Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.

Automated summary

This study investigates the feasibility of using vibration mode shapes to identify material degradation in composite structures. A deep learning approach is applied to determine the size and location of the degradation zone based on previously identified vibration mode shapes, yielding high accuracy in the identification process.