular air gap design. This paper deals with investigations of a novel self-sensing position determination approach on a demonstrator of the sensor-designed solenoid. The advantage of this method is an online consideration of transient effects such as mean current change and armature motion as well as temperature-dependent resistance. For this, a combined evaluation of the differential inductance and flux linkage during PWM periods is proposed.

<div>Self-sensing materials are engineered to transduce mechanical effects like deformations and damages into detectable electrical changes. As such, they have received immense research attention in areas including aerospace, civil infrastructure, robotic skin, and biomedical devices. In structural health monitoring (SHM) and nondestructive evaluation (NDE) applications, damages in the material cause breakage in the conductive filler networks, resulting in changes in the material's conductivity. Most SHM and NDE applications of self-sensing materials have used direct current (DC) measurements. DC-based methods have shown advantages with regard to sensitivity to microscale damages compared to other SHM methods. Comparatively, alternating current (AC) measurement techniques have shown potential for improvement over existent DC methods. For example, using AC in conjunction with self-sensing materials has potential for benefits such as greater data density, higher sensitivity through electrodynamics effects (e.g., coupling the material with resonant circuitry), and lower power requirements. Despite these potential advantages, AC techniques have been vastly understudied compared to DC techniques. </div><div><br></div><div>To overcome this gap in the state of the art, this thesis presents two contributions: First, an experimental study is conducted to elucidate the effect of different damage types, numbers, and sizes on AC transport in a representative self-sensing composite. And second, experimental data is used to inform a computational study on using AC methods to improve damage detection via electrical impedance tomography (EIT) – a conductivity imaging modality commonly paired with self-sensing materials for damage localization. For the first contribution, uniaxial glass fiber specimens containing 0.75 wt.% of carbon nanofiber (CNF) are induced with five types of damage (varying the number of holes, size of holes, number of notches, size of notches, and number of impacts). Impedance magnitude and phase angle were measured after each permutation of damage to study the effect of the new damage on AC transport. It was observed that permutations of hole and notch damages show clear trends of increasing impedance magnitude with the increasing damage, particularly at low frequencies. These damages had little-to-no effect on phase angle, however. Increasing numbers of impacts on the specimens did not show any discernable trend in either impedance magnitude or phase angle, except at high frequencies. This shows that different AC frequencies can be more or less useful for finding particular damage types.</div><div><br></div><div>Regarding the second contribution, AC methods were also explored to improve damage detection in self-sensing materials via EIT. More specifically, the EIT technique could benefit from developing a baseline-free (i.e., not requiring a ‘healthy’ reference) formulations enabled by frequency-difference (fd) imaging. For this, AC conductivity measurements ranging from 100 Hz to 10 MHz were collected from various weight fractions of CNF-modified glass fiber/epoxy laminates. This experimental data was used to inform fdEIT simulations. In the fdEIT simulations, damage was simulated as a simple through-hole. Simulations used 16 electrodes with four equally spaced electrodes on each side of the domain. The EIT forward problem was used to predict voltage-current response on the damaged mesh, and a fdEIT inverse problem was formulated to reconstructs the damage state on an undamaged mesh. The reconstruction images showed the simulated damage clearly. Based on this preliminary study, this research shows that fdEIT does have potential to eliminate the need for a healthy baseline in NDE applications, which can potentially help proliferate the use of this technique in practice.</div>

