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- Non-destructive testing (5) (entfernen)
The capabilities of non-destructive testing (NDT) methods for defect detection in civil engineering are characterized by their different penetration depth, resolution and sensitivity to material properties. Therefore, in many cases multi-sensor NDT has to be performed, producing large data sets that require an efficient data evaluation framework. In this work an image fusion methodology is proposed based on unsupervised clustering methods. Their performance is evaluated on ground penetrating radar and infrared thermography data from laboratory concrete specimens with different simulated near-surface defects. It is shown that clustering could effectively partition the data for further feature level-based data fusion by improving the detectability of defects simulating delamination, voids and localized water. A comparison with supervised symbol level fusion shows that clustering-based fusion outperforms this, especially in situations with very limited knowledge about the material properties and depths of the defects. Additionally, clustering is successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system.
Trans-European corridors for transport of people and goods require strengthening of bridges to carry increased axle loads in terms of absolute axle loads and of their frequency. Most of the bridges along highways are reinforced or prestressed concrete bridges.
Since the late 1990s, CFRP laminates have been applied to box girder and T-beam bridges to increase the overall capacity. Although regular inspections are carried out, no special method was available to test the bond condition of CFRP laminates after being in Service for more than ten years. The German Center of Competence in Civil Engineering is aware of this need and funded a research project to investigate the bond condition by means of advanced non-destructive testing (NDT).
We present a prototype for automated magnetic stray field testing of ferromagnetic roller bearings. For this purpose NDE-adapted GMR sensor arrays (giant magneto resistance) are used for the detection of surface breaking cracks. The sensors are miniaturized down to the lower µm-regime to achieve adequate spatial resolution. In doing so, sensor arrays with up to 48 elements are used to inspect the bearing surface within a few seconds only. In contrast to magnetic particle inspection (MPI), where the global magnetization requires a further inspection step and succeeding demagnetization, the presented prototype only locally magnetize the surface area in the vicinity of the GMR Sensors. For the local magnetization, the applied sub-surface magnetic field was simulated and proofed for detecting flaws with a depth of a few 10 µm. By multiplexing the sensor array with an adapted read out electronics we quasi simultaneously detect the normal field component of about 100µm above the surface. The detection of artificial notches with a depth of 40 µm and more could be resolved with a SNR better than 20 dB. The presented testing facility is fast and provides a step towards automated testing of safety relevant steel components.
Size adapted GMR arrays for the automated inspection of surface breaking cracks in roller bearings
(2014)
Their small size together with a remarkable field sensitivity are the most prominent features of present-day GMR sensors paving the way for various applications in automated non-destructive testing (NDT). This work presents a prototype for fast and automated magnetic testing of roller bearings. A local magnetization unit excites the magnetic field inside the bearing. As a result of a design study and the following wafer fabrication the probe was equipped with NDT-adapted GMR sensor arrays in which 48 elements measures the field response. The detection of artificial and 40 µm deep defects could be resolved with a SNR better than 20 dB. In addition, we report of first results of a POD (Probability of Detection) analysis using GMR sensors to investigate bearings with EDM (electronic discharge machining) notches having depths down to 10 µm. Finally, we estimate successfully the depth of a 57 µm notch from the measured data.
The multiaxial fatigue damage behaviour of short fibre reinforced polyamide 6 is analysed on injection moulded tube samples. In parallel with the fatigue tests, the damage state is evaluated
nondestructively by X-ray refraction analysis which detects inner surfaces by the variation of electron density. By applying X-ray refraction analysis and a model based on by GÜNZEL the micro damage evolution can be separated into fibre matrix debonding and matrix-Micro cracking.