The advent of additive manufacturing has opened up new frontiers in developing metal structures that can have complex geometries, composite structures made of dissimilar metals, and metal structures with embedded sensing and actuation capabilities. These types of structures are possible with ultrasonic additive manufacturing (UAM); a novel manufacturing technology that combines additive manufacturing through the ultrasonic welding of thin metal foils with computer numerical control (CNC) milling. However, the process suffers from a critical limitation, i.e., a range of build heights within which bonding between a foil and the substrate cannot be originated. <br>This work has two research objectives, the first is a fundamental understanding of the complex dynamic interaction between the substrate and ultrasonic horn, or sonotrode. Specifically, it focuses on the effects that specific modes of vibration have on the dynamic response of the substrate. The second objective is to utilize the UAM process to create metal structures with an embedded sensor that can detect contact or impact. In addressing the first objective, a semi-analytical model was developed to determine the response to three forcing descriptions that approximate the interfacial friction between the foil and substrate induced by sonotrode compression and excitation. Several observations can be seen in the results: as the height increases the dominant modes of vibration change, the modes of vibration excited also change during a single weld cycle as the sonotrode travels across the length of the substrate, and finally the three forcing models do not have a significant impact on the substrate response trends with height and during the weld cycle. <br>In addressing the second objective, three prototypes were created by embedding a triboelectric nanogenerator (TENG) sensor within an AL3003 metal-matrix. TENGs utilize contact electrification between surfaces of dissimilar materials, typically polymers, combined with electrostatic induction to generate electrical energy from a mechanical excitation. The sensors demonstrate a discernible response over a 1-5 Hz frequency range. In addition, the sensors have a linear relationship between output voltage and a mechanically applied load, and have the ability to sense contact through both touch and due to an impacting object.

In today’s industry, the sustainable use of raw materials and the development of new green technology in mass production, with the aim of saving resources, energy and production costs, is a significant challenge. Deep drawing as a widely used industrial sheet metal forming process for the production of automotive parts belongs to one of the most energy-efficient production techniques. However, one disadvantage of deep drawing regarding the realisation of green technology is the use of lubricants in this process. Therefore, a novel approach for modifying the conventional deep drawing process to achieve a lubricant-free deep drawing process is introduced within this thesis. In order to decrease the amount of frictional force for a given friction coefficient, the integral of the contact pressure over the contact area has to be reduced. To achieve that, the flange area of the tool is macro-structured, which has only line contacts. To avoid the wrinkling, the geometrical moment of inertia of the sheet should be increased by the alternating bending mechanism of the material in the flange area through immersing the blankholder slightly into the drawing die.

