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Neural network image reconstruction for nondestructive testing
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Pardoe, Andrew Charles (1996) Neural network image reconstruction for nondestructive testing. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b1352684~S1
Abstract
Conventional image reconstruction of advanced composite materials using ultrasound tomography is computationally expensive, slow and unreliable. A neural network system is proposed which would permit the inspection of large composite structures, increasingly important for the aerospace industry. It uses a tomographic arrangement, whereby a number of ultrasonic transducers are positioned along the edges of a square, referred to as the sensor array. Two configurations of the sensor array are utilized. The first contains 16 transducers, 4 of which act as receivers of ultrasound, and the second contains 40 transducers, 8 of which act as receivers. The sensor array has required the development of instrumentation to generate and receive ultrasonic signals, multiplex the transmitting transducers and to store the numerous waveforms generated for each tomographic scan. The first implementation of the instrumentation required manual operation, however, to increase the amount of data available, the second implementation was automated.
| Item Type: | Thesis (PhD) | ||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Library of Congress Subject Headings (LCSH): | Nondestructive testing, Composite materials -- Testing, Ultrasonic imaging, Neural networks (Computer science) | ||||
| Official Date: | November 1996 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | School of Engineering | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Hutchins, D. A. | ||||
| Extent: | xix, 246 p. | ||||
| Language: | eng |
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