This thesis is focused on non-contact air-coupled ultrasonic Non-Destructive Evaluation (NDE) of Carbon-Carbon (C/C) disc brake materials. The minimum detectable defect size in the C/C composite disc brakes up to the maximum thickness of 1 7/16" (36.33 mm) using 120, 225 and 400 kHz transducers was investigated in the experimental section of this thesis. The effect of scanning increment step size on resolution of the final C-scan image was also investigated. The results indicated that the 12.7 mm diameter flat bottom drilled holes were reliably detectable using 225 kHz transducers. The flat bottom drilled holes and the side drilled holes of 6.35 mm in diameter were detectable on the final C-scan images of 225 kHz testing mainly due to the known locations of the defects. Results showed that testing frequency of 120 kHz provides very transparent C-scans. Testing frequency of 225 kHz provides also good transparency and better resolution. Testing frequency of 400 kHz did not provide satisfactory results. No advanced signal filtering techniques were utilized during the experiments. The relationship between the speed of sound in C/C material and the carbon fiber orientation in the carbon matrix needs to be investigated. The air-coupled ultrasonic testing of the C/C composite disc brake samples was conducted at the Center for Non-Destructive Evaluation at Iowa State University.

Circular air-coupled ultrasonic testing (ACUT) setup for the inspection of commercial carbon-carbon composite aircraft brake disks was developed in Intelligent Measurement and Evaluation Laboratory (IMEL) at Southern Illinois University Carbondale (SIUC). The developed test setup utilizes Airstar single channel air-coupled equipment and has only manual A-scan and B-scan capability. The developed ACUT technique is unique compared to the commercial C-scan ultrasonic systems and is proficient, fast, economically feasible, and easy to implement method particularly for the inspection of carbon-carbon (C/C) composites aircraft brake disks. Prior to conducting air-coupled measurements, wobble analysis was carried out. This was important because significant wobbling in the test setup can lead to the interference of the reflected and the incident beam which would result to inaccurate ultrasonic measurements. The measured deviation due to wobbling, surface profile of the disk, design, and experimental error were relatively small. Therefore, these errors were neglected while performing ACUT measurements. For ACUT measurements, several through-transmitted amplitude signals were recorded within the C/C brake disks manually. The images were then reconstructed using Matlab based on the through-transmitted amplitude signals. Finally, a comparison was drawn between the reconstructed images and the C-scan images of the C/C brake disks obtained from the commercial Airstar C-scan ACUT system. Like commercial C-scan ACUT image results, reconstructed images were also able to detect all defects in the commercial C/C brake disks which served for the system verification and validation. In addition, defect, non-defect, and suspected areas within the C/C brake disks were quantified with air-coupled measurement. For this, light microscopy was conducted for every sample made from each C/C brake disks at lower magnification of 10X. It was concluded that it is very difficult to assess the crack or delamination situation based on a 2D micrograph of one depth. Also, it was concluded that an internal porosity and micro-cracks may not be only factors that can be related to defects. Finally, an intelligent systems approach, specifically, fuzzy logic and artificial neural network (ANN) methodologies were implemented for the automatic defect detection in commercial C/C aircraft brake disks by using air-coupled ultrasonic results. For this, a multi-layer perceptron (MLP) with two hidden layers and a scaled conjugate gradient back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode using the ANN toolbox in Matlab. The network training was repeated until a steady state was reached, where there was no further change in the synaptic weights. The ANN provided plausible results in detecting the defect areas for different C/C brake disks. It was also demonstrated that the system was able to learn the rules without knowing any algorithm for automatic defect detection.