Les paliers magnétiques actifs auto-détecteurs sont des transducteurs électromécaniques permettant de réaliser deux fonctions simultanées: une fonction puissance assurant le positionnement du rotor et une fonction capteur de position permettant d'estimer la taille de l'entrefer et la position du rotor. Dans cette thèse nous nous sommes intéressés au développement de modèles électromagnétiques de paliers magnétiques actifs auto-détecteurs pour leur conception et leur optimisation. Différents modèles ont ainsi été produits durant ces travaux de thèse:- Un modèle haute fréquence d'un matériau feuilleté et polarisé construit à partir d'une mesure de la perméabilité réversible et d'un modèle formel de diffusion haute fréquence;- Un modèle de simulation des courants induits dans un rotor feuilleté générés par la rotation de celui-ci. Ce modèle permet alors de déterminer les pertes par courants induits ainsi que l'effet de la rotation sur l'estimation de la position;- Enfin, un modèle de l'impédance haute fréquence d'un circuit magnétique feuilleté, saturable et polarisé. Cette modélisation se réalise en 3 étapes : calcul magnétostatique non linéaire, intégration du modèle haute fréquence et calcul magnétodynamique linéaire. Ces différentes approches de modélisation ont été validées expérimentalement au moyen d'un cadre d'Epstein et sur deux types de paliers magnétiques. L'ensemble de ces modèles ont ensuite été exploités dans un outil pour la conception optimale de paliers magnétiques actifs auto-détecteur. Finalement, une optimisation multivariable bi-objective a été réalisée de façon à concevoir un palier magnétique actif auto-détecteur optimal à la fois d'un point de vue capteur et d'un point de vue puissance. / Active magnetic bearings with a self-sensing approach are electromechanical transducers. They realize two different functions at the same time: power function for moving the rotor, and sensor function for estimating the size of the air gap and the position of rotor. In this thesis, we developed an electromagnetic model for conception and optimization of these active magnetic bearing. In order to achieve this goal, different models were developed during this thesis:- A high frequency model of a laminated and polarized material builds with a reversible permeability measure and an homogenization model for high frequency;- A simulation model induced currents in a laminated rotor created by its own rotation. This one permits eddy current losses determination and the impact of rotation on the position estimation;- Finally, a high frequency impedance model for a laminated, saturated and polarized magnetic circuit. This model computes in 3 steps: nonlinear magnetostatic calculation, integration of high frequency model and linear magnetodynamic calculation. These different approaches were validated experimentally with an Epstein frame and with two different magnetic bearings. All these models were then exploited in a tool for optimal conception of active magnetic bearings with a self-sensing approach. Finally, a bi-objective multivariable optimization was realized to design an optimal active magnetic bearing to both estimate position and create force.

A self-sensing material can not only carry a load but can also provide data aboutthe load and stress it’s being subjected to. Traditional additive manufacturing haslimited capabilities in producing self-sensing material. Existing 3D printers eitherused in industry or in scientific applications are either limited by closed-off software and planar motion which limits the design freedom, or the type of material orcost often limiting the attainability. Being capable of placing self-sensing materialwith full design freedom means that the sensor structure as well as the load carryingpart of the material can be tailored to the application specific use of the material,making application specific load carrying and sensing capabilities possible. Themanufacturing method produced in this aims to solve these existing limitations. Aliterature review in the topic of additive manufacturing of self-sensing material andcontinuous Carbon Fiber Reinforced Thermoplastics (CFRTPs) has been producedas a literature base. The review seeks to educate and inspire the design of an noveladditive manufacturing method and device capable of printing a self-sensing material as well as non-planar motion. A design for extruding self-sensing material andnon-planar motion has been realized through modified Commercial-Off-The-Shelf(COTS) parts and Geometric Code (G-Code). Existing hardware capable of producing this can be priced in the range of 70 000 C, but this result has been achievedwith around 200 C [42]. A software structure capable of manufacturing the selfsensing material has been produced. Real-world testing in terms of extrusion of theself-sensing material and non-planar motion has been tested and proven which arethe main practical outcomes demonstrating the technological feasibility.

