A computerized self-compensating system for ultrasonic inspection of airplane structures
- Northwestern Univ., Evanston, IL (United States). Center for Quality Engineering and Failure Prevention
- McDonnell Douglas, Long Beach, CA (United States)
Application of a self-compensating technique for ultrasonic inspection of airplane structures makes it possible not only to detect cracks in the different layers of joints but also to obtain information on crack sizes. A prototype computerized ultrasonic system, which utilizes the self-compensating method, has been developed for non-destructive inspection of multilayered airplane structures with in-between sealants, such as bolted joints in tail connections. Industrial applications of the system would require deployment of commercially available portable modules for data acquisition and processing. A portable ultrasonic flaw detector EPOCH II manual scanners and HandiScan, and SQL and FCS software modules form the PC-based TestPro system have been selected for initial tests. A pair of contact angle-beam transducers were used to generate shear waves in the material. Both hardware and software components of the system have been modified for the application in conjunction with the self-compensating technique. The system has bene tested on two calibration specimens with artificial flaws of different sizes in internal layers of multilayered structures. Ultrasonic signals transmitted through and reflected from the artificial flaws have bene discriminated and characterized using multiple time domain amplitude gates. Then the ratios of the reflection and transmission coefficients, R/T, were calculated for several positions of the transducers. Inspection of measured R/T curves shows it is difficult to visually associate curve shapes with corresponding flaw sizes and orientation. Hence for online classification of these curve shapes, application of an adaptive signal classifier was considered. Several different types and configurations of the classifiers, including a neural network, have been tested. Test results showed that improved performance of the classifier can be achieved by combination of a back-propagation neural network with a signal pre-processing module.
- DOE Contract Number:
- W-7405-ENG-82
- OSTI ID:
- 64653
- Report Number(s):
- CONF-931193-; ISBN 0-931403-23-5; TRN: IM9528%%313
- Resource Relation:
- Conference: American Society for Nondestructive Testing (ASNT) fall conference and quality testing show: NDT - a partner in engineering innovation, Long Beach, CA (United States), 8-12 Nov 1993; Other Information: PBD: 1993; Related Information: Is Part Of ASNT 1993 fall conference and quality testing show. NDT: A partner in engineering innovation; PB: 193 p.
- Country of Publication:
- United States
- Language:
- English
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