The research work focuses on implementing intelligent measurement and diagnostic techniques for the non-destructive evaluation (NDE) of aircraft carbon composites. The outcome of this research work developed reliable and faster techniques to aid in the rapid assessment of defects in anisotropic carbon composites by applying ultrasonic and infrared thermography NDE methods. To fulfill the requirement of the intelligent non-destructive evaluation methods, this research is divided into four sub-researches: fuzzy logic based delamination detection, super-resolution image reconstruction for ultrasonic C-scan, ultrasonic 3D reconstruction, and polynomial fitting techniques for infrared thermography inspection. These researches focus on the improvement and optimization of current ultrasonic testing and infrared thermography inspection. They are independent but interrelated component, and they all serve the same goal which is to interpret data correctly and provide detailed information about the region of interests (ROI) for intelligent non-destructive measurement and evaluation. Details of these researches are presented in Chapter 2, 3, 4, and 5 respectively. For the ultrasonic testing, a fuzzy inference classifier will be used to generate the rule base and knowledge base for different kinds of defects in composites. It will automatically manage large amounts of signal data sets and extract the important information. Data features and NDE expert knowledge are seamlessly combined to provide the best possible diagnosis of the potential defects and problems. As a result, the outcome of this research work will help ensure the integrity and reliability of carbon composites. The C-scan image resolution of ultrasonic testing system was improved by applying super-resolution algorithms to overcome the inherent resolution limitations of the existing ultrasonic system. It greatly improves the image quality and allows for more detailed inspection of the ROI with high resolution, making defect evaluation easier and more accurate. The ultrasonic 3D reconstruction technique will be able to provide NDE inspectors with more detailed information on defect depth, volume, and 3D structure, as well as help them make quick, accurate, and reliable decisions. For the IR inspection, the thermography methods based on the thermal contrast are strongly affected by non-uniform heating which due to the heat source alignment and specimen thickness variation. The proposed polynomial curve fitting and surface fitting techniques were applied to eliminate the non-uniform heating effect by subtracting the estimated non-uniform heating pattern from the corrupted IR images. Mainly, aircraft composite material: carbon fiber reinforced polymer (CFRP) panels will be considered for this research work. Based on the preliminary study, delamination defects due to impact damage and foreign object inclusions artificially embedded in CFRP panels were successfully detected by immersion ultrasonic testing (UT) and IRT inspection. Therefore, the next step will be in improving the detection algorithm and developing an intelligent quality inspection technique for NDE testing. Powered with multiple image processing techniques and mathematical algorithms, the research result will provide high resolution images and detailed information about defect areas. In addition, it will also capable of identifying the type, shape, size, and the distribution of defect.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-1767 |
| Date | 01 December 2013 |
| Creators | Li, Shanglei |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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