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Automated damage detection in carbon fibre and fibre metal laminates is still a challenge. Impact damage is typically not visible from the outside. Different measuring and analysis methods are available to detect hidden damage, e.g., delamination or cracks. Examples are X-ray computer tomography and methods based on guided ultrasonic waves (GUW). All measuring techniques are characterised by high-dimensional sensor data—in the case of GUW, a set of time-resolved signals as a response to an ac...Automated damage detection in carbon fibre and fibre metal laminates is still a challenge. Impact damage is typically not visible from the outside. Different measuring and analysis methods are available to detect hidden damage, e.g., delamination or cracks. Examples are X-ray computer tomography and methods based on guided ultrasonic waves (GUW). All measuring techniques are characterised by high-dimensional sensor data—in the case of GUW, a set of time-resolved signals as a response to an actuated stimulus. We present a simple but powerful two-level method that reduces the input data (time-resolved sensor signals) significantly through a signal feature selection computation, which can then be applied to a damage predictor function. Besides multi-path sensing and analysis, the novelty of this work is a feed-forward ANN with low complexity which is used to implement the predictor function: it combines a classifier and a spatial regression model.» weiterlesen» einklappen

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