Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263290
Title: Neural network image reconstruction for nondestructive testing
Author: Pardoe, Andrew Charles
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 1996
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.263290  DOI: Not available
Keywords: QA76 Electronic computers. Computer science. Computer software ; TA Engineering (General). Civil engineering (General) Pattern recognition systems Pattern perception Image processing Testing Laboratories
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