
Publica
Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Non-destructive testing with micro- and Mm-waves-where we are-where we go
| European Federation for Non-Destructive Testing -EFNDT-: 10th European Conference on Non-Destructive Testing, ECNDT 2010. CD-ROM : 7.-11.06.2010, Moskau Moskau, 2010 Vortrag 2.16 |
| European Conference on Non-Destructive Testing (ECNDT) <10, 2010, Moskau> |
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| English |
| Conference Paper |
| Fraunhofer IZFP () |
| SAFT; electromagnetic wave; wave; ECNDT |
Abstract
Electromagnetic waves covering the wavelength range from decimetre down to millimetre (in vacuum, frequencies from 300 MHz up to 300 GHz) are called micro- and mm-waves. As these waves can penetrate non-electrically-conductive materials in NDT they are especially suitable for testing of paper, wood, ceramics, concrete, polymeric materials and fibre composites too, mainly when the fibres involved are non-conductive. Insofar humidity plays not an important role the wave can penetrate also clothing to a certain amount which makes the waves in the last years also popular for detecting of concealed weapons or explosives in security applications. The contribution describes the basic physical interaction of these waves with objects for their detection and characterization based on phenomena like reflection and scattering similar to the propagation of ultrasound in solid state materials. Practical examples are given to profiling of humidity in porous material and to monitor injectionmoulding of polymers. Special emphasis is laid on the imaging of concealed objects under clothing and the problem to quickly but reliably detect weapons and explosives by scanning persons and using imaging algorithms like SAFT (synthetic aperture focusing technique) and pattern recognition procedures. Especially the characterization of the explosive materials ask for a multi-sensor concept, based not alone on the micro and mm wave interaction but also on an infrared spectroscopic application where all of the data are combined by data fusion algorithms in one image for evaluation.