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97292 HIDDEN CORROSION DETECTION WITH GUIDED WAVES

Product Number: 51300-97292-SG
ISBN: 97292 1997 CP
Author: J.L. Rose, D. Jiao, S.P. Pelts, J.N. Barshinger, M.J. Quarry
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A corrosion detection feasibility study with ultrasonic guided waves is presented. Rather than use normal beam ultrasonic tectilques that move point by point over the structure being examined, guided waves are more global in nature allowing examination of regions over 2-3cm on up to 4-5m with reasonable ellciency in corrosion detection and in estimating average wall thickness between the two transducers. A sample problem in aging aircrafi inspection for fuselage thinning is studied and used to illustrate the guided wave inspection procedure and potential for classification and sizing analysis. Special sensitivities to certain kinds of defects are possible with guided waves as a result of wave structure changes across the thickness of the structure by simple mode and frequency changes. A variable angle beam probe and multiple array comb type transducer are considered for flexibility in mode selection and isolation on the dispersion diagrams in order to achieve the special wave structures required for corrosion detection and also for reasonable penetration power. Going beyond the initial feasibility study of guided wave application for the detection of corrosion, a number of tools are developed that can be used in future work to more accurately detect, size, and classic corrosion types in vaious structures. Boundary element modeling analysis, for example, allows the calculation of reflection and transmission factors for a particular mode and frequency input to a particular pitting or corrosion type defect. TMs can be followed by detailed feature analysis for subsequent use in neural net algorithm development for corrosion analysis. The physically based features from guided wave analysis can than be used to provide insight and guidance into the algorithm development program using pattern recognition and neural nets. Keywords: Guided Waves, Corrosion Detection, Non-destructive Evaluating, Ultmsonics, Boundary Element Methods, Neural Nets
A corrosion detection feasibility study with ultrasonic guided waves is presented. Rather than use normal beam ultrasonic tectilques that move point by point over the structure being examined, guided waves are more global in nature allowing examination of regions over 2-3cm on up to 4-5m with reasonable ellciency in corrosion detection and in estimating average wall thickness between the two transducers. A sample problem in aging aircrafi inspection for fuselage thinning is studied and used to illustrate the guided wave inspection procedure and potential for classification and sizing analysis. Special sensitivities to certain kinds of defects are possible with guided waves as a result of wave structure changes across the thickness of the structure by simple mode and frequency changes. A variable angle beam probe and multiple array comb type transducer are considered for flexibility in mode selection and isolation on the dispersion diagrams in order to achieve the special wave structures required for corrosion detection and also for reasonable penetration power. Going beyond the initial feasibility study of guided wave application for the detection of corrosion, a number of tools are developed that can be used in future work to more accurately detect, size, and classic corrosion types in vaious structures. Boundary element modeling analysis, for example, allows the calculation of reflection and transmission factors for a particular mode and frequency input to a particular pitting or corrosion type defect. TMs can be followed by detailed feature analysis for subsequent use in neural net algorithm development for corrosion analysis. The physically based features from guided wave analysis can than be used to provide insight and guidance into the algorithm development program using pattern recognition and neural nets. Keywords: Guided Waves, Corrosion Detection, Non-destructive Evaluating, Ultmsonics, Boundary Element Methods, Neural Nets
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