Abstract
Detection of hidden damages in Fibre Metal Laminates (FML) is a challenge. Damage detection, classification, and localization is a part of the lower levels of Structural Health Monitoring (SHM) and is critical for damage diagnosis. SHM is an extremely useful tool for ensuring integrity and safety, detecting the evolution. Early damage detection and understanding of damage creation can avoid situations that can be catastrophic. X-ray tomography is a powerful tool for research as well as damage diagnostics. But high-resolution tomography results in high measuring and computational times up to 10 h for one specimen. The paper presents an early method of accessing the sections of the FML for identifying internal damages using X-ray imaging by optimized and adaptive zooming and scanning using automatic Region-of-Interest extraction with Machine Learning methods. The generated knowledge and the image data collected would further accelerate the development in the field of autonomous SHM of the composite and hybrid structures like fibre metal laminates which would further reduce the safety risks and total time associated with structural integrity assessment. A comprehensive image-based data set is collected by means of X-ray CT images containing micro-scale damage mechanisms (fibre breakage, metal cracks etc.) and macro-scale damages like delaminations. Starting point is an image sets were measured with two different X-ray CT devices with a static parameter set (set in advance and a-priori) and posing many limitations and issues that make damage diagnostics difficult. The adaptive and iterative measuring process should increase the quality of the images and decrease the measuring time significantly.
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Shah, C., Bosse, S., Zinn, C., von Hehl, A. (2023). Optimization of Non-destructive Damage Detection of Hidden Damages in Fiber Metal Laminates Using X-ray Tomography and Machine Learning Algorithms. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_37
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