INTELLIGENT NON-DESTRUCTIVE EVALUATION EXPERT SYSTEM FOR CARBON-CARBON COMPOSITES USING THERMOGRAPHY, ULTRASONICS, AND COMPUTED TOMOGRAPHY

Author

Yicheng Pan, Southern Illinois University CarbondaleFollow

Date of Award

5-1-2010

Degree Name

Doctor of Philosophy

Department

Engineering Science

First Advisor

Chu, Tsuchin

Abstract

This study develops a reliable intelligent non-destructive evaluation (NDE) expert system for carbon-carbon (C/C) composites based on thermography, ultrasonic, computed tomography and post processing by means of fuzzy expert system technique. Data features and NDE expert knowledge are seamlessly combined in the intelligent system to provide the best possible diagnosis of the potential defects and problems. As a result, this research help ensure C/C composites' integrity and reliability. Four types of orthotropic aerospace composite material groups, which include 2-D pitched based commercial aircraft disc brakes and asmolds, 3-D PAN based C/C composites, and carbon fiber reinforced plastic (CFRP) panels, were tested. Based on the performance testing results of thermography, air-coupled ultrasonic, and x-ray computed tomography, the testing data pattern corresponding to feature and quantification of defects were found. This NDE knowledge databases were transformed to fuzzy logic expert system models. The models succeefully classified and indicated the defect's size and distribution and the intelligent systems perform NDE better than human operators. These fuzzy expert systems not only eliminate human errors in defect detection but also function as NDE experts. In addition, fuzzy expert systems improve the defect detection by incorporating fuzzy expert rules to remove noises and to measure defect size more accurately. In the future, the expert system model could be continuously updated and modified to quantify the size and distribution of defects. The systems developed here can be adapted and applied to build an intelligent NDE expert system for better quality control as well as automatic defect and porosity detection in C/C composite production process.