| The global market researches showed that there is a growing trend in the field of polymer optical fiber (POF) and POF sensors. Telecommunications, medicine, defense, aerospace, and automotive are the application areas of fiber optic sensors, where the automotive industry is the most promising application area for innovations in the field of POF sensors. The POF sensors in automobiles are particularly for detection of seat occupancy, and intelligent pedestrian protection systems.;This dissertation investigates graded index perfluorinated polymer optical fiber as an intensity modulated intrinsic sensor for application in automotive seat occupancy sensing. Since a fiber optic sensor has a high bandwidth, is small in size, is lightweight, and is immune to electromagnetic interference (EMI) it offers higher performance than that of its electrical based counterparts such as strain gauge, elastomeric bladder, and resistive sensor systems. This makes the fiber optic sensor a potential suitable material for seat occupancy sensing.;A textile-based fiber optic sensor was designed to be located in the area beneath the typical seated human's thighs. The pressure interval under which the proposed POF sensor design could perform well was found to be between 0.18 and 0.21 N/cm2, where perfluorinated (PF) graded index (GI) POF (62.5/750 mum) was used as the POF material. In addition, the effect of the automotive seat covering including face material (fabric) and foam backing to the sensor's performance was analyzed. The face fabric structure and the thickness of foam backing were not found to be significant factors to change the sensor results.;A research study, survey, was conducted of which purpose was to better understand market demands in terms of sensor performance characteristics for automotive seat weight sensors, as a part of the Quality Function Deployment (QFD) House of Quality analysis. The companies joined the survey agreed on the first 5 most important sensor characteristics: reproducibility, accuracy, selectivity, aging, and resolution.;Artificial neural network (ANN), a mathematical model formed by mimicking the human nervous system, was used to predict the sensor response. Qwiknet (version 2.23) software was used to develop ANNs and according to the results of Qwiknet the prediction performances for training and testing data sets were 75%, and 83.33% respectively.;In this dissertation, Chapter 1 describes the worldwide plastic optical fiber (POF) and fiber optic sensor markets, and the existing textile structures used in fiber optic sensing design particularly for the applications of biomedical and structural health monitoring (SHM). Chapter 2 provides a literature review in detail on polymer optical fibers, fiber optic sensors, and occupancy sensing in the passenger seats of automobiles. Chapter 3 includes the research objectives. Chapter 4 presents the response of POF to tensile loading, bending, and cyclic tensile loading with discussion parts. Chapter 5 includes an e-mail based survey to prioritize customer needs in a Quality Function Deployment (QFD) format utilizing Analytic Hierarchy Process (AHP) and survey results. Chapter 6 describes the POF sensor design and the behavior of it under pressure. Chapter 7 provides a data analysis based on the experimental results of Chapter 6. Chapter 8 presents the summary of this study and recommendations for future work. |
