Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials

Leonard Ziemiański¹  Bartosz Miller¹ 

[1] Faculty of Civil and Environmental Engineering and Architecture, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland;

关键词: shell;    layered composites;    mode shapes;    identification;    machine learning;   

DOI  :  10.3390/ma14112801

来源: DOAJ

【 摘 要 】

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

【 授权许可】

Unknown