<p dir="ltr">The demand and utilization of fiber-reinforced composites are increasing in various sectors, including aerospace, civil engineering, and automotive industries. Non-destructive methods are necessary for monitoring fiber-reinforced composites due to their complex and often visually undetectable failure modes. An emerging method for monitoring composite structures is through the integration of self-sensing capabilities. Self-sensing in nanocomposites can be achieved through nanofiller modifications, which involve introducing an adequate amount of nanofillers into the matrix, such as carbon nanotubes (CNTs) and carbon nanofillers (CNFs). These fillers form an electrically well-connected network that allows the electrical current to travel through conductive pathways. The disruption of connectivity of these pathways, caused by mechanical deformations or damages, results in a change in the overall conductivity of the material, thereby enabling intrinsic self-sensing.</p><p dir="ltr">Currently, the majority of predictive modeling attempts in the field of self-sensing nanocomposites have been dedicated to microscale piezoresistivity. There has been a lack of research conducted on the modeling of strain-induced resistivity changes in macroscale fiber-matrix material systems. As a matter of fact, no analytical macroscale model that addresses the impact of continuous fiber reinforcement in nanocomposites has been presented in the literature. This gap is significant because it is impossible to make meaningful structural condition predictions without models relating observed resistivity changes to the mechanical condition of the composite. Accordingly, this dissertation presents a set of three research contributions. The overall objective of these contributions is to address this knowledge gap by developing and validating an analytical model. In addition to advancing our theoretical understanding, this model provides a practical methodology for predicting the piezoresistive properties of continuous fiber-reinforced composites with integrated nanofillers.</p><p dir="ltr">To bridge the above-mentioned research gap, three scholarly contributions are presented in this dissertation. The first contribution proposes an analytical model that aims to predict the variations in resistivity within a material system comprising a nanofiller-modified polymer and continuous fiber reinforcement, specifically in response to axial strain. The fundamental principle underlying our methodology involves the novel use of the concentric cylindrical assembly (CCA) homogenization technique to model piezoresistivity. The initial step involves the establishment of a domain consisting of concentric cylinders that represent a continuous reinforcing fiber phase wrapped around by a nanofiller-modified matrix phase. Subsequently, the system undergoes homogenization to facilitate the prediction of changes in the axial and transverse resistivity of the concentric cylinder as a consequence of longitudinal deformations. The second contribution investigates the effect of radial deformations on piezoresistivity. Here, we demonstrate yet another novel application of the CCA homogenization technique to determine piezoresistivity. This contribution concludes by presenting closed-form analytical relations that describe changes in axial and transverse resistivity as functions of externally applied radial strain. The third contribution involves computationally analyzing piezoresistivity in fiber-reinforced laminae by using three-dimensional representative volume elements (RVE) with a CNF/epoxy matrix. By comparing the single-fiber-based analytical model with the computational model, we can investigate the impact of interactions between multiple adjacent fibers on the piezoresistive properties of the material. The study revealed that the differences between the single-fiber CCA analytical model and the computational model are quite small, particularly for composites with low- to moderate-fiber volume fractions that undergo relatively minor deformations. This means that the analytical methods herein derived can be used to make accurate predictions without resorting to much more laborious computational methods.</p><p dir="ltr">In summary, the impact of this dissertation work lies in the development of novel analytical closed-form nonlinear piezoresistive relations. These relations relate the electrical conductivity/resistivity changes induced by axial or lateral mechanical deformations in directions parallel and perpendicular to the reinforcing continuous fibers within fiber-reinforced nanocomposites and are validated against in-depth computational analyses. Therefore, these models provide an important and first-ever bridge between simply observing electrical changes in a self-sensing fiber-reinforced composite and relating such observations to the mechanical state of the material.</p>

This dissertation focuses on the development of and applications for Nano-Composite Piezoresponsive Foam (NCPF). This self-sensing foam sensor technology was discovered through research in a sister technology, High Deflection Strain Gauges (HDSG), and was subsequently developed with some of the same base materials. Both technologies use nano and micro conductive additives to provide electrically responsive properties to materials which otherwise are insulative. NCPF sensors differ from HDSGs in that they provide a dual electrical response to dynamic and static loading, which is measured through an internally generated charge, or a change in resistance. This dissertation focuses on the development of the dynamic or piezoresponsive aspect of the NCPF sensors which tends to have more consistent electrical response over a larger number of cycles. The primary development goal was to produce a sensor that was accurate, while providing a consistent, repeatable response over multiple impacts. The hypothesized electric generation is attributed to a triboelectric interaction between the conductive additives and the polyurethane foam matrix. This hypothesis was validated by examining different conductive additives with varying loading levels and specific surface areas while accounting for other design considerations such as the electrode used to harvest the response. The results of this analysis support the triboelectric model and point to carbon or nickel-based additives for optimal performance. The NCPF response measured by digital signal acquisition devices is directly dependent upon its input impedance. Increased input capacitance has a negative effect on the signal, however, higher input resistance has a positive linear correlation to voltage. Other considerations that affect the electrical response include the temperature and humidity in which the sensor is used and result in a scaled electrical response.NCPF sensors are ideally suited for use in systems which benefit from impact energy attenuation while measuring the same. This work demonstrates how the NCPF sensors can be used to detect severity and location of impacts in systems with multiple sensors (football helmets), and those with one continuous sensor (carpets). When NCPF sensors were used in a football helmet the impact severity and location of impact was accurately identified. NCPF sensors provide the benefit of simplified design by replacing existing foam while providing a direct measure of the forces. Additional research was conducted on the changes in material properties, specifically how it affects the foam structures ability to absorb energy in quasi static loading scenarios. NCPF sensors are demonstrated as viable tool to measure many different biomechanical systems.

