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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.
The use of composite materials has been steadily growing during the last decades, as well as the requirements on quality, mechanical properties and geometries of the parts. Some processes, like the 3D UV pultrusion process, manufacture parts that are long and thin, whilst having a varying curvature radius along the same part or even no curvature at all. Studying their mechanical properties along the main fiber direction, which is of foremost interest, is not an easy task nor efficient with most nondestructive methods. The use of air-coupled ultrasonics to evaluate the properties of composite materials has been widely proved by several authors, mainly using guided waves that provide information on the orthotropic properties of this kind of materials. Most of this work has focused on analyzing straight plate-like geometries, due to the simplicity to generate desired Lamb modes in the plate and analyze the behavior of guided waves inside the plate. In our contribution, the differences in the propagation of Lamb waves for straight and curved geometry glass fiber reinforced polymers (GFRP) have been analyzed. A GFRP test sample cured with UV light with one straight and one curved area has been evaluated. The responses of the generated Lamb wave modes for the straight and curved geometries have been compared, accounting for variations in the transducer characteristics, e.g. resonance behavior and focusing.