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GMR sensors are widely used in many industrial segments such as information technology, automotive, automation and production, and safety applications. Each area requires an adaption of the sensor arrangement in terms of size adaption and alignment with respect to the field source involved. This paper deals with an analysis of geometric sensor parameters and the arrangement of GMR sensors providing a design roadmap for non-destructive testing (NDT) applications. For this purpose we use an analytical model simulating the magnetic flux leakage (MFL) distribution of surface breaking defects and investigate the flux leakage signal as a function of various sensor parameters. Our calculations show both the influence of sensor length and height and that when detecting the magnetic flux leakage of µm sized defects a gradiometer base line of 250 µm leads to a signal strength loss of less than 10% in comparison with a magnetometer response. To validate the simulation results we finally performed measurements with a GMR magnetometer sensor on a test plate with artificial µm-range cracks. The differences between simulation and measurement are below 6%. We report on the routes for a GMR gradiometer design as a basis for the fabrication of NDT-adapted sensor arrays. The results are also helpful for the use of GMR in other application when it comes to measure positions, lengths, angles or electrical currents.
Statistical analysis of tomographic reconstruction algorithms by morphological image characteristics
(2010)
We suggest a procedure for quantitative quality control of tomographic reconstruction algorithms. Our task-oriented evaluation focuses on the correct reproduction of phase boundary length and has thus a clear implication for morphological image analysis of tomographic data. Indirectly the method monitors accurate reproduction of a variety of locally defined critical image features within tomograms such as interface positions and microstructures, debonding, cracks and pores. Tomographic errors of such local nature are neglected if only global integral characteristics such as mean squared deviation are considered for the evaluation of an algorithm. The significance of differences in reconstruction quality between algorithms is assessed using a sample of independent random scenes to be reconstructed. These are generated by a Boolean model and thus exhibit a substantial stochastic variability with respect to image morphology. It is demonstrated that phase boundaries in standard reconstructions by filtered backprojection exhibit substantial errors. In the setting of our simulations, these could be significantly reduced by the use of the innovative reconstruction algorithm DIRECTT.