The interest in breaking the historical dependence on fossil energy and begin moving towards more renewable energy sources is rising worldwide. This is largely due to uncertainties in the future supply of fossil fuels and the rising concerns about humanity’s role in the currently ongoing climate changes. One renewable energy source is ocean waves and Uppsala University has since the early 2000s been performing active research in this area. The Uppsala wave energy concept is centered on developing linear generators coupled to point absorbing buoys, with the generator situated on the seabed and connected to the buoy on the sea surface via a steel wire. The motion of the buoy then transfers energy to the generator, where it is converted into electricity and sent to shore for delivery into the electrical grid. This thesis will mainly focus on the development and evaluation of technologies used to automate the manufacturing of the translator, a central part of the linear generator, using industrial robotics. The translator is a 3 m high and 0.8 m wide three sided structure with an aluminum pipe at its center. The structure consists of alternating layers of steel plates (pole-shoes) and ferrite magnets, with a total of 72 layers per side. To perform experiments on translator assembly and production, a robot cell (centered on an IRB6650S industrial robot) complimented with relevant tools, equipment and security measures, has been designed and constructed. The mounting of the pole-shoes on the central pipe, using the industrial robot, proved to be the most challenging task to solve. However, by implementing a precise work-piece orientation calibration system, combined with selective compliance robot tools, the task could be performed with mounting speeds of up to 50 mm/s. Although progress has been made, much work still remains before fully automated translator assembly is a reality. A secondary topic of this thesis is the development of stand-alone measurement systems to be used in the linear generator, once it has been deployed on the seabed. The main requirements of such a measurement system is robustness, resistance to electrical noise, and power efficiency. If possible the system should also be portable and easy to use. This was solved by developing a custom measurement circuit, based on industry standard 4–20 mA current signals, combined with a portable submersible logging unit. The latest iteration of the system is small enough to be deployed and retrieved by one person, and can collect data for 10 weeks before running out of batteries. Future work in this area should focus on increasing the usability of the system. The third and final topic of this thesis is a short discussion of an engineering approach to kinetic energy storage, in the form of high-speed composite flywheels, and the design of two different prototypes of such flywheels. Both designs gave important insights to the research group, but a few crucial design faults unfortunately made it impossible to evaluate the full potential of the two designs.

Production of construction materials requires a large amount of energy. That can be decreased by using of waste materials. This thesis deals with the self-sensing properties of composites. It presents electrical properties of building materials and their measurement. In the experimental part, the influence of conductive fillers on the self-sensing properties of aluminosilicate composites was examined. Test cubes were made of alkali-activated slag with a content of graphite powder, carbon black, carbon fibers, steel fibers or carbon nanotubes. The fractional change in resistence during cyclic compressive loading was monitored.

Animals exploit soft structures to move smoothly and effectively in complex natural environments. These capabilities have inspired robotic engineers to incorporate soft actuating technologies into their designs. Developing soft muscle-like actuation technology is one of the grand challenges in the creation of soft-body robots that can move, deform their body, and modulate body stiffness. This paper presents the development of smart pneumatic McKibben muscles woven and reinforced by using conductive insulated wires to equip the muscles with an inherent sensing capability, in which the deformation of the muscles can be effectively measured by calculating the change of wire inductance. Sensing performance of a variety of weaving angles is investigated. The ideal McKibben muscle models are used for analysing muscle performance and sensing accuracy. The experimental results show that the contraction of the muscles is proportional to the measured change of inductance. This relationship is applied to a PID control system to control the contraction of smart muscles in simulation, and good control performance is achieved. The creation of smart muscles with an inherent sensing capability and a good controllability is promising for operation of future soft robots.