Abstract
This work combines two state-of-the-art techniques in the area of magnetic nondestructive evaluation: the application of the superconducting quantum interference device (SQUID) as the magnetic field sensor; and the use of artificial neural networks as analysis tools applied to the detected magnetic signals. Pioneering measurements using the SQUID sensor have been made in steel samples containing various types of flaws, and a neural network system, based on the time-delay neural network and radial basis function algorithms, has been implemented to characterize the flaws. The neural network system aims to, based on the measured magnetic field, provide information about defect geometry, thus allowing the assessment of defect severity, as a basis for maintenance and repair procedures.
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