Design, Development and Optimization of A Flexible Nanocomposite Proximity Sensor
Creators: name: Moheimani, Reza
Publisher: name: Purdue University Graduate School
Year: 2022
Subjects: Mechanical engineering not elsewhere classified

description: 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. 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. 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. 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.