Graphite Epoxy Defect Classification of Ultrasonic Signatures Using Statistical and Neural Network Techniques

Brownt, L. M.; Newman, R. W.; DeNale, R.; Lebowitz, Carol A.; Arcella, F. G.

doi:10.1007/978-1-4615-3344-3_87

Cited by 3 publications

(2 citation statements)

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“…In fact, different kinds of defects can cause similar trends in ultrasonic signals, with a consequent ill-posedness of the pattern classification problem (please, see [6] for example of UT signals and for details). Various solutions are known in scientific literature to solve this kind of inverse pattern recognition matter [7], [8], [9], [10]. Within this framework, an essential approach is due to advances of computational intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Cacciola

Calcagno

Megali

et al. 2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

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Despite their high specific stiffness and strength, carbon fiber reinforced polymers, stacked at different fiber orientations, are susceptible to interlaminar damages. They may occur in the form of micro-cracks and voids, and leads to a loss of performance. Within this framework, ultrasonic tests can be exploited in order to detect and classify the kind of defect. The main object of this work is to develop the evolution of a previous heuristic approach, based on the use of Support Vector Machines, proposed in order to recognize and classify the defect starting from the measured ultrasonic echoes. In this context, a real-time approach could be exploited to solve real industrial problems with enough accuracy and realistic computational efforts. Particularly, we discuss the cross wavelet transform and wavelet coherence for examining relationships in time-frequency domains between. For our aim, a software package has been developed, allowing users to perform the cross wavelet transform, the wavelet coherence and the Fuzzy Inference System. Since the ill-posedness of the inverse problem, Fuzzy Inference has been used to regularize the system, implementing a data-independent classifier. Obtained results assure good performances of the implemented classifier, with very interesting applications.

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Section: Introductionmentioning

confidence: 99%

Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Cacciola

Calcagno

Megali

et al. 2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

View full text Add to dashboard Cite

show abstract

“…For example, Japan and the United States have employed artificial neural networks to characterize discontinuities in weld metal based on information in the ultrasonic signatures [2]. Similarly, ultrasonic signatures of discontinuities in thick graphite epoxy composites have been characterized using artificial neural networks [3]. Parameterization of eddy current signatures using the Fourier descriptor method has led to classification of defects in Inconel® tubing [4].…”

Section: Introductionmentioning

confidence: 99%