Throughout the world, engineers and technicians are working together finding cracks and other anomalies to prevent expensive failures. The increasingly important role of nondestructive testing in public security and safely technology development promote new and innovative methods to be developed and already known methods do be enhanced. Eddy Current Testing (ECT) is one of the most established nondestructive structure evaluation technique that enables defect detection and material condition monitoring with high sensitivity and reliability. Within this method the volume distribution of the material conductivity is determined by inducing eddy currents inside the conductor under test and measuring the magnetic field produced by those currents [6]. ECT is used with electrically conductive materials for the measurement of the thickness of metallic plates [7] or non-metallic coatings or metal substrates, estimation of electrical conductivity or magnetic permeability distribution, corrosion detection [8] and the determination of surface or subsurface defects [9]. However, regarding safety and economic efficiency assessment, defects are still the main concern and with the improvements on understanding the facture mechanics, the detailed geometric characterization of the cracks, still an open problem, became the target of the research in the area. One disadvantage of this type of method is related to the inevitability of well qualified and experienced technicians to perform the tests since, in spite of its implementation simplicity, data produced by eddy current based equipment are among the most complicated to interpret.
These factors together motivate the project objectives: to implement new and better measurements with both novel instrumentation and embedded artificial intelligence to automate the interpretation of the various imaging data streams in order to determine surface and subsurface defects shape and size [10].
With eddy currents nondestructive evaluation, the dimensions of defects are retrieved by inversion of the measured signals. Since the physical model of the problem is often complicated and non-linear, the inversion model is ill-posed and it’s solution always a challenge. Within this project an original strategy based on the current lines distribution is proposed. The idea is to assess the current density perturbation caused by the defects through a bi-dimensional map of the magnetic field components on the surface of the material to be tested, obtained experimentally. This current density can be represented as a composition of magnetic dipoles. The field of the single magnetic field dipole will be the kernel of the transformation to be used in the inversion process. Measurements are taken using electromagnetic sensors based on giant magnetoresistive (GMR) effect. GMRs extend the application field of eddy current non destructive evaluation methods since they measure the magnetic field directly and their sensitivity is equal for all working frequencies, allowing better performance than conventional probes for low-frequency applications as, for instance, when detecting defects deep buried in multilayered structures. Besides, these sensors are far more sensitive than those used in current commercial equipment [11] and the output signal signal-to-noise ratio is high enough to apply regularization methods to extract information.
It is our goal to develop a competitive automated system to be subsequently exploited and used in the industry.
This project besides its technical and scientific objectives is also focused on promoting nondestructive evaluation issues to young researchers. It is a multidisciplinary field of study that embraces issues like: (1) technology development; (2) image processing and recognition; (3) modeling and algorithms. In Portugal there are very few competencies within the nondestructive testing knowledge area in spite of the abundance of industrial relevance for them and as a result Portugal is losing competitiveness in a 600 million global market.
Centro de Investigação | Grupo de Investigação | Papel no Projeto | Data de Início | Data de Fim |
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IT-Iscte | -- | Parceiro | 2011-06-01 | 2013-06-01 |
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Nome | Afiliação | Papel no Projeto | Data de Início | Data de Fim |
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Octavian Adrian Postolache | Professor Catedrático (DCTI); Investigador Integrado (IT-Iscte); | Coordenador Local | 2011-06-01 | 2013-06-01 |
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