<p>NR 20140805</p>

<p>  </p> <p>Sensing systems have evolved significantly in recent years as a result of several advances in a number of sensor manufacturing approaches. The proximity measuring of approaching objects is a challenging, costly, and critical operation that permits the detection of any impediments without coming into touch with them and causing an unfavorable occurrence. However, developing a flexible proximity sensor capable of operating throughout a wide range of object motion continues to be a difficulty. The current work describes a polymer-based sensor that makes use of a nanostructure composite as the sensing element. The sensor will be used in healthcare and automotive applications in the near future. Composites comprising Thermoplastic Polyurethane (TPU) and Carbon Nanotubes (CNTs) are capable of sensing the presence of an external item at a great distance. The sensor model's performance was then enhanced further by microfabricating an integrated model with a certain shape. The design and production techniques for the TPU/CNTs proximity sensor are basic, and the sensor's performance demonstrates repeatability, as well as high electrical sensitivity and mechanical flexibility. The sensing process is based on the comparison of stored charges at the composite film sensor to the sensor's base voltage. The sensor operates reliably across a detection range of 2-20 cm. Tunneling and fringing effects are used to explain substantial capacitance shifts as sensing mechanisms. The structure's fringing capacitance effect has been thoroughly examined using ANSYS Maxwell (Ansoft) FEA simulation, as the measurements perfectly confirm the simulation's sensitivity trend. A novel mathematical model of fringe capacitance and subsequent tests demonstrate that the distance between an item and the sensor may be determined. Additionally, the model argues that the change in capacitance is significantly influenced by sensor resistivity, with the starting capacitance varying between 0.045pF and 0.024pF in the range 103-105 mm. This analytical model would enable the sensor's sensitivity to be optimized.</p> <p>Additionally, a new generation of durable elastomeric materials is commercially accessible for 3D printing, allowing the development of an entirely new class of materials for wearable and industrial applications. By using functional grading and adjusting to diverse users, the mechanical reaction of soft 3D-printed objects may now be modified for increased safety and comfort. Additionally, electronics may be included into these 3D printed lattice and wearable structures to offer input on the movement of objects associated with healthcare devices as well as automotive components. Thus, in order to investigate the influence of additive manufacturing on the sensitivity of TPU/CNT sensors, samples with equal thickness and size but varied orientations are printed and compared to hot-press samples. Among the many 3D printed patterns, the [0,0] direction has the highest sensitivity, and may be used as an optimum method for increased sensitivity. In contrast to the hot-press samples, the 3D-printed TPU/CNT film features a crystalline network, which may aid in the passage of surface charges and hence increase capacitance changes.</p> <p>To have a better understanding which feature, and parameter can give us the most sensitivity we need to do an optimization. This will be accomplished by collecting experimental and computational results and using them as a basis for establishing a computationally and experimentally supported Genetic Algorithm Assisted Machine Learning (GAML) framework combined with artificial neural network (ANN) to develop TPU/CNT nanocomposite flexible sensors in which material characterizations will be coupled to strain, tactile, electronic and proximity characteristics to probe intermolecular interactions between CNTs and polymers. The proposed framework provides enhanced predictive capabilities by managing multiple sets of data gathered from physical testing (material characterization and sensor testing) and multi-fidelity numerical models spanning all lengths scales. The GAML-ANN framework will allow the concurrent optimization of processing parameters and structural features of TPU/CNT nanocomposites, enabling fabrication of high-performance, lightweight flexible sensor systems.</p> <p>Our suggested nanocomposite sensor establishes a new mainstream platform for ultrasensitive object perception, demonstrating a viable prototype for wearable proximity sensors for motion analysis and the automobile sector.</p>

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Drilling is crucial to many industries, including hydrocarbon extraction, CO2 sequestration, geothermal energy, and others. During penetrating the subsurface rocks, drilling fluid (mud) is used for drilling bit cooling, lubrication, removing rock cuttings, and providing wellbore mechanical stability. Significant mud loss from the wellbore into the surrounding formation causes fluid lost-circulation incidents. This phenomenon leads to cost overrun, environmental pollution, delays project time and causes safety issues. Although lost-circulation exacerbates wellbore conditions, prediction of the characteristics of subsurface formations can be obtained. Generally, four formation types cause lost-circulation: natural fractures, and induced fractures, vugs and caves, and porous/permeable medium. The focus in this work is on naturally fractured formations, which is the most common cause of lost circulation. In this work, a novel prediction tool is developed based on analytical solutions and type-curves (TC). Type-curves are derived from the Cauchy equation of motion and mass conservation for non-Newtonian fluid model, corresponding to Herschel-Bulkley model (HB). Experimental setup from literature mimicking a deformed fracture supports the establishment of the tool. Upscaling the model of a natural fracture at subsurface conditions is implemented into the equations to achieve a group of mud type-curves (MTC) alongside another set of derivative-based mud type-curves (DMTC). The developed approach is verified with numerical simulations. Further, verification is performed with other analytical solutions. This proposed tool serves various functionalities; It predicts the volume loss as a function of time, based on wellbore operating conditions. The time-dependent fluid loss penetration from the wellbore into the surrounding formation can be computed. Additionally, the hydraulic aperture of the fracture in the surrounding formation can be estimated. Due to the non-Newtonian behavior of the drilling mud, the tool can be used to assess the fluid loss stopping time. Validation of the tool is performed by using actual field datasets and published experimental measurements. Machine-Learning is finally investigated as a complementary approach to determine the flow behavior of mud loss and the corresponding fracture properties.

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