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The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
Composite-Druckbehälter werden für Speicherung und Transport von Gasen unter hohem Druck verwendet. Durch die gewichtssparende Struktur, die aus einem dünnwandigem Metallgefäß und Faserverbundwerkstoff-Ummantelung besteht, sind solche Behälter insbesondere für die Automobilindustrie interessant, z.B. als Wasserstoffspeicher.
Die Druckprüfung ist ein konventioneller Test, um die Integrität von Metalldruckbehältern zu bewerten. Im Falle des Composite-Druckbehälters könne eine solche Prüfung jedoch den Faserverbundwerkstoff überbeanspruchen und somit die verbleibende Lebensdauer der getesteten Komponente verringern. Infolgedessen, es ist notwendig, die Verfahren zur zerstörungsfreie Prüfung und möglicherweise zur Zustandsüberwachung von Composite-Druckbehältern zu entwickeln. Unser Vorgehen verwendet geführte Ultraschallwellen und hat das Potenzial, kritische Schäden wie Risse im Metall, Faserbrüche und Matrixrisse in Faserverbundwerkstoff zu detektieren.
In diesem Beitrag wurde die Finite Elemente Methode benutzt, um die multimodale, geführte Wellenausbreitung in einer Metall-Faserverbundwerkstoffstruktur zu analysieren. Dadurch wurden die geeigneten Wellenmoden identifiziert und deren Wechselwirkung mit verschiedenen Fehlertypen analysiert. Diese Kenntnisse sollen für die Entwicklung von Verfahren zur wiederkehrenden Prüfung und zur Zustandsüberwachung von Composite-Druckbehältern angewendet werden.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.